FYEE 2026: ABSTRACTS
FYEE 2026: ABSTRACTS
The following abstracts are ordered chronologically and by session type.
To navigate to an abstract of interest, first select the session during which the abstract is scheduled. From there, you can navigate to individual abstracts.
A full length Table of Contents, which includes navigation for all abstracts presented during the FYEE 2026 Conference, is available at the end of this document.
WORKSHOP IV, PANEL II & III 72
AI PROMPTED POST-CONFERENCE REFLECTION 76
SUNDAY, AUGUST 2ND | 2:00 - 3:30 PM
Workshop: Cloud CAD Enabling Collaboration and Academic Insights (Sponsored) 3
Workshop: All Students Belong: Recognizing and Supporting Neurodiversity in the Classroom 5
Domenico DiMare, OnShape
Being cloud-native, Onshape is already the most modern and versatile professional CAD and product development platform on the market. It is also uniquely situated to grow and adapt as new technologies and design methodologies arise. In this hands-on workshop, we will demonstrate some of the capabilities that come with cloud-native CAD software.
Kevin Kaufman-Ortiz
Populations identifying closely with national identity, geographic location, and indigeneity face severe erasure in the United States. Specifically, migration is an inherent part of the experience of becoming an engineer from the U.S. territories. Yet, research on this topic and the barriers associated with this movement is scarce. Often, engineering education researchers think within the boundaries of what is mutually understood as important to United States scholars. Many scholars use historically and currently relevant categorizations of people who face unique barriers to pursuing an engineering education in the United States, such as race,
ethnicity, gender, and international status. Although these categorizations are important to investigate, there are other ways to categorize people that can shed light on new barriers and further access to an engineering education. In this community conversation session, participants will be introduced to migration scholarship and think critically about how migration studies can and should be its own field of study in engineering education research.
Home and host territories, universities, and workplaces can learn a great deal from engineering migrants; insights to improve processes and provide resources for lesser-known populations in the United States. Participants will learn about migration perspectives, theories, and current research in engineering education, and find new ways to introduce this scholarship in their own work. Participants will understand why transfer students and engineering professionals constantly consider moving to become engineers. Participants at varying stages of their careers will leave the session with new ideas for incorporating migration scholarship into their research
agendas, ultimately improving systems for a broad range of populations in the field.Home and host territories, universities, and workplaces can learn a great deal from engineering migrants' insights to improve processes and provide resources for lesser-known populations in the United States. Participants will learn about migration perspectives, theories, and current research in engineering education, and find new ways to introduce this scholarship in their own work. Participants will understand why transfer students and engineering professionals constantly consider moving to become engineers. Participants at varying stages of their careers will leave the session with new ideas for incorporating migration scholarship into their research agendas, ultimately improving systems for a broad range of populations in the field.
Drs. Lynn Albers
Margaret Hunter
Elisabeth Ploran
The increase of neurodivergent students in STEM is driving the need for faculty to develop new skills necessary to help neurodivergent students acclimate and grow in a first-year environment with the intent to create a seamless transition from high school to college. These students have unique challenges in college such as difficulty focusing during long class periods, adequate notetaking and record-keeping, working both independently and in groups, and starting in-class assignments without guidance. This can be exacerbated by the transition from high school to college in which they must now rely on themselves to self-regulate in the classroom and manage their own time.
The facilitators all engage with first-year students and find it challenging to address the struggles of neurodiverse students (both diagnosed and undiagnosed) in courses. The lack of training for higher education faculty in working with neurodiverse students during lectures and labs causes additional stress and misunderstanding for both the faculty and the students. This impacts females more than males as up to 80% of autistic females are not identified by age 18.
The intent of this workshop is to identify resources within academia and explore external resources to support faculty and students so that participants will gain a better understanding of how to identify neurodiversity and reduce barriers to learning. By sharing classroom experiences and exploring best practices within courses through roundtable discussion and rich drawings, together we can reduce the impact of these challenges and make the classroom a supportive environment and community of belonging for all students.
SUNDAY, AUGUST 2ND | 3:45 - 5:15 PM
Andrea Blais (MEd)
Katie Atkins (MBA)
As AI becomes more embedded in engineering education, first-year programs are
facing a more urgent question: how do we help students develop the human competencies that
matter when answers are easy to generate, but judgment remains hard?
This session shares lessons learned from embedding an open-response Situational Judgment
Test into first-year engineering programs. The assessment measures key ABET student
outcomes, including teamwork, communication, ethical reasoning, and professional judgment,
while also providing students with personalized feedback and learning resources.
Drawing on pilot experiences with engineering programs over the past year, this presentation
will highlight practical lessons related to implementation, student engagement, timing, retesting
cadence, and early patterns of student growth. We will also discuss how formative assessments
can support student development while providing programs with useful evidence for curricular
improvement and alignment with ABET-related competency frameworks.
Attendees will leave with practical ideas for embedding professional skills development into the
first-year engineering experience in ways that are both meaningful for students and feasible for
faculty.
Sarah Mack
Jennifer Fazio
Dr. Michelle Blum
Syracuse University, College of Engineering and Computer Science
This workshop introduces participants to a holistic, team-based academic advising model developed and
implemented at Syracuse University’s College of Engineering and Computer Science (ECS) since Fall
2018. The model assigns every undergraduate student a three-person advising team, a Faculty Advisor, a
Success Advisor, and a Career Advisor. The team provides coordinated support across the academic,
personal, and professional dimensions of the student experience. Grounded in NACADA and NACE
professional standards and informed by institutional retention data, the model was designed to address
systemic gaps in a prior advising structure that left students without consistent support after their first year.
Since implementation, first-year retention rates have increased by an average of 4.1 percentage points,
second-year retention has improved comparably, and participation in the College’s Academic Recovery
Program among students on probation has risen from 57.8% to 95.7% over five years. The overall probation
rate has declined from approximately 11% to under 8%.
In this 90-minute interactive workshop, participants will examine the model’s design principles and
operational structure, analyze outcome data, and engage in facilitated activities that guide them through a
structured diagnostic of their own institution’s advising gaps. Participants will leave with a concrete action
plan for adapting key elements of the team-based model to their specific context, along with planning
templates and reference materials. The workshop is designed for faculty, academic advisors, student affairs professionals, and administrators involved in first-year engineering programs who are seeking evidence-
based strategies to improve retention and student success.
Matthew James
Cassie Wallwey
Several studies have shown that providing choice to students can have positive benefits such as increased motivation [1], [2], [3]. This can be especially true for students from underrepresented groups in engineering; providing a chance for students who enter their engineering studies to harness a wide range of prior knowledge and lived experiences [4]. However, incorporating student choice comes with its own challenges: many instructors
are understandably hesitant to take on the additional workload of managing student teams who are all working on different topics, especially for teams who are exploring subject areas outside of their expertise [5]. Furthermore, when not implemented appropriately, open-ended projects can cause additional stress for students and faculty members that can negate learning benefits [3], [6].
This interactive workshop seeks to break down barriers for instructors who want to incorporate more student choice into their first-year engineering courses by covering best practices and lessons-learned from implementing open-ended projects at scale in a first-year engineering program. For several years, the facilitators have successfully
implemented projects with various levels of student choice in their own classes at a scale of several hundred students per instructor. Their courses are part of a general first-year engineering program and cover a range of learning outcomes including basic programming, CAD, teamwork, communication, self-regulatory skills, ethics, and engineering design.
Building on our experiences and current literature, the workshop will facilitate an open
conversation around the key elements, tools, activities, and assessments that help instructors successfully facilitate open-ended activities, assessments, and/or projects into their courses. Participants will explore practical aspects such as establishing effective guardrails that facilitate student agency without sacrificing course outcomes or instructor
workload, administrative tools to manage various project themes, and assessment strategies. The session will also address the advantages and potential drawbacks of open-endedness in first-year engineering programs, encouraging attendees to reflect on how they can use these approaches in a way that aligns with the learning outcomes of their own courses.
MONDAY, AUGUST 3RD | 9:45 - 10:30 AM
WIP: A Structured Transition: Supporting Engineering Students 11
WIP: Redesigning the First-Year Engineering Experience at Texas State University 13
WIP: Decision Timing and Career Confidence Among First-Year Engineering Students 17
WIP: Engineering Education Meets GenAI: Rethinking First-Year Design for an AI-Enabled Future 18
WIP: Using Generative AI to Support First-Year Engineering Students in Developing Measurable Design Criteria and Test Methods 23
Miosotis Hernandez
Samuel Lieber
Jaskirat Sodhi
Many first-year engineering students enter college with strong motivation and clear aspirations, yet a significant number require additional support in foundational math and science to succeed in a four-year engineering curriculum. Others discover that their current engineering major is not working out as expected or face early academic challenges that place them at risk of probation, pre-suspension, or a change of major. To address these transitional needs, the Newark College of Engineering (NCE) at the New Jersey Institute of Technology (NJIT) established the General Engineering (GEN) program, a structured, student-centered pathway designed to strengthen academic readiness, provide targeted support, and guide students toward successful entry into degree-granting engineering programs. The GEN program functions as a transitional home for students who need reinforced preparation in math and science or who are navigating the discovery of engineering major options. Through personalized advising, intentional course sequencing, and structured academic recovery plans, students receive the support necessary to rebuild confidence, improve performance, and make informed decisions about their academic trajectory. In addition to its transitional role, the program offers industry-focused concentrations that create a non-traditional, workforce-aligned pathway not found in other departments. Developed in collaboration with industry advisors, these concentrations address emerging workforce needs while maintaining a strong grounding in engineering principles, an option particularly valuable for students exploring alternative pathways or transitioning from other majors. Central to the program is the development of accessible advising tools and resources that promote informed decision-making, academic accountability, and sustained engagement. By cultivating a supportive and inclusive environment, the GEN program serves as an essential bridge, enabling students to regain momentum, strengthen foundational competencies, and transition successfully into engineering majors or related technical fields. Since its implementation, the program has demonstrated measurable impact and is demonstrating effectiveness in supporting student success and reinforcing the engineering talent pipeline. This work will highlight the GEN program’s advising structures, curricular design, and intervention strategies that have proven effective in supporting students’ transition into engineering majors.
Atheer Almasri, Todd R Hamrick, Robin A.M. Hensel, Susie Huggins, Carter Hulcher, WenJuan Mo, Akua Oppong-Anane, Gia Huy Pham, Lizzie Santiago
Many institutions offer a mandatory first-year seminar course aimed at facilitating students’ successful transition to higher education while supporting students in exploring professional careers and the majors offered at these institutions. Specifically, major selection is a critical component of many first-year engineering and computing science programs. Although many students enter college having declared a major, first-year curricula often include structured opportunities for major exploration to help students make more informed academic and career decisions. In a first-year seminar course at the College of Engineering at a land-grant mid-Atlantic R1 university, three separate activities, (1) a Major Exploration Project, (2) Out-of-Class Experience (OCE) events, and (3) an online learning platform paired with a textbook, were methodically integrated to support students’ exploration of various engineering and computing disciplines, to help students make better informed choices and reinforce students’ decision on their academic and professional career pathways.
This work-in-progress (WIP) paper examines students’ perceptions of the value behind the three aforementioned activities designed to support major selection in a first-year seminar course. Quantitative survey data were collected from first-year engineering and computing science students enrolled in the course during the Fall 2025 semester (n = 542). Survey items related to the Major Exploration Project assessed students’ perceived understanding of potential career pathways, confidence in independently exploring engineering or computing topics, increased interest in engineering and computing disciplines, and overall learning experience gained from the project. Survey items related to the OCE events examined students’ perceived awareness of engineering and computing disciplines, interest development, understanding of professional career pathways, and motivation to explore new areas within engineering and computing sciences. Survey items for the online learning platform and textbook focused on students’ perceived clarity and engagement of the content, enjoyment and perceived usefulness of the associated videos and homework assignments, and ease of access to the platform.
Preliminary findings across the first-year engineering and computing science majors within the College of Engineering suggest that these major exploration activities function as important intervention points to clarify disciplinary distinctions, challenge initial preconceptions, and provide authentic exposure to professional engineering environments. These activities appear to influence students’ perceived capabilities and alignment of interests with specific disciplines. However, the findings also indicate that a subset of students remained undecided after completing these major exploration activities, suggesting that additional or continued interventions beyond the first-year seminar course may be necessary to further support their major selection. Additionally, it was observed that some engineering programs in the College of Engineering benefited from the recruitment and retention of students with the OCE events, highlighting the importance of organizing events where all majors are equally represented and supported, for students to discover their respective engineering majors.
This research contributes to the literature on strategies that support first-year engineering retention and students’ early career development and offers guidance for engineering programs and curriculum design.
Henry Cabra, Felipe Gutierrez
As society and technology evolve rapidly, engineering schools around the world must adapt their curricula to keep pace. Their mission is clear: to produce highly skilled, diverse, and motivated professionals who are prepared to drive technological innovation and make a significant impact on society in an increasingly competitive global economy. However, the courses, university advising, and community environment offered to first-year engineering students often do not align with or support their chosen engineering majors. Additionally, these students frequently lack adequate information on how their technical knowledge can help them build strong networks of support and friendships outside the classroom. This disconnect can lead to unmet expectations, low academic achievement, and decreased student retention.
The low sense of belonging in the engineering field among first-year students is primarily due to several factors:
• First, there are minimal or no engineering applications integrated into the first-year courses of the engineering program.
• Additionally, students have little to no contact with engineering faculty because of the structure of these courses.
• Science concepts are often taught in isolation, lacking connections to real-life applications, which hinders long-term memory formation. Additionally, students often work alone, limiting their personal and professional growth and essential interpersonal skills like teamwork, conflict resolution, and leadership.
• Lastly, the advising during the first year tends to be very general, providing students with insufficient guidance to navigate student life or make informed curriculum decisions.
