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GenAI in Flux - Framing The Conversation

Policy Shifts, Open-Source Disruption, Adoption Surge, and Ethical Implications for Healthcare, Education & USF

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Opening: Framing the Conversation

Welcome to the 2nd USF GenAI Symposium.
At our first symposium in 2024, we asked: Is GenAI a fad or a force?
Since then, GenAI has disrupted every technological, policy, and education assumption.
Today, I offer a framework for navigating this two-day event through the lens of ethics, equity, and institutional mission alignment.

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Positionality & Transparency

MBA, MIPS, EdDr. Candidate, Focusing in GenAI Ethics USF
USF Faculty, Program Director, Serial Entrepreneur, Former Corporate Executive
A longtime advocate of ethical, inclusive GenAI
Engaged in USF-wide GenAI efforts since Fall 2022
Practitioner-scholar using GenAI critically for research, teaching, praxis
Transparent about bias: committed to reflective and values-aligned adoption– GenAI Ethics for Leadership Framework

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GenAI Rapid Adoption Continues

Rapid adoption continues –ChatGPT hits 400M+ users (Reuters, 2025)
55% of Americans use AI regularly (Pew Research Center, 2024)
56% of U.S. college students used GenAI in coursework (Nam, 2023)
55% of Americans use AI regularly (Pew Research Center, 2024)
70% of USF students used GenAI academically (ETS, 2025)
76% of employers seek GenAI-skilled workers (Wells, 2024)

Benefits and Challenges: efficiency, rapid analysis, cost reduction — but also growing ethical risks

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GenAI BigTech Technology Dominance Shifting

BigTech grip on the market challenged
DeepSeek, small Chinese launches a very competitive model with less than $6 million and two months, using open source models and older NVIDIA computer chips, billions of market valuations evaporates (CNN, 2025; Stanford Cyber Policy Center, 2025; Reuters, 2025)
Open-source teams at Stanford et al., launches model with $50 investment (Zhang et al., 2024)
This carks open the door to more cost-effective choices for the market and high education

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GenAI Policy Shifting: U.S.

Biden’s Whitehouse’s AI Bill of Rights promising, was not enforceable, repealed with new administration (White House, 2024)
U.S. Congress launched bipartisan AI task force report (U.S. Congress, 2024)
California vetoed its AI accountability bill (SB 1047) in Sept 2024, but author plans to reintroduce it (CA Legislature, 2024; ACM.org 2025)
700 bills submitted, only 20% approved (Tech Policy Press, 2025; BSA 2025)

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GenAI Global Policy

EU AI Act (2nd Amendment): mandates transparency, oversight, and ethical compliance across law, healthcare, education, finance, and IT (European Commission, 2025)
China: Accelerated state-sponsored AI innovation, and policy (CSET, 2024)

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Navigating GenAI Ethical and Operational Tensions

Ethical & Operational Challenges

Bias in training data and outputs
Data monopoly & access control
Dependency and erosion of autonomy
Deskilling of learners and professionals
Global imbalance in AI access and influence
Human replacement anxieties
Inequality and access gaps
Intellectual property confusion
Manipulation of user behavior
Market dominance by few actors
Misinformation and hallucinations
Over-reliance on AI tools
Plagiarism and academic misconduct
Political influence and policy gaps
Privacy violations and surveillance risks
Transparency and algorithmic opacity
Unintended consequences of AI deployment
User consent inadequacies

Sources: AAC&U, 2024; AMIA 2024; Bowen & Watson, 2024; European Commission, 2025; JAMA Pediatrics, 2024; Liang et al., 2024; Pew Research Center, 2024; Stanford HAI, 2024; U.S. Department of Education, 2024; U.S. Congress, 2024; White House, 2024; World Health Organization, 2024)

Ethical Opportunities & Pathways

Accessibility-first frameworks
AI literacy embedded in curriculum
Critical thinking + digital discernment
Data sovereignty and ethical stewardship
Equity-by-design in tools and policy
Ethical impact reviews (AI IRB-style)
GenAI-compatible honor code structures
Human-in-the-loop feedback systems
Legal frameworks for GenAI + IP clarity
Mission-aligned digital governance
Open-source foundational model alternatives
Participatory co-design with students & stakeholders
Redefining rigor through reflective design
Sustainability and long-term digital planning
Transparent algorithms & traceability tools
Upstream & downstream monitoring of AI use

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GenAI Ethics in Higher Education

Who is accountable when GenAI misguides a diagnosis or student paper?
Are detection tools unfair to multilingual students? (Liang, 2023)
Can bias be traced, corrected, and mitigated?
Are institutions ready for this surge?

