Conference Resources

Welcome to our AI conference resource page. This section will serve as a hub for resources before, during and after the conference.

Our knowledge and experiences with AI in education are not uniformed and the conference is designed to acknowledge and respect that diversity. As we embark on our  journey towards shared understanding, we curated some resources that we would like conference participants to watch, read, and listen to before April 19th. We anticipate that exploring these resources should not take more than 3 hours of your time. We won't have a quiz so skimming the materials is ok :) as we know you have very busy schedules. Your time is a valuable commodity and we feel so honored that you have chosen to spend your day exploring and engaging with AI together. Cheers to One MUSC and Changing what's Possible.

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WATCH: Mollick & Mollick 5-part Interactive Crash Course on AI in Education. (2023). Wharton School at the University of Pennsylvania.

  • In this five-part course, Wharton Interactive's Faculty Director Ethan Mollick and Director of Pedagogy Lilach Mollick provide an overview of AI large language models for educators and students. They take a practical approach and explore how the models work, and how to work effectively with each model, weaving in your own expertise. They also show how to use AI to make teaching easier and more effective, with example prompts and guidelines, as well as how students can use AI to improve their learning. (Time commitment about 50 minutes the average length of each video is about 11 minutes).

Link to playlist: Wharton Interactive Crash Course: Practical AI for Instructors and Students

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LISTEN: Derek Bruff's Intentional Teaching Podcast – interview with James Lang & Michelle Miller about Rethinking Teaching in an Age of AI

  • In her 2022 book Remembering and Forgetting in the Age of Technology, Michelle D. Miller writes about the "moral panics" that often happen in response to new technologies. In his 2013 book Cheating Lessons: Learning from Academic Dishonesty, James M. Lang argues that the best way to reduce cheating is through better course design. Michelle is a professor of psychological sciences at Northern Arizona University and a prolific writer and speaker on teaching and learning in higher ed. Jim is a former professor of English at Assumption College and also a prolific writer and speaker on teaching and learning in higher ed. In the podcast they raise some important questions for educators to consider  for retooling courses and assignments to account for AI technology. (Time commitment about 40 minutes)

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READ:  Resources that address a wide range of topics  from assessing an institution's AI readiness to enhancing AI literacy among healthcare students, understanding AI's role as a pedagogical aid, and preparing students for the AI-driven workforce. These materials provide  insights and guidelines to navigate the complexities of AI adoption in education and health sciences.

  • Before Conference Reading (You will need to log in with your NET ID) (Time commitment about 30 - 45 minutes to Skim Articles)

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BOOKMARK: We will be using co-pilot during conference for the interactive sessions. https://copilot.microsoft.com/ (You can use your MUSC email to login to use this AI.)

Please use responsibly and DO NOT enter any HIPAA or FERPA data. Please see MUSC's policy for AI use