To effectively address these issues, this WIP paper proposes redesigning the first year of the engineering programs at Texas State University. Our primary objective is to implement a robust problem-based learning curriculum that emphasizes real-world problem-solving through an engaging first-year design project. This initiative will establish a more applied, comprehensive, and holistic first-year curriculum that includes person-centered advising and mentorship.
The program will merge the "University Seminar" course with "Introduction to Engineering" courses from each engineering discipline. Taught by an engineering professor, the new course will require students to work on a team project that integrates concepts from their first-year curriculum (e.g., Physics, Chemistry, Calculus). This project will connect engineering principles with fundamental sciences and facilitate direct interaction with faculty.
In summary, the proposal advocates for:
• Creating an engaging engineering experience that enables students to apply abstract concepts learned in their science, language, social studies, and ethics classes to solve real-world engineering problems.
• Promoting the development of critical thinking skills while enhancing communication and collaboration among students.
• Motivating deep learning, encouraging innovation, and fostering students' autonomy and responsibility in their learning process.
• Aligning with current global education trends and addressing labor market needs.
By implementing these approaches, we aim to enhance belonging and support our students' intellectual curiosity and commitment. The project-based method will boost retention, motivation, adaptability, and academic performance among engineering students, leading to a more enriching educational experience for all.
Gretchen Dietz
This work-in-progress paper presents the development and early implementation of Engineering Discovery, a first-year engineering experience designed to increase student engagement, sense of belonging, and retention at a primarily undergraduate institution in Western North Carolina. The program integrates hands-on, project-based learning, industry exposure, and structured reflection to help students explore engineering disciplines while building foundational skills and confidence.
Grounded in research on student persistence and early academic momentum, Engineering Discovery emphasizes early connection to the engineering identity through authentic experiences, including design challenges, field trips to regional industry partners, and collaborative problem-solving activities. The program is intentionally designed to support students who may be uncertain about their chosen major or lack prior exposure to engineering.
Preliminary observations suggest that students participating in Engineering Discovery demonstrate increased engagement, improved confidence in problem-solving, and stronger peer connections. This paper outlines the program structure, pedagogical framework, and initial assessment strategy. Future work will evaluate the program’s impact on first-year retention, academic success, and long-term persistence in engineering pathways.
Gregory Bucks
Jeremy Hill
Jeff Kastner
Sheryl Sorby
This Work In Progress paper describes efforts made at the University of Cincinnati (UC) to increase pathways for students transferring into the College of Engineering and Applied Science (CEAS) after completing some or all of their first-year courses at another institution. The primary transfer pathways into CEAS include students enrolled at regional campuses, students that transfer in from other institutions, local high-school students participating in a College Credit Plus program, and students at the University who decide to enroll in engineering during their first or second year. Currently, students in the CEAS take 3 credit hours of first-year engineering courses in both the Fall and Spring semesters. These 6 credit hours have created bottlenecks for students transferring into the college as the courses cover a wide range of content and must be completed before students can begin their co-op rotations. To begin addressing this issue, the 6 credit hours are being split into 11 modules which are available in-person or online. By dividing the content this way, the regional campus instructor can easily use the material to teach their sections, local high-school teachers can utilize the content in a similar manner but also align the content delivery with their school year, and students transferring into the University/CEAS do not need to take the full 6 credits if they have already taken some of the content prior to transferring. These approaches allow transfer students to pave a path that can quickly get them on track for graduation without losing access to some of the fundamental knowledge taught in these courses. There are roughly 250+ students each year entering CEAS from one of these pathways and this number is growing, further emphasizing the need for unique approaches like this. This paper takes a deeper dive into the pathways, how the modularization of the course has played a key role in getting transfer students on schedule quicker, and matriculation data to UC and CEAS will be presented to further support the advantages of the modular approach described above.
Jeffrey Carver
Manar Yamany
The first year of engineering education represents a critical transition period during which students begin to form and evaluate their confidence in pursuing engineering careers. While many first-year engineering programs emphasize technical foundations and introductory design experiences, often less attention is given to understanding student confidence related to their chosen pathway when they begin their studies. This work-in-progress study examines the career confidence of first-year engineering students and explores how early academic and experiential pathways may shape students’ perceptions of alignment between their major and future career goals. The study draws on survey data collected from undergraduate STEM students (N = 216) at a large research institution in the United States and focuses specifically on first-year engineering students (n = 156). The study was conducted under approved Institutional Review Board protocols. The primary research question guiding this study asks: How confident are first-year engineering students that their current major will lead to a career aligned with their goals? Descriptive analysis was conducted on responses to a career confidence survey completed by 156 first-year engineering students who provided valid responses. Results indicate that 36.5% of students reported being very confident that their major will lead to a career aligned with their goals, while 53.2% reported being somewhat confident. In contrast, 9.0% of students reported neutral confidence and 1.3% reported low confidence in their engineering career pathway. Although most first-year engineering students reported moderate to high confidence in their chosen major, a smaller group reported uncertainty about their future career pathway. Preliminary analyses also suggested that students with greater familiarity with industry expectations reported higher career confidence, indicating that industry awareness may be one factor associated with confidence during the first year. These findings suggest that first-year engineering programs may benefit from incorporating structured opportunities for career exploration, industry awareness, and mentoring that help students connect academic experiences with future career pathways. By examining career confidence during the early stages of engineering education, this study contributes to ongoing discussions within the First-Year Engineering Experience community about how first-year programs can better support student engagement, confidence, and persistence in engineering pathways.
Prarthona Paul
Chirag Variawa
In recent years, Generative Artificial Intelligence (GenAI) tools have caused significant shifts within engineering, transforming academic and professional practices and ways of working [1], [2], [3]. This has changed the skill demand across numerous professions, including engineering [4], [5], [6], [7]. A federal government study spanning 10 years identified that vacancies for some engineering entry-level roles have declined since ChatGPT became publicly available in 2022 [8]. Whether this is a causation or correlation remains to be seen, but it is clear that employment skills are affected, nonetheless. As engineering educators, it is important for us to prepare students for an AI-enabled workforce, starting as early as in first-year.
Some universities have outlined their own policies and recommended practices for using AI in academic contexts [9], [10]. However, some of these policies are, to some degree, in a constant state of flux due to the evolution of AI technologies and approaches. Amid these changes, how do we as instructors adapt existing first-year engineering design curricula to prepare students for an AI-enabled workplace within institutional guidelines?
In this exploratory study, we use constructive alignment as a framework to investigate the relationship between institutional policies, industry skill demands, and a specific first-year engineering design curriculum [11]. We focus our investigation on two large (1000+ student) first-year engineering design courses at an R1-public university in Canada. We compare course learning outcomes, teaching methods, and assessments with existing literature on AI competencies and workplace expectations. We also consider institutional factors by grounding our analysis in the university’s AI policies and recommended practices. Based on our findings, we propose improvements to future iterations of these courses to enable our students to effectively use AI in their engineering education and beyond.
Mahathi Chinthapalli
Pranavi Kamana
Sarah Kliger
Kaitlyn Mathew
Samvitti Nag
Sruthi Pillai
Generative artificial intelligence tools are rapidly becoming part of how students approach learning, problem solving, and information gathering in early engineering coursework. While these tools can provide explanations and guidance on demand, less is understood about how ongoing interaction with these tools shapes the responses they generate over time. In particular, conversational history, previous prompts and exchanges stored within a session, may influence the style, depth, and reliability of the responses students receive. Understanding this dynamic is important as first-year engineering students increasingly rely on AI tools when exploring unfamiliar technical concepts, particularly as these tools may serve as accessible, on-demand learning supports for students with varying levels of prior preparation.
This work-in-progress study investigates how conversational history influences the linguistic characteristics, explanatory depth, and accuracy of generative AI responses to first-year engineering learning prompts. In the study design, multiple participants create new AI chat sessions and interact with the system over several days using a shared set of prompts representing common types of student exploration including factual questions, conceptual explanations, and reflective or opinion-based prompts. A control condition using a fresh session with no prior conversational history is used for comparison. Responses are analyzed using a combination of linguistic and qualitative metrics, including syntactic complexity, response length, explanatory structure, consistency across sessions, and factual accuracy.
Initial observations will explore whether extended conversational interaction leads to measurable changes in how AI systems construct explanations or present information. By examining patterns across different prompt types and conversational contexts, the study aims to better understand how interaction history may shape the outputs students encounter when using AI tools to support their learning.
The findings of this work contribute to ongoing conversations about the responsible integration of AI into first-year engineering education. In particular, the study highlights the importance of helping students critically evaluate AI-generated explanations and recognize how system behavior may evolve during extended use. These insights may be especially valuable for supporting a broader range of learners, including those who may rely more heavily on AI as a supplementary learning resource, and for informing instructional practices that promote equitable and effective use of emerging technologies. These insights may inform instructional strategies that encourage thoughtful engagement with generative AI while promoting informed skepticism and deeper conceptual understanding among early engineering learners.
Md. Ali
Mohammad Khalid
Construction work is physically demanding and increasingly affected by rising global temperatures and more frequent extreme-heat events. Unmanaged occupational heat stress can contribute to worker injuries, cognitive impairment, productivity loss, absenteeism, and increased project and compensation costs. Despite these growing risks, undergraduate engineering programs rarely provide students with hands-on opportunities to evaluate heat exposure, interpret thermal indices, and formulate mitigation strategies for realistic construction environments. This work-in-progress study introduces TER-ASC (Thermal Exposure and Risk Analytics System in Construction), a web-based informatics platform for heat-stress assessment and decision support in construction education. The platform uses a deep neural network with a physics-based fallback layer to estimate heat-stress and thermal-comfort indices from environmental and worker-specific inputs, including air temperature, humidity, wind speed, solar radiation, clothing insulation, metabolic rate, and working height. The system also estimates productivity impacts and supports mitigation and work-rest planning under varying construction conditions. The platform will be integrated into a three-week instructional module designed for first- and second-year undergraduate students. Week 1 introduces the physiological and operational implications of heat stress through lectures and guided discussion. Week 2 engages students in field- or laboratory-based collection of environmental and physiological data. Week 3 focuses on platform-assisted analysis, interpretation of thermal-risk conditions, and development of mitigation and scheduling strategies under realistic construction scenarios. Student outcomes and platform impact will be evaluated using pre/post heat-stress self-efficacy survey, the System Usability Scale (SUS), and trust-in-automation scale. The study aims to support the development of analytical reasoning and data-informed safety decision-making skills needed to prepare the future construction workforce for increasingly complex environmental conditions.
Atheer Almasri
Michael Brewster
Todd Hamrick
Robin Hensel
Susie Huggin
Carter Hulcher
Akua Oppong-Anane
Gia Huy Pham
Lizzie Santiago
The use of generative Artificial Intelligence (AI) has become widely adopted both in and outside of higher education over the past few years. AI tools have the potential to enhance students’ understanding of programming concepts. However, excessive reliance on these tools may lead to gaps in conceptual understanding. This underscores the importance of integrating AI into programming education in pedagogically meaningful ways that complement, rather than replace, the development of foundational programming skills. In this WIP paper, the authors have deployed an AI chatbot to serve as a teaching assistant (TA) in an introductory programming course for engineering students at a public land-grant R1 university in the Mid-Atlantic region of the United States. Recent studies have explored the use of AI assistants for administrative tasks, upper-level coursework, and Python-based instruction; however, this study deploys a pedagogically restricted AI TA for an entire semester in an introductory MATLAB programming course to measure shifts in student perceptions of AI and their programming proficiency.
This chatbot was deployed in the Spring 2026 semester, and its use and impacts are still being evaluated. The AI chatbot's knowledge included general course information and details on topics covered, homework questions, quizzes and exam topics, and project information. Specific instructions were provided to the AI TA regarding its role and limitations. The AI TA was prohibited from providing direct solution code snippets but was permitted to offer step-by-step guidance to support students with homework challenges. It was also instructed to generate study materials, such as study guides, flashcards, and quiz- or exam-style multiple-choice questions, as well as answer general course-related questions, including information listed in the syllabus and course policies.
Students in all sections of the course consented to participate in this IRB-approved survey and were asked to complete a mixed-method survey at the start of the semester, asking about their opinions on and current/previous use of AI (n = 334). After completing this survey, the AI assistant was released and used by students as a supplemental resource. Student feedback throughout the semester indicated that the AI assistant was most used as a study tool for quiz/exam preparation and homework help, aligning with the intended uses identified in the pre-survey. The authors administered an end-of-semester mixed-method survey to further evaluate students’ perceptions and the utility of the AI chatbot.
This research found that: (1) student trust and comfort in AI rose through the use of the AI chatbot, which can indicate student satisfaction with the AI responses and (2) student expected effort with the use of the AI chatbot was initially lower than that which they actually had to put forth – students still had to put in the effort to learn the material, regardless of having the AI chatbot as an additional resource. The authors anticipate that this work will inform policy at their institution and contribute to the broader academic community’s understanding of how AI can be used effectively in pedagogical development, supporting the meaningful deployment of generative AI in similar courses.
Osman Sayginer
Evelyn Walters
Cory Budischak
The growing presence of artificial intelligence in engineering education has reshaped how students engage with problem solving while raising new challenges for traditional approaches to assessment. As students increasingly rely on AI tools to complete coursework, assessment instruments focused on final outputs provide limited insight into individual reasoning and conceptual understanding. This work-in-progress study explores an AI-assisted conversational assessment approach implemented in ENGR 1102, Introduction to Engineering Problem Solving, a large-enrollment first-year engineering course organized around a semester-long wind energy feasibility study. A custom AI chatbot, built on the Google Gemini API at a total deployment cost of approximately $1.25 for nearly 70 students, was designed to function as an experienced wind energy engineer. Students participated in a structured 30-minute chatbot quiz in which they explained their methodology, justified design decisions, and reflected on their results through written dialogue. The chatbot posed probing follow-up questions and offered guidance when students encountered difficulty, creating a hybrid evaluation and learning experience that partially mimics an oral exam while reducing performance anxiety. Post-activity survey results indicate that 76% of students agreed the format encouraged deeper critical reflection and that the AI provided useful, actionable guidance. Student comfort and format preferences were more varied, likely reflecting unfamiliarity with the modality. Qualitative responses highlighted adaptive follow-up questioning and immediate corrective feedback as the format's core strengths, while verbosity and technical reliability emerged as the primary areas for improvement. This study contributes initial evidence on the feasibility of AI-assisted conversational assessment in large first-year engineering courses and highlights its potential to support more meaningful evaluation in AI-rich learning environments.