Higher Education policy continues to shift from prohibition – detection and punishment– to faculty-centred policies, to institutional critical assessment and adoption. Specially given that detection tools are unreliable and introduce new ethical concerns (Bender et al., 2021)

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GenAI in Education: Use Cases

GenAI detection falsely flags multilingual students (Lian et al., 2023)
New pedagogy: require GenAI use + critical reflection (Bowen & Watson, 2024)
Institutional mission framing: trust, inclusion, student-centered support
Focus on fairness, transparency, and digital equity

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GenAI in Healthcare- Case Studies

AMIA reports widespread AI tool use in diagnostics (AMIA, 2025)
JAMA Pediatrics: 83% LLM misdiagnosis rate in pediatric triage (2024)

Implication: Efficiency without ethics = harm
Response: human-in-the-loop, privacy, values-first innovation

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USF GenAI Efforts – Non Exhaustive List

GenAI literacy efforts: 160+ faculty/staff completed USF ETS GenAI Certificate
USF’s AAC&U Interdisciplinary AI Institute launched, which allow to connect, learn and share best practices with campuses in North America (Howell, Seba, Ramos, Azarm, Munnich)
Surveys: 1,000+ students & 60+ faculty shared GenAI usage data

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USF GenAI Efforts – Non Exhaustive List

Multiple groups sprucing in campus (for example AI and Faith)
UAC “Bias & Ethics in AI” Workshop Series created and launched to interrogate GenAI detection and equity (Seba & Howell, 2024)
Curriculum redesign – new and enhance GenAI centric courses
SOM conference taking place this Friday

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From Flux to Framework – Where do we go from here?

Policy: Clear institutional guidelines with student voices
Practice: AI fluency for all—educators, clinicians, students
Values: Equity, human dignity, and justice at the center

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UAC Workshop Sessions– Howell & Seba

First day: “high level” review of GenAI and the role of bias in the technology.
Second day: “dive in” to detection tools and hands on exercises
Third: “monolingualism and/or lingualism”

standard language ideology language attitudes that deem standardized English superior and all other Englishes and their users inferior,
tacit English only Policies:
The Myth of LInguistic Homogeneity:, and
The Myth of Linguistic Uniformity, Stability, and Separateness

Fourth day: connecting language bias to LLMs and the way language is used for gatekeeping detection platforms…just don’t. What do we think we are detecting and why do we think we need to do it?

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UAC Workshop Thoughts-Faculty’s Critical ROle

Call to Action: Let’s continue the conversation and collaborate on how we can collectively shape GenE's role in education at our institution.
For Students: International and first-generation students are disproportionately impacted, leading to accusations of academic dishonesty. This can damage reputations and result in penalties like failing grades or disciplinary actions.
For Faculty: Faculty members, relying on faulty tools, have faced challenges in fairly assessing student work. In some cases, faculty trust in AI detection tools has led to disputes between instructors and students, complicating the student-teacher relationship (Lian et al., 2023).

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GenAI Ethics Framework

More than tools— teach discernment
More than results – teach process
More than detecting GenAI – teach how to critically and ethically use tools
More than keeping student’s GenAI use underground – teach to bring it to the light, understand its challenges – critical thinking, bias, deskilling – assist students to get the required skills for citizenship and workforce integration in the AI era!

Sources: AI capabilities derived with reference to an analysis of the MAGE framework, based on ChatGPT 4 as of October 2023. See Zaphir, L., Lodge, J. M.,

Lisec, J., McGrath, D., & Khosravi, H. (2024). How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial

intelligence. arXiv preprint arXiv:2406.14769.

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More Use Cases – Curriculum Design & Advocacy

HS 633: Exploring GenAI Ethics (from scratch): Fall 2024
HS 632: Consumer Health Informatics enhanced w/ GenAI integration: Spring 2025
AAC&U Leadership Conference Podium Presentation: April, 2025
AMAI Clinical Health Informatics Podium Presentation: May, 2025
UIC & USF Joint GenAI Curriculum Research Fall 2024- Spring 2025
USF SOE Dissertation – GenAI Ethics Leadership Within Rapidly Shifting Technology and Policy Context: A Case Study of Higher Education Stakeholders' Perspectives and Concerns (expected Fall 2025)

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Recap & Reflection

GenAI disruption is not a moment—it is a movement
We design with GenAI ethics and foresight
Mission values are not add-ons—they are our goal
Institutional support, open conversation, and faculty and student empowerment are critical with GenAI

Please reflect on how to critically engage in this transformational force to shape the future of society, higher education, our roles as educators, and students lives…….. How are you going to lead?

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To Conclude: Encouraging Progress

Global, state and civil society actors policy encouraging
Open-source and more cost effective capable models (DeekSeep, Stanford $50 models et al) challenging BigTech and providing more access
Anthropic making progress on understanding GenAI’s “back-box”
Workforce seeking more GenAI skilled candidates
We know more about GenAI now than ever before
Higher education:

moving from prohibition to per course/faculty to selected cases to cross programs/campus curriculum adoption (Florida University, et al)
GenAI enthusiast groups popping up in campuses across the country
Critical faculty and leaders more aware!

