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THEME 1 SESSION 4: �STRATEGIES TO INCORPORATE CHATGPT/AI IN BME CURRICULUM

Fifth Biomedical Engineering Education Summit

May 29-31, 2024

NJIT

Alexandra Werth

Cornell University

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Assistant Professor in the Meinig School of Biomedical Engineering and Engineering Education Researcher

Session Chairs / Facilitators:

David A. Rubenstein

Stony Brook University

Associate Professor of Biomedical Engineering and Associate Dean of Academic and Student Affairs (The Graduate School)

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Objective

  • The primary objective of this session is to facilitate a collaborative brainstorming exercise that identifies potential applications and integration opportunities for generative Artificial Intelligence (AI) and Large Language Models (LLMs) in the Biomedical Engineering (BME) curriculum.
  • Specifically, we aim to explore how generative AI can be used to:
    1. Enhance student learning experiences
    2. Improve teaching methodologies and tools
    3. Teach best-practices surrounding AI to prepare our BME graduates for the future
    4. Use AI to enable more justice, equity, and inclusion in our classrooms
  • While essential to consider, we will save discussions about academic integrity concerns for another session.

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Session Outline

  • Break into five sub-groups to discuss:
    1. Current practices of using generative AI in BME classes
    2. Current practices of using generative AI in BME research
    3. Motivations and opportunities for incorporating generative AI into the BME curriculum
    4. Support needed to feel confident using LLM/AI in the classroom
    5. Justice, diversity, equity, and inclusion and generative AI - concerns and opportunities
  • We will begin sub-group conversations with a round of short introductions (name, institution, role, research interest) as well as a brief thought on how you imagine the impact of generative AI on a BME undergraduate class in 2050 (10 minutes)
  • Brainstorming session about your sub-topic (15 minutes)
  • Share out discussion summary to the session (3 minutes / team)

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Session Outline

Big Sticky Note Pads:

  • For summarizing and presenting team discussions and ideas
  • Will be posted around the room for:
    • Future sessions to review and build upon previous discussions
    • Creating our session report

Smaller Sticky Note Pads:

  • Record and capture "what-ifs", "ah-ha" moments, or interesting thoughts throughout conversations
  • Use them to record your own thoughts or comments from others
  • Post these on walls of the room to share with others

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Session Outline

The Jargon Wall

There is a lot of jargon and differing levels of understanding surrounding generative AI and Large Language Models

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If you notice a term being used:

  1. Incorrectly or inconsistently, that needs to be defined more clearly for the group
  2. You are unfamiliar with and would like a definition

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Please come up to the jargon board at any time during the session to write these down. If needed, we may interrupt the session to help clarify critical terms.

JARGON

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Session Outline

  • Break into five sub-groups to discuss:
    1. Current practices of using generative AI in BME classes
    2. Current practices of using generative AI in BME research
    3. Motivations and opportunities for incorporating generative AI into the BME curriculum
    4. Support needed to feel confident using LLM/AI in the classroom
    5. Justice, diversity, equity, and inclusion and generative AI - concerns and opportunities
  • We will begin sub-group conversations with a round of short introductions (name, institution, role, research interest) as well as a brief thought on how you imagine the impact of generative AI on a BME undergraduate class in 2050 (10 minutes)
  • Brainstorming session about your sub-topic (15 minutes)
  • Share out discussion summary to the session (3 minutes / team)

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