Laura Riggio, Evelyn Walters, Julie Drzymalski, Cory Budischak
Retention in engineering programs remains a persistent challenge, with approximately half of students leaving engineering majors before graduation. Research suggests that engaging first-year students in hands-on design experiences can improve persistence by helping students connect engineering concepts to real-world problems [1]. Introductory engineering courses frequently use open-ended design projects to teach the engineering design process, including defining design criteria and developing methods to test whether a design meets those criteria. However, novice engineers often struggle to translate design requirements into measurable evaluation methods.
Our Introduction to Engineering course is structured around a semester-long incubator design project intended to introduce first-year students to the engineering design process. Assignments throughout the semester are scaffolded to focus on key elements of design thinking, including problem identification, development of design criteria, and creation of testing methods. However, instructors have observed that student design proposals often include design criteria that are vague or not measurable, making it difficult to develop meaningful testing methods to evaluate whether a design meets its requirements. Because the ability to translate design goals into specific, testable criteria is a fundamental engineering skill, improving students’ ability to develop measurable design criteria and corresponding evaluation methods became a focus for course improvement. In this study, we explore whether generative AI can support students in developing testable evaluation methods within a first-year team-based incubator design project.
Research Question
To what extent does generative AI support first-year engineering students in developing specific, measurable design criteria and corresponding test methods for their designs?
Intervention
To support students in developing more specific and testable design criteria, generative AI was introduced as a structured support tool during the proposal stage of the incubator design project. During the Fall 2025 semester, students were encouraged to use generative AI to help generate and refine design criteria and testing methods based on issues identified in their initial incubator prototypes. While some students were able to effectively use AI to improve the specificity of their proposals, others struggled to apply the tool productively due to limited guidance on how it could support the design process. Based on this feedback, the instructional approach was refined for the Spring 2026 semester. Additional guidance was provided to help students understand how generative AI could be used to translate design goals into measurable criteria and corresponding testing methods during proposal development.
The impact of the intervention will be assessed through artifact analysis of student design proposals using an instructor-developed rubric. The rubric evaluates the quality of students’ design criteria and corresponding test methods, focusing on the specificity and measurability of the criteria and the extent to which the proposed testing methods appropriately evaluate those criteria. Each proposal will be scored across several dimensions, including clarity of design criteria, whether the criteria are operationalized in measurable terms, and the alignment between the criteria and the proposed evaluation methods. Rubric scores will be used to compare the quality of proposals developed with generative AI support to those developed without AI assistance from the previous academic year.
MONDAY, AUGUST 3RD | 10:30 - 11:15 AM
WIP: Engineering Where It Happened: Experiential Learning in Engineering History 25
WIP: Interdisciplinary Hands-On Learning and Field Trips in a Summer Bridge Program 29
WIP: Continued Validation of Self-Efficacy Instruments For Pre-Post Assessments 30
WIP: Fundamental of Engineering Increase in Student understanding of the Engineering Design Process 31
WIP: Short-Form Instructional Media to Support Rapid Skill Acquisition 32
WIP: Is it safe?: Exploring safety standard integration in First-Year Engineering Courses 33
Robin Hensel robin.hensel@mail.wvu.edu, Susie Huggins susie.huggins@mail.wvu.edu
This Work-in-Progress (WIP) paper examines the use of multiple optional field trips as a strategy for incorporating site-based learning into an undergraduate engineering history course. The approach was designed to help students connect engineering concepts to real-world historical and industrial contexts by engaging directly with local sites of technological significance. By visiting locations such as the Henry Clay Furnace and a regional technology museum, students encountered engineering as a practice shaped by materials, landscape, labor, and community rather than solely as abstract technical problem-solving. These experiences provided opportunities for students to observe physical artifacts of past engineering work and consider how technological development emerged from specific social and environmental contexts. While two locations were visited during one semester, only one of those locations was offered in all three semesters. Therefore, this WIP paper examines student response data associated with visits to that site across three semesters.
The study employed a qualitative narrative inquiry methodology to explore how students interpreted and processed these experiential learning opportunities and how the visits influenced their understanding of engineering history and practice. Narrative inquiry was selected because it examines learning as experience unfolding across time, social interaction, and place. Data sources included instructional planning notes documenting the rationale and logistics for organizing the field trips, researcher field notes and observations during the visits, informal conversations with participating students, and student-generated artifacts such as photographs taken during the excursions. Together, these sources captured both the design intentions of the activity and the ways students documented and reflected on the experience in situ.
The field trips were implemented as optional, low-stakes learning opportunities for students enrolled in an introductory engineering history course, with participation open to both in-person and online sections. Preparation focused primarily on logistical coordination – transportation, scheduling, safety information, and meeting locations – rather than structured assignments. During the visits, faculty members informally guided students through the sites, providing historical context and explaining relevant engineering processes while encouraging observation and discussion. Students explored site features, examined remnants of industrial activity, and documented the experience through photographs and informal reflection. The absence of graded tasks allowed the visits to remain exploratory and student-driven while still providing an immersive learning experience.
Preliminary findings suggest that field-based encounters helped students connect course concepts to tangible engineering artifacts and prompted discussion about the historical and social dimensions of engineering practice. Future work could examine how such experiences influence students’ conceptual understanding and perceptions of engineering, while documenting participation patterns, successes, and barriers to better understand how community-based contexts can strengthen engineering education.
Amy De Jongh Curry adejongh@memphis.edu, Logan Sirbaugh
This work-in-progress paper reports on the continued development of a Work-Based Learning (WBL) program that embeds high school students into university research laboratories alongside first-year engineering coursework. A prior work-in-progress study introduced the program's design and preliminary observations from its initial implementation. This paper extends that work by describing emerging outcomes as the program enters a new phase in which early participants are now matriculating into major-specific engineering programs and returning to research labs as paid contributors.
The program, a partnership between the [university college of engineering] and on-campus [high school], delivers a two-semester sequence for 11th and 12th grade students. Fall semester pairs [first-semester problem solving course] with one-week rotations through six research laboratories spanning biomedical, civil, electrical/computer, and mechanical engineering. Spring semester advances students to [second-semester problem solving course] with semester-long placements in preferred laboratories. Assessment integrates WBL components (20%) with traditional coursework (80%).
Three developments since the initial study motivate this continued investigation. First, graduates of the inaugural cohort have enrolled in first-year major-specific engineering programs at the [university], offering an early window into how rotational research exposure may influence informed major selection. Second, a second cohort of high school students is currently progressing through laboratory placements, creating an opportunity for cross-cohort comparison. Third, graduating seniors are now placed in paid internship positions within their previous research laboratories, where they contribute actively to faculty-led funded grants. This transition from mentored learners to compensated research contributors suggests the program may function as a sustained pipeline rather than a standalone pre-college experience.
This paper examines four evolving research questions:
1. How does rotational laboratory exposure influence major selection patterns?
2. What professional competencies emerge through embedded research experiences, and do these transfer into paid research roles?
3. How effectively does the WBL-coursework integration model support learning outcomes across multiple cohorts?
4. How does the multi-directional mentorship structure scale as the program grows?
Planned data collection includes academic performance metrics, engineering self-efficacy measures, major selection and retention tracking, and qualitative analysis of the intern transition. This scalable model aims to contribute a replicable framework for university and high school research partnerships that create early pathways into the research enterprise.
Theo Fairchild-Coppoletti fairchildcoppolett.t@northeastern.edu, Don Heiman d.heiman@northeastern.edu, Haridas Kumarakuru phxkh79@gmail.com, Bala Maheswaran mahes@coe.northeastern.edu, Brendan O'Riordan oriordan.b@northeastern.edu
Hands-on design experiences are essential for helping first‑year engineering students bridge the gap between theory and practical application. In this project, we designed and tested a low‑cost smart irrigation prototype built around a capacitive soil‑moisture sensor, with the goal of creating an accessible platform for introducing students to the engineering design process, open‑ended problem solving, and collaborative prototyping.
The sensing approach relies on the relationship between soil permittivity and water content, using a parallel‑plate capacitor to detect changes in capacitance as moisture levels vary. These changes are converted into a measurable square‑wave signal through a 555‑timer circuit and interpreted by an Arduino microcontroller. This structure allows students to engage with core electrical engineering topics, including capacitance, signal conversion, and microcontroller interfacing, through direct experimentation rather than solely through theory.
Throughout development, we emphasized iterative design and teamwork by constructing the sensor using inexpensive components that support widespread adoption in introductory engineering courses. To improve reliability during testing, we created a custom 3D‑printed enclosure, giving students experience with CAD tools, rapid prototyping, and the integration of mechanical and electrical design considerations.
The project reinforces foundational skills such as circuit assembly, sensor calibration, programming, and data collection, while encouraging students to troubleshoot, analyze system behavior, and refine their design through multiple iterations. The real‑world context of water‑efficient irrigation also helps students recognize the societal relevance of engineering solutions and motivates creative thinking about sustainable technologies.
Future improvements may include extending the prototype into a fully automated irrigation system driven by sensor feedback. Overall, this work demonstrates how a simple, low‑cost sensing platform can effectively support first‑year students in learning interdisciplinary design principles, practicing teamwork, and engaging in meaningful open‑ended engineering challenges.
Don Heiman d.heiman@northeastern.edu, Haridas Kumarakuru phxkh79@gmail.com, Orlando LuPone lupone.o@northeastern.edu, Bala Maheswaran mahes@coe.northeastern.edu, Daniel Vogt daniel.t.vogt06@gmail.com
This project explores the design of an Arduino-driven cooling system capable of temperature-dependent fan speed modulation using a compact, low-cost hardware architecture. The goal was to develop an embedded heat management system that regulates airflow using inexpensive components while maintaining responsive temperature control. A proof-of-concept prototype was implemented on a solderless breadboard and demonstrated reliable fan speed regulation based on temperature measurements and preset threshold values.
Developing the system required the integration of temperature-dependent resistance behavior, control logic, microcontroller interfacing, and embedded programing within a functional prototype, thus enhancing experiential and activity-based learning. During implementation, several real-world engineering challenges were encountered, including sensor variability, signal instability, and discrepancies between theoretical predictions and measured performance. These challenges were addressed through an iterative engineering design process involving subsystem isolation, firmware refinement, structured testing and teamwork. Through this process, we examined practical factors such as component tolerances, Analog-to-Digital Converter (ADC) resolution limits, and thermal response behavior, which reinforced our problem-solving skills and our ability to work collaboratively.
Future development will focus on transitioning the prototype toward a deployable system through the design of a 3D-printed enclosure and a custom printed circuit board (PCB). These improvements will enhance reliability, airflow management, and manufacturability, while reducing wiring complexity and interferences. The resulting compact thermal management module demonstrates a scalable approach to low-cost embedded cooling with potential applications in small-scale electronics, including battery packs, CPUs, and other heat sensitive devices.
Atheer Almasri atheer.almasri@mail.wvu.edu, Robin Hensel robin.hensel@mail.wvu.edu, Susie Huggins susie.huggins@mail.wvu.edu, Lizzie Santiago lxs187@yahoo.com
This work in progress examines the impact of early major exploration hands-on learning activities and field trips, introduced through an interdisciplinary approach, implemented in the Summer Bridge program on student retention and academic success during their first semester at the university. Bridge programs play an important role in supporting student transition, retention, and academic success in engineering education. Active and experiential learning approaches have been shown to significantly increase student engagement, understanding, and retention compared with traditional lecture-based instruction. This program was developed through collaboration between multiple departments within the engineering college, with the goal of expanding experiential learning opportunities and exposing students to a broader range of engineering disciplines. As part of this approach, different departments designed and facilitated interactive activities that emphasized practical application and real-world engineering problem-solving.
Guided by the research question, “How does the integration of interdisciplinary hands-on learning and field trips affect student engagement, interest in engineering, retention, and academic success in the * program?”, this study evaluates student perceptions and faculty observations of the program. Data were collected through a post-program survey completed by 30 participants. Students were asked to evaluate their learning experiences, rank their preferred activities, and provide qualitative feedback on program components.
Preliminary results indicate that hands-on activities introduced through the interdisciplinary collaboration were consistently ranked among the most engaging aspects of the program. Activities such as metal fabrication, drone coding, field trips, project presentations, and the ropes course team-building activity are the highest rankings from participants. Qualitative feedback from an open-ended question supports these findings, with many students expressing a clear preference for interactive and experiential activities over lecture-based sessions. Participants frequently recommended increasing the amount of time dedicated to project-based work and expanding hands-on opportunities within the program. Furthermore, by the end of their first semester, approximately 50% of the students in the program had earned a cumulative GPA of 3.5 or higher, 90% were retained in engineering, and 100% were retained at the institution.
Overall, the preliminary results suggest that integrating interdisciplinary collaboration across engineering departments could enhance experiential learning opportunities and strengthen student engagement and retention. These findings highlight the potential role of the departmental engineering programs in creating meaningful, hands-on learning experiences that support early major exploration of engineering and computing pathways and play a potential role in supporting student transition, retention, and academic success.