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Reading – 2024 GenAI Symposium List

Ethan Mollick (2024). Co-Intelligence. Living and Working with AI,
Reid Hoffman (2023). Impromptu: Amplifying Our Humanity Through AI, 23) Free download
Benjamin, R. (2019) Race After Technology
Lee, P., Goldberg, C., & Kohane, I. (2023). The AI Revolution in Medicine: GPT-4 and Beyond
Haby David Epstein (2019) Range: Why Generalists Triumph in a
Clive Thompson (2019) Specialized WorldCoders: The Making of a New Tribe and the Remaking of the World

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Useful Reading

As If Human – Shadbolt & Hampson (2023)
Genesis – Kissinger, Schmidt, Huttenlocher (2021)
Teaching with AI – Bowen & Watson (2024)
Holley & Mathur (2024) – LLMs and Generative AI for Healthcare
Seba’s GenAI Ethics for Leaders Newsletter: LinkedIn | Substack | freddieseba.com

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Gratitude

My students, co-designers, and thought partners at USF
Colleagues at USF’s AAC&U Institute on AI, Pedagogy & the Curriculum
Faculty, researchers, staff, leadership, and practitioners committed to responsible innovation
USF leadership, faculty, and staff!

Let’s shaping a more equitable GenAI-powered present and future!

Connecting & Collaborating: seba@usfca.edu; linkedin.com/in/freddiesebaprofile, freddieseba.com

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Transparency

This presentation reflects my academic and practical perspective as a faculty member, researcher, and doctoral candidate focused on GenAI ethics.
I have used GenAI tools in drafting and refining this presentation.
I strive to model transparency, responsibility, and ethical curiosity in GenAI use and welcome critical reflection and dialogue on this evolving topic.

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References

AAC&U. (2024). Institute on AI, Pedagogy & the Curriculum. https://www.aacu.org
AI & Society Journal. (2023). The ethics of AI in academic integrity: Detection tools and the future of assessment. AI & Society Journal.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2023). On the dangers of stochastic parrots: Can language models be too big? Communications of the ACM, 64(10), 62–71. https://doi.org/10.1145/3442188.3445922
Bloomberg. (2025). How DeepSeek and Open-Source AI Models Are Disrupting Big Tech. https://www.bloomberg.com
Bowen, J., & Watson, C. (2024). Teaching with AI. Routledge.
Brock University. (2023). International students and AI: Misclassification and discrimination in academic integrity algorithms. https://brocku.ca
California State Legislature. (2024). SB 1047 – Safe and Secure Innovation for Frontier Artificial Intelligence Models Act. https://leginfo.legislature.ca.gov
Chronicle of Higher Education. (2023). We should normalize open disclosure of AI use. https://www.chronicle.com
CNN. (2025). What is DeepSeek? The Chinese AI Startup That Shook the Tech World. https://www.cnn.com
European Commission. (2025). EU AI Act. https://digital-strategy.ec.europa.eu
Goldman, D. (2025). DeepSeek AI: China’s Challenger to Big Tech. CNN Business. https://cnn.com
Holley, K., & Mathur, R. (2024). LLMs and generative AI for healthcare: Ethical implications. In Proceedings of the 2024 AMIA Symposium.

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References

Howell, N., & Seba, F. (2024). Bias in GenAI detection. USF-UAC Workshop.
JAMA Pediatrics. (2024). Misdiagnosis in pediatric LLM applications.
Liang, W., Yuksek Gonul, M., Mao, Y., Wu, E., & Zou, J. (2024). GPT detectors are biased against non-native English writers. arXiv preprint arXiv:2304.10638.
McFaul, S., et al. (2024). Fueling China’s Innovation: The Chinese Academy of Sciences and Its Role in the PRC’s S&T Ecosystem. Center for Security and Emerging Technology (CSET). https://cset.georgetown.edu
Mok, C. (2025). Taking Stock of the DeepSeek Shock. Stanford Cyber Policy Center. https://cyber.fsi.stanford.edu/publication/taking-stock-deepseek-shock
Nam, H. (2023). GenAI in higher ed survey. EdTech Journal.
National Conference of State Legislatures. (2024). Artificial Intelligence 2024 Legislation. https://www.ncsl.org
New York Times. (2023). ChatGPT in schools: Bans, challenges, and new directions. https://www.nytimes.com
Pew Research Center. (2024). AI in U.S. education and workforce. https://www.pewresearch.org
Seba, F. (2024). GenAI Ethics for Leaders Newsletter. https://freddieseba.com
Silicon UK. (2024). Stanford Team Builds Powerful AI Model for $50. https://www.silicon.co.uk
Stanford HAI. (2024). Human-Centered AI Principles. https://hai.stanford.edu
Stanford University. (2024). AI in education: Guidelines and frameworks. https://hai.stanford.edu
U.S. Congress. (2024). Bipartisan Task Force on AI. https://www.congress.gov
U.S. Department of Education. (2024). Guiding Principles for AI in Education. https://www.ed.gov
USF ETS. (2025). Certificate in Ethical GenAI Use. University of San Francisco.

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References

White House. (2024). AI Bill of Rights. https://www.whitehouse.gov
World Health Organization. (2024). Ethics and Governance of Artificial Intelligence for Health. https://www.who.int
Zaphir, L., Lodge, J. M., Lisec, J., McGrath, D., & Khosravi, H. (2024). How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence. arXiv preprint arXiv:2406.14769. https://arxiv.org/abs/2406.14769