Aturika Bhatnagar ab2765@njit.edu, Prateek Shekhar pshekhar@njit.edu
Entrepreneurship education (EE) equips engineering students with entrepreneurial skills in addition to the technical skills. Often, EE employs survey instruments for evaluation using data collected at the beginning (pre) and end (post) of the intervention. However, to ensure rigorous comparisons between the two time points (pre- and post), the instruments need to possess temporal validity and reliability. The presented work-in-progress study discusses continued validation of an instrument using multi-group analysis. The study examines the following research question: To what extent does the latent factor structure of an instrument (comprising of searching, planning, and design constructs) remain invariant across the pre- and post-groups? The data for the study were collected across three semesters in 2024-2025, with 187 pre- and 140 post responses. The reliability of the instrument was measured using Cronbach’s alpha, and validity was evaluated using Confirmatory Factor Analysis (CFA). Additionally, measurement invariance was examined to evaluate the consistency of the relationship between the observed variables and the latent variables across pre-post data. Results suggested that configural, metric, and structural invariance were achieved, indicating similar structures, factor-loading contributions, and path coefficients across the pre- and post-groups. The findings suggest that the instrument reliably and validly measures the pertinent constructs across both time points and can be used for pre-post assessment in engineering education. Furthermore, a paired t-test was conducted to assess changes in students’ confidence following exposure to the entrepreneurial modules integrated into the existing undergraduate engineering curriculum. Results of the paired t-test suggested a change in the students’ entrepreneurial and design self-efficacy after being exposed to entrepreneurship modules.
Olumuyiwa Bamisaye oob2@njit.edu, Brandon Igot brandonigot01@gmail.com, Patrick Thornton pt365@njit.edu
In this Work In Progress paper, we are going to describe how mid-size Stem university has changed its lab instruction to improve student understanding of the Engineering Design Process (EDP) as well as the depth and width of capstone projects presented at the end of the semester and during the Freshman Engineering Design showcase event. The major goal of this course is to establish a strong foundation in the EDP, teamwork and the ability to execute a project with a real world application.
The student set-up in each group was designed to imitate the pattern used in consulting engineering firms. The students in some groups have different interests and are going for different degrees. The diversity of strengths and interests create opportunities for the students to assist each other and build teamwork and communication skills. This group dynamic is further reinforced during the instructional period of the lab when they are learning specific skills in CAD or programming, with having to work as a group first to complete assignments.
The students in the Fundamentals of Engineering Design lab sections were assigned to their groups during the 2nd week of the semester, and were assigned weekly group assignments.The basic lab format was to allow the learning of the skill from in-class exercises, then move to demonstrating their knowledge with the homework assignment, and finally apply their knowledge to the final project.This enabled the students to immediately apply the knowledge they were learning to the final project and understand how each step they were taking further developed their final project.The class during the process of learning SOLIDWORKS and programming C++ were held in a computer lab, and upon completion of those lessons, we then moved into the MAKERSPACE here on campus so students could have quick and easy access to the tools to make their prototypes and test them for their final presentation at the end of the semester.
Zoe Edwards zedwards@terpmail.umd.edu, Giang-Nam Facchetti gfacchet@terpmail.umd.edu, Matthew Paul matpaul321@gmail.com
The rapid growth of short-form video platforms such as TikTok, Instagram Reels, and YouTube Shorts has reshaped contemporary information consumption patterns, suggesting that instructional communication strategies in engineering education should adapt to incorporate concise and visually driven content aligned with these emerging media formats. First-year engineering design courses require students to rapidly acquire technical fabrication, prototyping, and engineering skills while simultaneously completing a team-based project. In Introduction to Engineering Design (ENES100) at the University of Maryland, students must learn various fabrication and key engineering skills, such as CAD, programming, electronics, and system testing within compressed timelines. Traditional lengthy instructional videos or written documentation is often poorly aligned with the point at which hands-on work is being performed, limiting their effectiveness during active lab sessions. This work describes the development of a structured short-form video repository designed to support skill acquisition at the point of application in a project-based first-year engineering course. The intervention consists of two minute long, single-skill instructional videos embedded in learning management systems (LMS) and accessible via QR codes posted throughout the classroom lab space. Videos are aligned with course milestones and focus on common technical bottlenecks such as soldering fundamentals, CAD file preparation for 3D printing and laser cutting, Arduino programming, and class-specific tool utilization. The model supports pre-lab preparation, in-lab execution, troubleshooting, and out-of-class review. This scalable and low-cost approach leverages short-form instructional media to enhance student autonomy and support rapid technical skill development in first-year engineering courses.
Bethany King Wilkes bkingwilkes@gmail.com, UrLeaka Newsome umwoodard@gmail.com
Technical standards shape the safety, sustainability, and societal impact of engineered systems; however, undergraduate students often encounter them only indirectly or late in their academic programs if at all. Prior findings suggest that standards education in first-year engineering courses is limited or absent [1]. Faculty may also face barriers to integrating standards into courses due to time constraints, limited familiarity with technical standards, or uncertainty about how to scale activities across course levels. Guided by an experiential learning model and Bloom’s Taxonomy demonstrated by Wu, Kang, and Cleary [2], we present a pedagogical framework for integrating standards education into engineering curriculum using freely available resources from Standards Academy. The framework conceptualizes standards as collaborative tools developed to address real-world risks and public needs and introduces at least three levels of implementation – exposure, application, and integration. This work examines faculty and student perceptions of Standards Academy as a tool for supporting standards education through semi – structured interviews. Faculty of first-year engineering students will also respond to examples of classroom activities and implementation guidance incorporating Standards Academy. Findings will provide practical direction for incorporating standards into first – year engineering instruction.
Heydi Dominguez hld6@njit.edu, Carlos Perez clp9668@njit.edu, Prateek Shekhar pshekhar@njit.edu
This work-in-progress (WIP) research paper describes preliminary results examining what influences engineering students' motivation in entrepreneurial activities when participating in an engineering course. Universities around the world have created different entrepreneurship education (EE) offerings to train engineering students in entrepreneurial thinking and better prepare them for a competitive work landscape. However, there is limited research that explores student motivation in the context of EE in engineering education. Considering the importance of student motivation in educational development and research, this qualitative study examines the research question: What factors inform engineering students’ motivation when engaging in EE environments? In our ongoing work, we conducted two focus groups of 3 participants each with first and second-year engineering students at a research-intensive university in the United States. Reflexive thematic analysis, with deductive and inductive coding, was employed to generate themes that elucidated students’ motivations to engage in EE environments. Through deductive coding, we leveraged the Attention-Relevance-Confidence-Satisfaction (ARCS) instructional design model to unpack students’ motivations. Preliminary findings include the motivating factors of autonomy in project design, career relevance, supplemental video resources, and reflective assessment. Students stated that aligning course projects with their interests and passions would expand their motivation. Overarchingly, the findings underscore that a variety of sources inform student motivation and the utility of the ARCS model to understand student motivations in the context of engineering EE. Key findings from ongoing analysis note that increasing the relevance of EE programming by situating EE in student contexts is critical for fostering student motivation.
Heydi Dominguez hld6@njit.edu, Carlos Perez clp9668@njit.edu, Prateek Shekhar pshekhar@njit.edu
Large-enrollment first-year engineering courses provide limited opportunities for individualized student-faculty interactions, leaving many students feeling anonymous in a large lecture hall and consequently hesitant to seek support when needed. This work-in-progress study describes the implementation of a structured one-on-one instructor-student meeting in a large first-year engineering course and examines student perceptions of the experience. In a large introductory engineering course [N =145; Fall 2025], students were offered a 15-minute one-on-one meeting with the course instructor as one of the six options within a professional development assignment, of which students were required to complete any five. Students were provided with a structured discussion guide and flexible scheduling to arrange the meeting anytime during the semester. Discussion topics included motivation for joining the major, areas of interest within the major, study habits and academic challenges, course feedback, and an open topic of student’s choice. Following the meeting, students submitted a written reflection on the interaction and its perceived impact. This study presents preliminary findings from a qualitative analysis of student reflections from 98 students who completed the instructor meeting. Student reflections revealed themes related to students’ motivation for choosing the major, areas of interest, and course and career concerns as well as themes reflecting clarity, connection and belonging in the course. These preliminary findings contribute to an improved understanding of student needs for connection in a first-year engineering course and suggest a single 15-minute structured interaction with the instructor could meaningfully shift students’ perception from feeling anonymous to feeling seen, heard and supported. Future work will extend this study with pre- and post-meeting surveys and a comparison of emerging themes across two student cohorts to further examine how structured one-on-one meetings shape students’ sense of connection within a large-enrollment course.
MONDAY, AUGUST 3RD | 1:30 PM - 3:00 PM
Workshop: Empowering Educators: Modern Teaching Resources with MATLAB and Generative AI (Sponsored) 37
Workshop: Integrating Motivational Interviewing Approaches to Strengthen Student Retention 38
Workshop: Preparing Your Students for Modern Engineering Workflows (Sponsored) 39
Ram Krishnamurthy, MathWorks
Abstract: First-year engineering (FYE) programs face increasing challenges in engaging large, diverse student populations while building foundational skills, confidence, and engineering identity. This interactive 90-minute workshop introduces practical, scalable teaching strategies that leverage cloud-based computation, simulation, and generative AI using MATLAB and Simulink.
Participants will explore how to:
• Engage first-year students with real-world engineering problems using hardware, IoT activities, Live Notebooks, MATLAB and Simulink.
• Support diverse learners and promote independent exploration with interactive MATLAB Course Designer, MATLAB Apps, and self-paced MATLAB Academy resources.
• Provide scalable, consistent feedback in large introductory courses through automated assessment and mentoring with MATLAB Grader and MATLAB Copilot.
• Strengthen critical thinking and problem-solving skills through project-based assignments and hands-on computational activities.
• Accelerate curriculum development and instructional efficiency using Generative AI tools such as MATLAB Copilot and Simulink Copilot.
• Model responsible and effective use of AI in engineering education with MATLAB’s LLM integrations and guided Copilot interactions.
Angy Estrada
Miosotis Hernandez
Improving student retention and supporting students on academic suspension requires a strategic, student-centered advising approach. This session explores how advisors can incorporate Motivational Interviewing (MI) techniques to foster student ownership, resilience, and informed decision-making during academic difficulty.
Participants will learn how to combine MI strategies with data-informed outreach using tools such as EAB Navigate 360 to proactively identify and support academically underperforming students. The presentation will highlight practical techniques that encourage self-reflection, strengthen intrinsic motivation, and align academic choices with professional interests through experiential learning opportunities.
Attendees will leave with actionable strategies to design personalized outreach plans, guide students toward academic recovery, and support progress toward degree completion.
Matt Shields, OnShape
Abstract
At its root, engineering is about creative problem solving, critical thinking, systems thinking, communication, and collaboration. These essential skills have been taught in engineering schools around the world for centuries and it is unlikely that they will change. What continues to change – and change at an increasing rate – is the tools and workflows that new engineers will use in their careers while engaging in these perennial skills.
Artificial Intelligence, autonomous systems, distributed workforces, additive manufacturing, big data, and other modern developments are all challenging how schools think about preparing engineering students for the future. In particular, engineering education is seeing a transition from traditional CAD courses to modern digital engineering workflows. This affects how design is taught, documented, and shared.
Being cloud-native, Onshape is already the most modern and versatile professional CAD and product development platform on the market. It is also uniquely situated to grow and adapt as new technologies and design methodologies arise. In this hands-on workshop, we will demonstrate some of the capabilities that come with cloud-native CAD software, such as:
● AI-enhanced modeling and rendering
● Model-based definition (MBD)
● Built in PDM; version control, branching, and merging
● Real-time seamless collaboration
● Data analytics
● Cloud-based simulation
● Augmented reality
Through hands-on, collaborative exercises, workshop participants will experience first-hand the benefits of cloud-native CAD. They will use some of these new digital engineering workflows and come away with practical ways to use them in their courses.
MONDAY, AUGUST 3RD | 3:15 - 4:45 PM
Full Paper: Exploring Major Interest, Choice, and Change in a General Engineering Program & Beyond 42
Full Paper: Understanding Students’ Goal Orientation During the First-Year Engineering Transition: Evidence from a Summer Bridge Program 44
Full Paper: Preparing for Engineering Before College: A Conceptual Framework Linking Robotics Experiences to First-Year Engineering Readiness 45
Full Paper:A TAM-based Evaluation of AI Tool Adoption and Benefits in First-year Learning Experience 46
Full Paper:Assessing First-Year Engineering Students’ AI Literacy Through Problem Statement Evaluation 47
Full Paper: Supporting Well-Being in First-Year Engineering: Towards a Practical Mindfulness Implementation Framework and Directions for AI-Adaptive Delivery 48
Elizabeth Breton ebreton@hcc.edu, Tyler Clark tclark@sagefoxgroup.com, Alan Peterfreund apeterfreund@sagefoxgroup.com, Roberta Rincon roberta.rincon@swe.org, Michael Rust michael.rust@wne.edu, Katherine Schlef katherine.schlef@wne.edu, Joyce Wang jwang@sagefoxgroup.com
Latinx students remain underrepresented in engineering despite representing a growing share of the U.S. population. Community colleges serve as key access points for these students, yet little research examines how mentoring interventions in these settings shape professional identity and belonging. This qualitative study investigates how structured informational interviews between Latinx community college engineering students and practicing Latinx engineers influenced students’ confidence, professional identity, networking orientation, and sense of belonging. It also explores how mentors experienced reciprocal identity development. Using an interpretivist qualitative design, semi-structured debrief interviews were conducted with nine participants (seven students and two engineers) involved in a community college STEM pathway program. Data were analyzed using thematic analysis informed by STEM Identity Theory (Carlone & Johnson, 2007) and Social Capital Theory (Bourdieu, 1986; Yosso, 2005). Five themes emerged: identity development through encounter, networking as relational social capital, mentorship authenticity and reciprocity, structural supports for follow-up action, and transformative belonging through representation. Findings suggest that informational interviews function not only as career preparation tools but also as identity-affirming encounters that support STEM identity, belonging, and professional development. This study contributes to STEM education research by conceptualizing informational interviews as scalable, culturally responsive mentoring interventions within equity-oriented STEM pathway programs.
Daniel Newcomb danieln1@vt.edu, Alice Noble ahlee05@vt.edu, Cassie Wallwey cwallwey@vt.edu
Universities' first-year engineering (FYE) programs aid students by helping them develop foundational engineering skills such as programming, problem solving, engineering design, teamwork, etc. and help boost students’ confidence, motivation, identities, and interest / engagement with engineering that they use for the rest of their studies and careers. While specific topics or curriculum vary from program to program, most FYE experiences serve as an "introduction to engineering.” Whether that is through an introduction to a specific engineering discipline or an introduction to engineering as a career, part of the FYE experience involves gaining exposure to and awareness of the different engineering disciplines available to study and earn a degree in. An increasingly popular FYE model is a common "general engineering" course or program that precedes students selecting a specific engineering discipline. This type of FYE experience gives students time in college to make a more informed decision of which of the available engineering disciplines and majors align with their strengths, interests, and aspirations. Another feature considered when comparing first-year engineering courses, programs, seminars, etc. is the duration or length of the FYE program and their contact time with students. Some programs run across a two-semester sequence, delivering a FYE program that lasts a full academic year. Other programs deliver their FYE content in one semester. A year-long FYE experience may allow students more time to learn engineering curriculum, disciplines, and adjust to college life, while a one-semester FYE experience may allow students earlier access to their engineering in-major courses.
This full research paper will explore first-time-in-college (FTIC) engineering students’ major interests (noted at FYE summer orientation), choices (upon completion of the FYE curriculum), and changes (as of March 2026) from Fall 2023 to Spring 2026 to identify patterns of interest, choice, and change across three cohorts of engineering students at a large, public, R1 university. The FYE experience duration is mentioned, as the authors of this paper are affiliated with a university that in Fall 2023 started broadly offering their common first-year engineering program to FTIC students in two forms: a two 2-credit hour class sequence across two semesters or a one 4-credit hour class offered in only Fall semesters (after small 1-section pilots of the 4-credit hour class for FTIC students in Fall 2021 and Fall 2022). This full research paper investigates and compares the academic decisions related to engineering major interest and selection of two populations of engineering students - one population who completed their general engineering coursework by taking two 2-credit hours classes across two semesters, and a second population who completed their general engineering coursework by taking one 4-credit hour class in one semester. The results showcase differences and similarities between these two populations regarding engineering major interest, choices, and changes that occurred across these three cohorts of students, and authors close with elaborating on trends in engineering major selection as well as considerations related to extended (2-semester) or condensed (1-semester) FYE experiences with regards to engineering discipline and major selection.
Laura Cruz Castro cruzcastrol@ufl.edu, Sarah Rajkumari Jayasekaran srajkumari@ufl.edu, Nichole Ramirez nmramirez3@utep.edu, Matilde Sanchez-Pena matildes@buffalo.edu
Student agency has been identified as a key factor in promoting engagement, motivation, and persistence in engineering education. Developing students’ capacity to influence their own learning is particularly important in first-year engineering courses, where students begin forming their identities as engineers and navigating complex learning environments. This study explores first-year engineering students’ perceptions of agency and their suggestions for how courses and technological tools could better support their sense of ownership, voice, and control over their learning. Data were collected through a voluntary questionnaire administered during class in a first-year engineering design course at a R1 university in the southeastern United States. A total of 39 students across two course sections responded to two open-ended questions about potential course changes and technological solutions to enhance student agency.
RQ1: What changes do first-year engineering students believe could be made in a course to help them feel greater ownership, voice, and control over their learning?
RQ2: What technological solutions do first-year engineering students propose that could help engineering students feel more empowered and in control of their learning across their courses?
Responses were analyzed using inductive thematic analysis with descriptive and in vivo coding. The findings of this study indicate that a majority of students either articulated no need for changes or were unable to articulate specific suggestions, suggesting that many first-year students may still be developing awareness of agency within their learning environments. At the same time, students who proposed changes emphasized the importance of increased hands-on, interactive experiences and a need for greater autonomy through choice in projects and learning pathways. As for technology, most students did not advocate for additional tools; rather, those who did highlighted the need for centralized, organized systems that support coordination, clarity, and access to resources.
These findings suggest that agency in first-year engineering courses is both experienced and constrained by how learning environments are structured and by the extent to which agency is explicitly supported. Hands-on, collaborative learning environments may already provide a foundation for agency. At the same time, additional opportunities for choice and clearer, more integrated course systems can further strengthen students’ sense of control in the classroom. Importantly, the results also indicate that students may require scaffolding to recognize and develop their agency. Overall, this study highlights the importance of centering student voice to inform course design and underscores that fostering agency requires not only providing opportunities for action but also supporting students in understanding and exercising those opportunities.
Lorena Benavides Riano lab900@msstate.edu, Mahnas Mohammadi-Aragh jean@ece.msstate.edu
The transition into first-year engineering programs involves not only socio-academic adjustments but also shifts in students’ motivation and goal orientation. Achievement goal theory suggests that students’ orientation toward mastery or performance goals influences how they engage with and respond to academic tasks. While mastery goals emphasize learning and improvement, performance goals focus on demonstrating competence relative to others. When students enter engineering programs, institutions often have limited understanding of students’ initial motivation and how early college experiences may shape their approach to learning compared with high school. In this context, Summer Bridge Programs (SBPs) serve as early interventions that support students’ transition by providing structured, short-term experiences to help them navigate first-year challenges.
This study examines first-year engineering students’ baseline motivation and changes in goal orientation following participation in an SBP at a large public university in the southeastern United States. The study is part of a broader research effort evaluating the effectiveness of SBPs as co-curricular initiatives in supporting the transition to college. Data were collected from two cohorts (n = 91) before and after the program using the Achievement Goal Questionnaire-Revised (AGQ-R). The AGQ-R measures mastery- and performance-oriented goals across approach and avoidance dimensions. A repeated-measures analysis of covariance (RM-ANCOVA) was conducted to assess changes over time while controlling for prior academic preparation (ACT Math). An exploratory motivational profile analysis was also performed to examine shifts in students' dominant goal-orientation patterns.
RM-ANCOVA results indicated no statistically significant changes across the four achievement goal orientations. Mastery-approach goals remained consistently high, indicating that students entered with strong intrinsic motivation, while mastery-avoidance and performance-oriented goals showed small increases. The profile analysis found that mastery-oriented profiles were most common before the program, whereas the post-program assessment showed an increase in mixed performance profiles. Together, these findings suggest that students maintained strong learning-oriented motivation while becoming more aware of academic expectations and the evaluative nature of engineering as they transitioned to higher education.
These results imply that SBPs may not substantially change students' motivational orientations in the short term but may help them navigate the motivational demands of engineering during this critical period. By combining traditional pre-post assessments with exploratory motivational profile analysis, this study contributes to understanding how first-year engineering students' motivation evolves and informs the design of co-curricular programs to better support students.
Jeffrey Carver jeffrey.carver@mail.wvu.edu, Susie Huggins susie.huggins@mail.wvu.edu, Manar Yamany mmy00002@mix.wvu.edu
Students enter first-year engineering programs with varying levels of prior exposure to engineering concepts and practices. While some students encounter engineering ideas for the first time in college, others gain early exposure through extracurricular activities such as robotics clubs, robotics competitions, and classroom-based robotics initiatives. These environments often engage students in engineering-related practices including iterative design, programming, troubleshooting, and collaborative problem solving. Despite the increasing prevalence of robotics programs in K–12 and informal learning environments, limited conceptual work has examined how these experiences may contribute to students’ readiness for engineering education or shape their perceptions of engineering careers.
This conceptual paper addresses the question: What mechanisms connect robotics experiences to students’ engineering identity and career aspirations? Drawing on literature from engineering education, STEM learning environments, and robotics education, the paper proposes a conceptual framework explaining how robotics participation may influence students’ pathways into engineering. The framework suggests that robotics experiences may support students’ development in three interconnected areas: exposure to authentic engineering practices, development of technical and collaborative problem-solving skills, and early formation of engineering identity. Through participation in robotics activities, students may begin to see engineering as both accessible and personally relevant, while also gaining familiarity with the types of practices commonly emphasized in introductory engineering courses.
By positioning robotics participation as a form of early engineering engagement, the proposed framework offers a new perspective on how informal and pre-college learning experiences may contribute to students’ transition into engineering programs. The paper concludes by discussing implications for engineering educators, robotics program designers, and institutions seeking to strengthen pathways into first-year engineering programs through early exposure to engineering practices.
Rui Li rui.li@nyu.edu, Tianyi Liang tianyi.liang@ndus.edu, Yuxin Ren yr2110@nyu.edu
In recent years, Artificial Intelligence AI has drawn significant attention from both the public and professionals across many fields. AI is designed to mimic how humans learn, reason, and create, and it is becoming increasingly capable of producing responses that feel natural and conversational. As this technology continues to improve, it is beginning to change how people work, communicate, and approach complex problems. By reflecting key aspects of human thinking, AI has strong potential to influence many industries. For example, in the legal process, AI is already being used to help review documents, organize information, and support structured reasoning, showing how it can assist in complex, knowledge driven tasks.
This shift is especially important in education and early career development. First year students often face a steep learning curve as they adapt to new expectations in reading, writing, and critical thinking. At the same time, junior professionals such as analysts, associates, and technical staff are expected to handle demanding workloads efficiently. AI can support both groups in meaningful ways. It can help students better understand difficult concepts, organize their ideas, and build confidence in their learning process. For junior professionals, AI can reduce time spent on repetitive tasks, improve efficiency in research and analysis, and allow more focus on higher level thinking and decision making.
To better understand how individuals adopt and use AI tools in these contexts, this study draws on the Technology Acceptance Model. This model suggests that users are more likely to adopt a technology when they perceive it as useful and easy to use. In the context of AI, perceived usefulness reflects how much the tool improves learning outcomes or workplace productivity, while perceived ease of use captures how intuitive and accessible the measured scores of perceived usefulness and perceived ease of use indicate strong technological acceptance of AI tools, particularly within the legal domain. These findings suggest that users recognize both the practical value and usability of AI, which positively influences their attitudes and willingness to integrate such tools into their workflows. There is still limited understanding of how they should be thoughtfully integrated into everyday workflows and training environments. The future work is to explore how AI can be used effectively while maintaining ethical responsibility and human accountability in the legal processes.
Alexander Boback alex@bobacks.org, Sebastian Nowicki, Esther Tian EstherTian@virginia.edu
This Full Paper investigates first-year engineering students’ AI literacy through their evaluation of AI-generated problem statements within a sustainability design context. The study addresses a critical gap in AI literacy research by examining demonstrated competencies through applied tasks rather than self-reported abilities.
As part of our institution’s first-year engineering curriculum, students complete a design project addressing a sustainability challenge around campus. As part of the project, students complete a scaffolded assignment in which they: (1) develop a problem statement without AI assistance, (2) use Microsoft Copilot to generate a problem statement for the same problem, and (3) systematically evaluate the AI output through guided prompts addressing factual accuracy, structural quality, and comparative strengths/weaknesses. This study analyzes submissions from consenting students across fall 2024 and fall 2025 semesters.
Prior research on AI literacy has predominantly relied on Likert-style survey instruments to measure student competencies [1], which capture perceived rather than demonstrated abilities. In contrast, this study evaluates students’ applied AI literacy by analyzing their evaluations of AI-generated content. The assignment structure specifically engages Long and Magerko’s “What can AI do?” competency domain [2], requiring students to recognize AI’s capabilities and limitations, assess output quality, and consider implications for engineering practice.
This research addresses two key research questions: (1) To what extent do students accurately evaluate AI-generated problem statements? (2) How do students conceptualize the relationship between their own work and AI capabilities, and what perspectives do they articulate about AI’s role in engineering design?
The analysis employs a mixed-methods approach. Researchers and students evaluated the same AI-generated problem statements assessing four critical components: context, specific problem, current state, and ideal state. Match scores quantify agreement between student and researcher assessments, providing a measure of evaluation accuracy. Thematic analysis identifies patterns in how students compare human and AI work and conceptualize AI’s role in engineering design.
Preliminary findings indicate students demonstrate strong evaluation accuracy for three of four structural components (context, specific problem, and ideal state), with notably lower accuracy for assessing current state in AI-generated statements. Furthermore, student reflections consistently indicated that their own problem statements were stronger than AI-generated ones, emphasizing that humans should still play a central role in developing problem statements. This work contributes to engineering education by (1) demonstrating an artifact-based assessment approach for AI literacy that moves beyond self-report measures, (2) providing empirical evidence of first-year students’ evaluation capabilities when analyzing AI-generated technical content, and (3) offering an example of how scaffolded reflection activities can develop AI evaluation skills in engineering design courses.
Brian Chan brianchan1@vt.edu, Mark Huerta mvhuerta@asu.edu
First-year engineering students face a well-documented convergence of stressors that threaten both their well-being and retention. Despite growing recognition of this challenge, few instructors have practical, evidence-informed tools for supporting student mental health and well-being within existing course structures. Previous studies primarily describe instructors’ experiences integrating mindfulness into engineering classrooms and report student perceptions of these practices; however, they tend to focus on individual course contexts and provide limited guidance for implementation across first-year engineering settings. Building on our critical evaluation of a nine-session in-class mindfulness-based intervention (MBI) involving 100 students in a first-year foundations of engineering course at a large R1 university, this paper presents a practical implementation framework for integrating MBIs into engineering classrooms. Drawing on quantitative and qualitative findings, participation patterns, and implementation lessons from the source study, the framework organizes key considerations into four pillars: (1) Sequencing, which describes how to progressively introduce meditation types; (2) Framing, which addresses how to present mindfulness to students socialized into engineering’s rational-emotional culture; (3) Logistics, which provides guidance on facilitation, opt-out policies, and session structure; and (4) Reflection, which captures lessons learned from implementation. This paper emphasizes implementation considerations rather than intervention effectiveness evaluation, offering instructors a structured approach for adopting and adapting MBIs within existing course environments. The paper also discusses AI-adaptive delivery as a future research direction, considering how student response data could inform more personalized mindfulness practices while highlighting ethical considerations for future development. The framework is intended as a practical guide for instructors and a foundation for future research at the intersection of mindfulness, AI, and first-year engineering education.
MONDAY, AUGUST 3rd | 5:00 - 6:00 PM
Panel: Real Talk About AI Usage: Student Experiences in Engineering 50
Moderator: Kevin Calabro (Director, Keystone Program) kcalabro@umd.edu
Panelist: Sebastian Nowicki zrg4jh@virginia.edu, Alex Kult akult@nd.edu, Terrence Pierce tpierce2@terpmail.umd.edu, Matthew Patrick Paul mpaul125@umd.edu, Abdul Azeez Shaik as4768@njit.edu
Artificial intelligence is rapidly changing how students learn, solve problems, and prepare for careers in engineering. In this interactive student panel, current engineering students will share their experiences using AI tools in coursework, research, design projects, and everyday academic life. Panelists will discuss both the opportunities and challenges of AI, including how they determine when AI is helpful, how they verify AI-generated information, and how they balance efficiency with authentic learning.
This session will provide practical insights into students' perception of responsible and effective AI use at the university level and in the context of their first-year engineering experience. Attendees will have the opportunity to ask questions, hear diverse perspectives, and explore strategies for using AI as a tool for learning, creativity, and professional growth while maintaining academic integrity. Whether you are new to AI or already experimenting with these technologies, this discussion will help you navigate the evolving role of AI in engineering education.
TUESDAY, AUGUST 4TH | 8:30 - 9:30 AM
GIFTS: Leveraging Embedded Therapy Services to Support First-Year Engineering Students 52
GIFTS: Enhancing First-Year Engineering Student Success through Integrated Wellness Initiatives. 53
GIFTS: A First-Year Engineering Module on the Technical Foundations of LLMs and AI 54
GIFTS: Balancing History, Budget, and Pedagogy: Renovating a First-Year Engineering Classroom 55
GIFT: A Tool to Help Students Become Aware of their Community Cultural Wealth 56
GIFTS: Enhancing Programming Preparedness Through an Arduino and MicroPython Based Simulator 57
GIFTS: Learning-Impact Observations from a Multimodal Approach to Troubleshooting Robotic Systems 59
GIFTS: Learning-Impact Observations from Reassessments of Major Assignments 60
Melissa Bottiglio Melissa.Bottiglio@Colorado.edu, Colleen Ehrnstrom colleen.ehrnstrom@colorado.edu, Audrey Gilfillan arblankenheim@gmail.com, Alison West alwe3979@colorado.edu
Over the past several years, universities have experienced a rise in mental health concerns. In response, many university counseling centers have adopted embedded therapy models, where select mental health therapists are embedded within specific colleges and schools to integrate mental health programming into the academic setting. These programs aim to provide a community mental health model that addresses systemic and cultural barriers to student mental health, offer a myriad of mental health interventions to students beyond the traditional model of individual therapy, and help to evolve the campus culture around supporting mental wellness. Embedded therapists work not only with students, but also with staff and faculty, to optimize student academic outcomes by addressing mental health challenges that often interfere with academic performance (e.g., anxiety, lack of motivation, chronic stress and burnout, perfectionism, etc.).
In this GIFTS, presenters will share an overview of their partnership with an engineering college and discuss how their close collaboration with this community has allowed them to customize their approach to effectively meet the unique needs of these students. They will describe specific programs that have resulted from these collaborations and will review utilization data. Furthermore, presenters will discuss the results of a qualitative assessment, summarizing engineering staff and faculty’s perception of the embedded program at the institution.
Presenters will provide recommendations for how attendees can partner with their university counseling center. Collaborations with embedded providers can help create and enhance first-year programming – contributing to a campus culture that prioritizes student wellbeing alongside academic rigor.
Grace Gangitano grazia.gangitano@njit.edu
The rigorous academic demands of Science, Technology, Engineering, and Mathematics (STEM) disciplines, characterized by high credit loads and competitive grading, foster a unique "STEM stress" environment that contributes to high attrition rates. This proposal advocates for a holistic wellness framework specifically designed to improve the transition and retention of undergraduate STEM students by addressing psychological, social, and physical well-being. Key interventions include the implementation of growth-mindset workshops to combat imposter syndrome, the establishment of wellness-focused living-learning communities, and the integration of embedded counseling within academic departments. By shifting the culture from one of "weeding out" to one of comprehensive support, institutions can stabilize GPA performance and increase retention. Ultimately, this GIFTS explains that an integrated approach posits that student wellness is not a secondary concern but a fundamental prerequisite for academic excellence, retention, and the development of a resilient technical workforce.
Malayna Leopold malaynaleopold@gmail.com, Charalambos Marangos cm00@lehigh.edu, Evan Mazor edm527@lehigh.edu, Eric Obeysekare ero324@lehigh.edu
With the rapid spread of Artificial Intelligence (AI), work, society, and higher education are experiencing rapid transformation and disruption. To meet this moment, many universities are introducing classes and modules on “prompt engineering”, “effective and responsible use of AI”, and other similar topics. Yet many such modules largely leave AI as a “black box” that takes input and produces output leaving students with little understanding of how such systems actually work and often inaccurate mental models of the strengths and weaknesses of AI. This presents an opportunity for engineering education: can we peel back the layers of the AI black box, dive deep on the technical details, and help students view AI as another tool in the tool box allowing them to form more critical opinions and stronger mental models of AI’s strengths, weaknesses, and potential.
This GIFTS paper describes a module designed to build first-year engineering students’ technical understanding of Large Language Models (LLMs) and Generative AI, develop their skills with Python, and to harness LLMs not as a magic homework machine but as an engineering tool with strengths and weaknesses. The module took place over a 7-week period with the class meeting three times a week for a total of 7 hours per week and employed team- and project-based learning.
Assuming that they entered the course with zero programming experience, students were introduced to basic Python programming concepts in this module while exploring the inner workings and applications of AI. This was not a “vibe coding” module and students did learn and write Python code by hand themselves as they built their skills. A key unit in the module introduced the concept of tokenization that LLMs use to process input and generate output; a concept that also lent itself to introduce programming concepts including lists, dictionaries, loops, and more as students built several versions of tokenizers themselves and learned how LLMs use tokens, vectors, and matrices in a neural network to generate output. Other concepts related to LLMs like calculating the environmental impact of their use were utilized to cover other introductory programming and computational techniques.
In parallel, students also iteratively built chatbots that interfaced with the Claude LLM application programming interface (API) showing them that LLMs can be used as another tool to create more complex engineering systems. Writing custom tools and functions showed students how they can extend the functionality of LLMs beyond generating text and answering simple questions. This culminated in a final project for the module that saw pairs of students spend several weeks building their own AI Agents, designed to solve a problem of their choice. Students brought these ideas to life– providing them with first-hand experience individually leading engineering design projects. Overall, we are excited to introduce a module that explores the technical details behind LLMs and their potential as tools for engineers.
Aysa Galbraith agalbrai@uark.edu, Leslie Massey lbmassey@uark.edu, Latisha Puckett lpucket@uark.edu, Heath Schluterman hschlut@uark.edu, Gretchen Scroggin gms013@uark.edu
The physical design of a classroom can significantly influence how students interact, collaborate, and engage with course content and their instructors. Renovating classroom spaces to support a more contemporary pedagogical view can be quite difficult, especially if one is in a historical building or faces financial constraints. In 2024, the First-Year Engineering Program (FEP) at REDACTED renovated its classroom spaces to better align the learning environment with its teaching philosophy and instructional needs. Our renovation efforts were guided by the desire to design a classroom environment that is safe and welcoming to all, seamlessly integrates technology, promotes teamwork and collaboration, and embodies FEP’s emphasis on active learning.
While the renovation aimed to create a more student-centered classroom, the process came with some challenges. The FEP is centered in a building that was constructed in 1927 and has historical significance to our campus. Additionally, the project had to be completed within a limited budget and timeframe and could not involve the dismantling of existing walls and structural components. Balancing these constraints while still achieving the desired instructional outcomes necessitated thoughtful decision-making regarding furniture and technology.
This paper will describe the 2024 renovation along with selection of classroom materials and furnishing, and the rationale behind our decision. It will also examine how the new and improved space is currently being used to support the instructional practices of FEP. Particular attention will be given to how the new classroom configuration facilitates collaboration, teamwork, and active participation among students. By reflecting on both the challenges and successes of the renovation process, this paper aims to provide practical insights for other institutions seeking to modernize instructional spaces within similarly constrained conditions.
The preparation of this work the author(s) used Microsoft Copilot and ChatGPT in order to assist with generating the title and to provide clarification and grammatical revisions of the written content. After using this tool/service, the author(s) reviewed and edited content as needed and assumes responsibility for publication content.
Lucas Galey ljgaley2@utep.edu, Meagan Kendall mvaughan@utep.edu, Nichole Ramirez nmramirez3@utep.edu
Great Ideas for Teaching (and Talking with) Students (GIFTS): When students first begin their college experience, they are unlikely to fully grasp the extent and impact of their current level of experience. Namely, their understanding of experience is often limited to formal positions held or proficiency in a narrow set of skills. One of the objectives of this course was to increase their sense of belonging in engineering, connecting their prior experience with engineering professional practice. To address this, we developed a tool to help students build confidence in articulating their experiences in an engineering context, enabling first-semester students to participate effectively in career fairs and internship searches. While it is clear that students will gain extensive experience throughout the course of their studies, most also have a wealth of developed cultural experience that they are unaware they can leverage. Using the Community Cultural Wealth framework, our objective was to make students aware of this experience and help them leverage it into effective narratives and resume highlights.
To accomplish this goal, we developed an asset worksheet that helped students more accurately identify their gained cultural experiences, even outside of engineering. The worksheet broke experiences down into the following categories that students could more readily relate to: academics, professional, aspirations, familia, communication, social, navigational, and resistance. This worksheet has been administered to approximately 700 students across 24 sections since Fall 2024 in the first month of a first-year engineering design experience course.
This GIFTS paper will share the motivation behind this approach, a detailed description of delivery, and anecdotal information about the success seen in first-year students. The handout represents a simple intervention that appears to improve both resume development and student narrative.
Margaret Crooks maggie.crooks5@gmail.com, Forrest Milner forrestmilner@gmail.com, Matthew Paul matpaul321@gmail.com
The transition toward modern embedded programming environments in first-year engineering courses requires instructional structures that support both technical readiness and equitable team participation. Introduction to Engineering Design (ENES100) at the University of Maryland enrolls approximately 800 students per semester across 15 to 20 sections, with teams of about eight students designing and building an autonomous over-the-terrain vehicle (OTV). Historically, teams designate one member with prior programming experience to lead software development. However, because OTV fabrication and development require several weeks to complete, designated programmers often lack functional hardware on which to test navigation logic, resulting in idle time, unvalidated pseudocode, late-semester software bottlenecks, and uneven workload distribution. To address this challenge a public browser-based Arduino and MicroPython OTV simulator was developed as a self-directed preparatory tool. The simulator preserves the ENES100 programming command structure while implementing Arduino or MicroPython syntax, allowing students to develop and validate navigation logic prior to hardware completion. The platform models a mock OTV within the standard physical testing environment within the classroom and supports ultrasonic sensing, WiFi-based location communication, differential drive motor control, obstacle interaction, and console debugging. Ultimately, the code developed in the simulator transfers to the physical OTV with minimal modification. Preliminary instructional observations indicate earlier functional navigation demonstrations, increased engagement of designated programmers during early project phases, and improved workload distribution within teams. By decoupling software development from hardware readiness, this approach enhances programming preparedness while supporting equitable participation in large-scale first-year project-based engineering courses.
David Gutierrez dg3ty@virginia.edu, Jesse Pappas pappas@virginia.edu
Introducing first-year students to engineering is not only a matter of teaching foundational skills in design, analysis, and problem solving. It is also an opportunity to support the longer process of becoming engineers—developing the professional values, habits of mind, and identity-level capacities that shape how engineers think, collaborate, and respond to challenges. Character development—the cultivation of these identity-level capacities—is an important but often implicit part of this process. Yet in early engineering education, conversations about character can feel abstract, overly moralized, or disconnected from the technical realities of engineering practice. As a result, students may struggle to recognize how aspects of their character influence engineering behaviors such as persistence, collaboration, decision-making, and iteration under uncertainty.
This GIFTS paper presents a discussion-based activity that makes character development concrete for first-year engineering students by connecting it directly to engineering design practice. Pairing a shared viewing of October Sky [2] with early design experiences, the activity introduces character constraints: consequential patterns of self-expression that may limit the design process in ways similar to traditional design constraints and that can be intentionally improved to enhance individual and team outcomes. The activity helps students recognize deeply human aspects of engineering practice that require deliberate (rather than default) responses to uncertainty, complexity, ambiguity, and failure.
More broadly, the activity aims to support early engineering identity development by helping students see character not as a fixed trait or abstract ideal, but as effective self-expression that can be achieved through practice, collaboration, and experience.
Terrence Pierce tpierce2@terpmail.umd.edu
In this GIFTS paper, we present observations developed over six semesters of teaching the same highly interdisciplinary first-year engineering design course requiring consistent troubleshooting of robotic systems. Troubleshooting is ubiquitous in mechatronic and robotic systems, occurring at both a component and an integrated system level. While critical thinking and problem-solving are emphasized throughout engineering, the number of possible faults grows rapidly with every part added, and the resulting complexity can quickly outpace the methods available to a first-year student. In practice, students tend to seek out faculty or staff who have likely observed similar problems before and can efficiently narrow the set of likely causes. This is effective in the moment, but one might wonder whether it equips students to tackle future problems on their own, without a staff member to guide them. We first analyze common methods of troubleshooting robotic systems, noting that common subsystems are electronic, mechanical, and software-based. Because issues at both the unit and integrated levels usually stem from a detectable failure in one of these subsystems, teaching high-level detection strategies can let students quickly isolate where a fault is occurring, even before they fully understand the subsystem itself. We examine the effectiveness of these various approaches, addressing the challenges of providing students with tools while they may not fully grasp the fundamentals of the respective subsystems, and provide suggestions for implementing these strategies in similar class environments. We also note the fast-paced evolution of technology, especially in recent years on the software side, and observe the challenges associated with newer troubleshooting techniques involving generative AI in comparison with more traditional approaches. In this GIFTS paper, we explore practical robotic troubleshooting techniques from a pedagogical perspective tailored to the unique background of a first-year engineering student. It is critical that students learn the value of efficient troubleshooting without creating barriers to the exploration that is typical of a first-year engineering design course.
Terrence Pierce tpierce2@terpmail.umd.edu
In this GIFTS paper, we present observations gathered over six semesters of teaching the same first-year engineering design course under different faculty members and pedagogical philosophies surrounding assignment reassessment. While research has demonstrated the effectiveness of reassessment for the learning process, we note additional dimensions through which it benefits an interdisciplinary first-year engineering design course. Although the field of engineering has grown more accessible to enter, challenges remain, and reassessment can act as an equitable approach that encourages learning without penalizing students for a lack of initial knowledge. Reassessment also aids the learning process by providing an additional round of instructor-student interaction in which feedback can be delivered. Furthermore, it reduces reliance on outcome-oriented motivational strategies, such as the overall class grade, since students have multiple chances to obtain a positive outcome, and instead increases emphasis on mastery-based learning, as learning itself becomes a stronger motivational factor. Given how many success metrics rely on outcome-oriented measures, it is unsurprising that students place such emphasis on factors like GPA. However, mastery-based approaches often lead to better understanding and retention, as well as a healthier relationship with learning, and thus improved long-term success. Exposing students in their first year to these learning-motivated approaches may therefore prove critical for the rest of their collegiate and post-collegiate careers. Additionally, the feedback provided during reassessment can generate cascading improvements, as students learn what they can do better both from a metacognitive perspective and from a technical perspective on the task at hand. When providing specific technical feedback, however, especially for an activity students have seen before, it is critical that they do not overfit to the specific assignment rather than learn the general approaches needed for similar future tasks. This GIFTS paper expands upon each of these benefits of reassessment methodology and provides suggestions for implementation in similar first-year engineering design courses.
Andrea David andavid@ucsc.edu, Tela Favaloro tela@soe.ucsc.edu, Ian Phan iahphan@ucsc.edu
Traditional classroom participation often privileges visible verbal contributions, overlooking the many other ways students meaningfully engage with course content, peers, and instructors. As such, we introduce the Participation Passport, a low-stakes reflection tool designed to broaden how participation is recognized by encouraging students to identify and reflect on diverse forms of engagement after each class session. Grounded in principles of Universal Design for Learning and informed by student perspectives, the tool reframes participation as a multimodal, student-centered process that supports metacognition while providing instructors with greater insight into classroom engagement.
The Participation Passport was implemented across first-year design-build courses to explore how structured reflection legitimizes less visible forms of participation. Findings suggest that the tool helped uncover a wider range of meaningful participation practices, broadened how participation was understood by both students and instructors, and encouraged metacognitive reflection and growth. By shifting participation from the traditional, narrow measure of classroom speaking toward a broader understanding of engagement practices, the Participation Passport offers a practical framework for fostering more equitable, reflective, and collaborative learning communities.
Shanna Daly srdaly@umich.edu, Philip Derbesy pderbesy@gmail.com, Charlie Michaels cmic@umich.edu, Steve Skerlos skerlos@umich.edu
Engineering is inherently sociotechnical, yet first-year engineering courses often separate social and technical aspects of engineering, unintentionally signaling that social considerations are separate and secondary. This paper presents the design and early implementation of a course that integrates these dimensions through a project on adaptive video game controllers for users with physical disabilities. Grounded in the Socially Engaged Design Process Model, the course embeds stakeholder engagement, context, and iteration throughout technical activities. Students connect with individuals with lived experience and align technical decisions to user needs through integrated design and communication assignments. We present the course not as a single implementation but as a transferable model for fully integrated first-year sociotechnical design, supported by an institutional infrastructure of open-source instructional modules and a shared process framework that other programs can adapt to their own first-year contexts.
Yin-ping Chang ychang@oakland.edu, Zhijun Wu wu@oakland.edu
Engineering visualization and Computer-Aided Design (CAD) are foundational skills for engineering students, enabling them to translate conceptual ideas into digital models and communicate technical designs effectively.
This paper presents the instructional framework used to integrate CAD learning within the first-year design course. The course is designed to support the development of spatial reasoning, design communication, and engineering problem-solving skills early in students’ undergraduate academic path. The course combines guided lab tutorials, supportive homework assignments, and a team-based design project to introduce CAD and build student modeling skills throughout the semester. This study also explores how rapidly evolving Artificial Intelligence (AI) tools could be integrated into this early engineering design course to enhance idea generation, information access, and design exploration.
TUESDAY, AUGUST 4TH | 9:45 - 11:00 AM
Full Paper: Assessing Evidence of Metacognition in Formative Lecture Quizzes 66
Full Paper: A Tale of Two Teams' Perspectives of Conflict in a First-Year Design Course 69
Full Paper: An Arguable Point: Team Conflict and Individual Team Member Grade Performance 70
Christine Alexander cealex@umd.edu, J. Hylton j-hylton@onu.edu, Rodica Ivan rivan@acuityinsights.com, Gabriel Sitarenios gsitarenios@acuityinsights.com
This study evaluates the effectiveness of a formative Situational Judgment Test (SJT) designed to measure, develop, and longitudinally track personal and professional competencies in engineering students. The Professional Skills Development (PSD) tool presents authentic, real-world dilemmas requiring open-ended responses. Dilemmas were presented as either text scenarios (emails, news articles, etc.) or as video scenarios rendered using AI avatars. Because the questions are open-ended and do not prescribe a single correct answer, scoring emphasizes the quality of students’ rationale, enabling assessment of higher-order judgment and applying professional skills rather than factual recall.
PSD measures competencies across four core dimensions: Interpersonal Skills, Intrapersonal Skills, Social & Ethical Responsibility, and Critical Thinking. These dimensions were developed to align with engineering competency frameworks (e.g., ABET, CEAB) and parallel frameworks in Business, Law, and Medicine, supporting both discipline-specific and cross-professional relevance.
A total of 146 Engineering students from two US institutions (Program A, N = 73; Program B, N = 63) completed PSD at two time points approximately two months apart. The sample included both first-year and fourth-year students. We tested three hypotheses: (1) scores would improve at retest due to coursework exposure and targeted developmental feedback; (2) fourth-year students would outperform first-year students due to greater curricular experience; and (3) first-year students would demonstrate steeper growth trajectories given lower baseline scores. As another area of investigation, we also gathered feedback from students and their reactions to the overall experience as well as the use of the AI avatars in the video scenarios. Across the two sittings, the test content included 18 text-based scenarios and 12 video scenarios.
Results indicated overall score improvement across administrations, though patterns differed by institution. For Program A, significant gains were observed in Intrapersonal Skills (Cohen’s D = .84, p < .001) alongside a decline in Interpersonal Skills (Cohen’s D = -.59, p < .001). For Program B, improvement occurred across all dimensions, with the largest gains in Critical Thinking (Cohen’s D = .36, p < .01) and Interpersonal Skills (Cohen’s D = .44, p < .001). As predicted, fourth-year students scored higher than first-year students at baseline, consistent with cumulative competency development across the curriculum. Cohen’s D values ranged from .14 to .47, although significant differences were only observed for Intrapersonal Skills, and Social and Ethical Responsibility (both p < .05). However, contrary to expectations, first- and fourth-year students demonstrated comparable magnitudes of improvement over time.
Implementation challenges included ensuring sufficient student engagement to promote valid responding, integrating the assessment within existing curricular time constraints, and identifying an optimal retesting cadence aligned with program milestones. We found that students appreciated the specificity and clarity of the personalized feedback, and found the resources and exercises helpful overall. At the same time, students expressed an overall negative sentiment towards the uncanniness of the AI avatars, and preferred the text scenarios to the video scenarios. Despite these challenges, findings support PSD as a promising formative tool for objectively assessing and tracking professional competencies while simultaneously supporting student development through structured feedback and targeted learning resources.
Kathleen Harper kathleen.harper@case.edu, Cemantha Johnson cml147@case.edu
The first-year engineering experience at X University aims to introduce students to a variety of engineering disciplines and to hone their skills in foundational concepts like programming, design, and teamwork. This one-semester course involves two laboratory meetings and one lecture each week, with new programming concepts primarily being introduced during the lecture meetings. The instructional team introduced weekly formative lecture quizzes during the 2023-24 school year with the goals of improving lecture attendance, enhancing engagement with guest lecturers, and promoting metacognitive skills like self-testing and reflection. This exercise has since undergone iterative improvements to best ensure that it is meeting these goals. Here, we present the results of an analysis from the 2025-26 school year assessing whether student responses on these quizzes reflect evidence of metacognitive engagement. Additionally, we present an exploration of how these responses relate to performance on subsequent higher-stakes course assessments.
As discussed previously, these formative quizzes have undergone several modifications. The one-page quizzes have space for students to respond to a few short questions based on the material from the previous week’s lecture, which they respond to before class begins. There is also space for students to answer the same questions during an in-class discussion of the quiz, giving them the opportunity to make corrections to their initial responses. For the 2025-26 school year, the instructors added a question asking students to identify topics that they should further review to strengthen their understanding. This was done to better scaffold metacognitive strategies for students throughout the semester.
Student responses from the Fall 2025 semester showed evidence of students initially making mistakes, correcting errors during the discussion, and writing notes to themselves. When assessing student responses to the “topics for review” question, most students identified at least one topic for further review. Many of these topics were semi- or well-aligned with actual student mistakes and relevant quiz topics. Additionally, several responses exhibited other markers of metacognitive awareness and planning, including expressions of confidence (or lack thereof), connections to outside topics, and specific action steps. Ongoing analysis is assessing if and how student responses on a formative quiz focused on MATLAB functions correlate to performance on subsequent assessments, including a higher-stakes quiz and the final exam. Initial observation suggests that how students engaged with the formative lecture quiz did impact their performance on the related summative quiz; the results of the completed analysis will be included in the full paper.
Andrew Bartolini abartoli@nd.edu, Joseph Lyon jlyon2@nd.edu
One of the biggest challenges with using a flipped classroom is ensuring that the content covered before class through preparatory work is sufficiently engaged with, such that learners are ready from the moment they enter the class. Unfortunately, in previous years, the authors have observed that many students do not engage with the material before class and ultimately arrive unprepared or don't show up at all despite online quizzes due before class.
As a result, the authors implemented a daily low-stakes start-of-class assessment that students must be prepared to engage with the minute they walk into the classroom. The assessment is structured to create a low-stakes environment, with an attendance component and the ability to answer one question incorrectly without penalty.
After the addition of start-of-class quizzes, the number of unexcused absences compared to the previous semester, which had pre-class online quizzes, dropped by 50%. This reduction was particularly notable for sections that met during the first period of the day.
Previously, homework scores were used to predict potentially non-thriving students before exams. However, with the prevalence of generative artificial intelligence, the instructional team raised concerns about the homework's predictive abilities. The start-of-class quiz scores also showed a stronger correlation with exam scores than with homework scores, suggesting they may be a better predictor of students who are not thriving. Furthermore, an odds ratio analysis was conducted and confirmed that the start-of-class quizzes were better predictors of non-thriving students early in the semester than homework assignments.
Finally, the start-of-class quizzes were positively received by both instructors and students at the end of the semester. The quizzes led to better note-taking, stronger exam simulation skills, and more effective self-checking of knowledge. The instructors plan to implement these start-of-class quizzes in the technical content of the university's first-year, design-based engineering course as well.
Whitney Gaskins whitney.gaskins@uc.edu, Mark Onyango onyangmo@mail.uc.edu
This full paper presents how Black and Latiné (BL) undergraduate engineering students utilize structured reflection to not only enhance meaning-making but also overcome challenges as they participate in an undergraduate research experience (URE) program. Experiential learning (EL) theory emphasizes that learning is not produced by experience alone, but by the learner’s ability to critically reflect on that experience. This experience includes instances where learners encounter challenges. For example, students often navigate unfamiliar research environments, particularly while assessing their thoughts, behaviors, and beliefs as they move through authentic inquiry in a URE setting. Within engineering education, structured reflection has emerged as a critical mechanism through which students transform hands-on activities into meaningful learning and skill development. Participants for this study were four BL undergraduate engineering students sampled from the summer 2025 Louis Stokes Alliance for Minority Participation (LSAMP) URE cohort at a large R1 state university in the Midwestern United States. Thematic analyses of participants’ responses to reflective prompts show that by engaging in structured reflection, participants examined their experiences and interactions with mentors, revealing how they employed two key strategies, (1) shared goals and (2) validation. Regarding shared goals, participants critically evaluated their values and aspirations, identifying how they aligned with their mentors' advice and guidance. Additionally, participants reflected on how their mentors' encouragement and affirmation influenced their confidence and motivation, shedding light on the role of validation in shaping their engineering career pathways.
Marcia Gail Headley gheadley@udel.edu, Pamela Lottero-Perdue plottero@towson.edu, Haritha Malladi malladi@udel.edu
This paper presents perspectives of conflict from student team members and their near-peer mentors (PMs) in two different teams within a first-year engineering course. Team-based design projects are widely used in introductory engineering courses. As novice collaborators experiencing their first college-level design project, students in these teams are often challenged with issues stemming from interpersonal conflict with teammates. To support students in collaborating effectively, it is important for instructors to identify and help mitigate these team conflicts. Monitoring, diagnosis, and interventions to mitigate student team conflicts can be especially difficult when the student-to-instructor ratio is high, as is often the case with large-enrollment introductory engineering courses.
This research study is set in a first-semester introductory engineering course taken by approximately 700 students at a large public R1 university. Students in this course are assigned to interdisciplinary teams of around five members to work on a semester-long design project. The course instructor (first author) supervises a team of engineering undergraduates who have previously completed the course to serve as PMs for students in the course. Each PM is assigned to mentor around five student teams by facilitating required discussion sections, grading formative assignments, reviewing CATME results, and answering student questions.
In this paper, we characterize student team conflicts in two teams from one semester of this course using three data collection mechanisms: within-semester peer evaluation surveys (completed by students) and two different end-of-semester surveys (each completed by students and PMs). Within the semester, students were required to complete multiple peer evaluation surveys using the widely used Comprehensive Assessment of Team Member Effectiveness (CATME) tool. We developed two novel survey instruments—the Student Team Reflection (STR) survey and the Peer Mentor Observation (PMO) survey. The STR survey collects data from students regarding their experiences with the incidence, severity, and reporting of conflict within their team. The PMO survey collects data from the PMs to capture their impressions of team conflicts within the teams that they mentored. Both surveys contain a mixture of multiple-choice, Likert scale, and open-response questions. These surveys were distributed via Qualtrics at the end of the semester after all student teams had submitted their final project report.
The two teams were picked based on differences in student and PM ratings of team conflict severity. Overall, one team (Improved Team) ameliorated their conflict by the end of the semester, while the other (Stagnant Team) stagnated with respect to their conflict. This paper presents a unique mixed methods analysis of the CATME, STR, and PMO data to elaborate on the viewpoints and perspectives held by students and PMs on the sources of team conflict. It also identifies similarities and differences between these two teams.
Benjamin Chambers bdc0112@vt.edu, Natalie Van Tyne nvantyne@vt.edu
Since project-based introductory engineering courses on the college level often involve teamwork in assigned teams, many students become concerned about their course grades that will depend on the outcome of teamwork when they do not have full control over the team’s operations or deliverables. These students may approach their involuntary assignment to an engineering design team with apprehension: Will we get along well enough to get the work done? How will working on this team affect my final grade in the course? Will I be stuck with doing all of the work because I’ll be the only one who cares about it?
When educators are concerned about how assigned teams will operate in order to complete a project successfully, one way to monitor teams operations covertly is to detect which teams are having difficulties due to conflict within the team, as measured by team and peer evaluation surveys. We used this method to measure team members’ perceptions of conflict and whether these perceptions were related to final grades on both an individual and a team basis. This led us to ask:
How do team member perceptions of team-based conflict relate to team-based and individual course grades in a first-year engineering design course?
After we compiled the individual and team-based grades for all team members across twelve sections of the same course and instructor over three semesters, we measured individual team members’ responses to prompts about three types of conflict in their teams with quantitative and qualitative peer evaluation survey questions in the CATME® BARS survey, namely task, relationship, and process conflict. Task conflict questions addressed how team members disagreed about how to approach the project in general, receptiveness to ideas among the members, and which tasks to complete and by whom. Relationship conflict questions probed for disagreements with fellow team members, incidents of anger, and emotional conflict resulting in a sense of isolation and/or disillusionment with the team. Process conflict questions dealt with how to do the work as opposed to what work should be done.
We performed Spearman correlations with Holm correction for individual and team grades, against conflict subscales and overall means. We found no significant correlation between any conflict scale or grade level (all adjusted p values > 0.05). This is in contrast with studies in other contexts, which have shown process and relationship conflict to predict lower performance, and task conflict to predict higher performance. This suggests that there may be further contextual and course design factors to explore which can mitigate the grade impacts of conflict in teams.
Michael Butler mxb672@case.edu, Sarah Dallas sed123@case.edu, Claire Dorsett dorsett.claire@gmail.com, Kurt Rhoads krr38@case.edu, Daniel Smith dcs93@case.edu
The First-Year Engineering program and Makerspace at X university created a two joint projects with the goals of increasing student participation, expanding the options for student innovation, and increasing the opportunities to utilize the campus maker space.
The previous design module asked students to design and manufacture “whegs”, or wheel-legs that are attached to remote controlled cars and act like wheels on flat ground but have spokes for climbing obstacles. In teams of four, students worked over six weeks to find a biological inspiration for their designs, test prototypes, sketch their designs in SolidWorks, 3D print, and make modifications. Students competed to traverse an obstacle course using their final whegs. While students rated this module as their favorite, there was often unequal participation among group members because most of the design work can be completed by one or two individuals.
We designed a new module that asks students to design and manufacture a ring collector that collects rings along the obstacle course. The ring collector attaches to the car chassis, above the whegs, and can be made from cardboard, plywood, or acrylic. Students cannot 3D print the ring collector, but instead must use an additional manufacturing method available at the First-Year Engineering Lab or maker space, such as laser cutting, traditional woodworking, or milling.
In another project, students designed and manufactured light boxes. Students are provided LED lights with batteries and switches and asked to make an object to surround the light using at least one of the following techniques: 3D printing, laser cutting, vinyl cutting, sewing/embroidery.
Adding the new component increased individual participation on teams, increased the number of manufacturing techniques used by students, and added an exciting challenge during the obstacle course. Total maker space visits from first-year engineering students increased from an average of 2.3 times during the second month of the semester to 3.6 after adoption of the light box project.
After the introduction of the light box project, laser cutter usage for the whegs project increased from 21% of students in Spring 2024 to 36% in Fall 2025. Forty-four percent of students visited the makerspace to make their ring collector in Spring 2024 vs. sixty-two percent in Fall 2025.
Suzanne Keilson skeilson@loyola.edu
Engineering students often encounter sensors and measurement systems only after substantial coursework. Early experiences with engineering frequently emphasize calculation rather than investigation of real devices and the interpretation of measurement signals. The movement from physical concepts and insights to engineering habits of mind is often kept implicit. This paper describes a first‑year engineering activity in which students discover and analyze rotary encoders in a common object, the computer mouse.
In the activity, students disassemble computer mice identifying components and how mechanical motion is transformed into digital pulse signals via the rotary encoder. Students learn to interpret pulse sequences from two phase‑shifted signals (A and B) as a code, construct timing diagrams, and relate pulse counts to physical distance traveled by the mouse. They also see how the same physical principles get transferred into the design of the optical mouse (diffraction) and can be used more generally for relative position and distance (or length) measurement. (as in automated manufacturing). Rather than focusing solely on correct numerical results, the activity emphasizes reasoning and interpretation of signal sequences. Students confront practical questions such as encoder resolution, missed pulses, and direction detection.
We describe the structure of the lesson and supporting activities and materials in full. The exercise demonstrates how familiar consumer technology can provide an accessible entry point to fundamental concepts in sensing, measurement, and the repeated use of fundamental physical principles to solve an entire category of engineering aims (knowing relative and absolute position, distance, length). This represents a pedagogical refinement of the way these topics may have been introduced in the past and a strengthening of the connections between physics and engineering as well as measurement and real time digital signals and codes.
TUESDAY, AUGUST 4TH | 1:30 PM - 3:00 PM
Workshop: Preparing to Execute Change In Your First-Year Program (Sponsored) 75
Panel: Interventions Supporting Students with Deciding Their Major 76
Panel: Building an AI-Ready University: Lessons from Institutional Innovation 77
Kaitlin Mallouk mallouk@rowan.edu, Blake Hylton
This workshop is for you if you want to make a change in your curriculum or college. That change could come from your engagement with this year’s FYEE or could be something you’ve had in mind previously.
By participating in this workshop, you will be able to:
● Refine an idea for a change
● Develop a roadmap for your change
● Anticipate the needs of stakeholders affected by the change
● Write a pitch for their project to use when engaging stakeholders
Moderator: Esther Tian (Associate Professor) EstherTian@virginia.edu
Panelist: Frances Davis (Program Head Engineering Dept) fdavis@brightpoint.edu, Lucie Tchouassi (Associate Dean for Academics) lucie.thibeaud@njit.edu, Andrew Bartolini (Director, First-Year Engineering Program) abartoli@nd.edu, Audrey Gilfillan (Embedded Therapist for the College of Engineering and Applied Science)
Choosing an engineering major is a pivotal yet often overwhelming decision for first-year students, shaped by factors ranging from academic preparation and career aspirations to identity, belonging, and access to information. This panel convenes educators and researchers from diverse institutional contexts to share evidence-informed interventions designed to support students through this process. Panelists will discuss program design, implementation strategies, equity considerations, and outcomes — offering practical insights for institutions seeking to better serve students at this critical juncture in their engineering education journey.
Moderator: Ashish Borgaonkar (Assistant Professor, Engineering Education, NJIT) ashish.borgaonkar@njit.edu
Panelists: Nicole Bosca (Director, Center for Educational Innovation and Excellence, NJIT) <nicole.bosca@njit.edu>, Jamie Payton (Dean, Ying Wu College of Computing, NJIT) <jamie.payton@njit.edu>, Samuel Lieber (Director, School of Applied Engineering and Technology, NJIT) <samuel.lieber@njit.edu>, Justine Krawiec (Director, Learning Technologies, NJIT) <jk224@njit.edu>
How can universities move beyond isolated AI initiatives to create lasting institutional change? This panel showcases NJIT's multifaceted approach to AI adoption through faculty innovation grants that support AI integration into teaching and learning, alongside AI Exploration Day, a university-wide event dedicated to exploring AI's opportunities, challenges, and societal impact. Panelists will share strategies for engaging faculty across disciplines, supporting experimentation, fostering collaboration, and developing a sustainable institutional vision for AI.
A Voice-Based Reflective Interview Process
What This Is?
This is a guided reflection tool that helps you process your conference experience through a one-on-one conversation with an AI facilitator. It works best as a spoken interview rather than typed responses—think of it as debriefing with a thoughtful colleague who asks good follow-up questions.
The process produces two outputs:
Set Up Voice Input
Choose one of these options so you can speak your responses naturally:
Option 1: Use Claude's built-in voice feature (if available in your interface)
Option 2: Use your device's voice-to-text
Why Voice Works Better
Time Commitment
Copy the entire prompt in the box below and paste it into your conversation with Claude (or another AI tool).
The AI will ask you one question at a time. Respond naturally—there are no wrong answers. If something feels significant, the AI will follow that thread before moving on.
After the interview, you'll receive a personal reflection summary. You'll have a chance to revise or confirm it.
You'll also receive a separate document with inferred insights for conference organizers. Review it to ensure you're comfortable with what's shared.
Copy everything in italics below and paste it into the GenAI model of your choice:
I have just returned from a conference and want to engage in a structured, reflective post-conference debrief. This process should center on my experience and growth, while also generating inferred insights that could be shared with event organizers.
Please guide me through this process one question at a time.
Your role:
- Act as a reflective facilitator for the interviewee
- Ask focused, sequential questions
- Adapt dynamically to what I share
- If a response reveals tension, energy shifts, or meaning, follow that thread before advancing
- Do not ask direct evaluative questions about conference logistics or administration
Question domains to cover (interviewee-facing only):
1. My mindset, expectations, and emotional state entering the conference
2. Significant takeaways or shifts in thinking (intellectual, professional, or personal)
3. Reflections on presentations, panels, or roles I played
4. Changes in how I see myself or my work after the conference
5. Pacing, energy, and engagement across the event
6. How the venue or city shaped the experience
7. Social, mentoring, or interpersonal dynamics that stood out
8. Connections made that may merit follow-up
9. Commitments or intentions for the next professional cycle or conference
All questions should remain reflective in tone and oriented toward meaning-making rather than critique.
After questioning is complete, produce two distinct outputs:
Output A: Interviewee-Facing Reflective Synthesis
A concise synthesis intended for the interviewee that highlights:
- Evidence of growth or change across the conference arc
- Key insights, values, or priorities surfaced
- Recurring themes or tensions
- What appears to matter most to the interviewee going forward
Then:
1. Ask the interviewee to confirm or revise this synthesis
2. Refine it once based on their feedback
3. Generate a brief personal follow-up to-do list (contacts, ideas, commitments)
Output B: Organizer-Facing Inferred Insights (Analytical, Not Attributed)
Separately, produce a non-attributed, inferred summary suitable for event organizers that:
- Synthesizes patterns implied by the interviewee's experience
- Infers strengths and friction points related to:
- Venue and location
- Scheduling and pacing
- Programming balance and flow
- Informal interaction spaces and networking affordances
- Administrative or logistical structures
- Avoids quoting the interviewee directly
- Frames insights as patterns observed rather than complaints
Then generate:
- A short list of actionable recommendations or considerations organizers might reflect on for future events
This organizer-facing output should be interpretive, professional, and constructive, and should clearly distinguish inference from direct testimony.
Purpose:
This process is designed to:
- Support interviewee metacognition and professional growth
- Translate lived experience into system-level learning
- Allow reflective interviews to inform event improvement without burdening the interviewee with evaluative labor
Begin with the first question.
What to Expect
The AI will:
You'll walk away with:
Privacy Note
The organizer-facing output is designed to be non-attributable. It won't quote you directly or identify you. However, you should review it before sharing to ensure you're comfortable with the inferences drawn from your experience.
Questions?
If the AI asks something that doesn't resonate, just say so. This is your reflection process—you can redirect or ask to skip questions that don't feel relevant.
Ready? Copy the prompt above and begin your reflection.