• Keynote: The Human Opportunity in an AI-Driven World Video

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    Join me for a dynamic session on the transformative impact and challenges of artificial intelligence across key sectors, including civil interactions, energy, privacy, education, and media. I will spotlight the University of Michigan as a leader in responsible AI usage, showcasing tools like umgpt, maizey, go blue, and mimaizey. Participants will explore the concept of autonomous AI agents and discussions will focus on truth verification, privacy concerns, and the real costs of "free" AI services.

    The session will include an interactive breakout activity where we will tackle critical privacy and equity issues and the changing landscape of education. I will also touch on AI's role in medicine, law, and media integrity, emphasizing the need for critical media literacy in the age of deepfakes.

    BBB 1670
    Mon 10:00 am - 11:15 am
    Presentation
    docs.google.com

  • Concurrent I: Adding Homework Drafts to your Course Video

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    Homework drafts are an assignment that has been added to Biomede 221, a ~90-student sophomore/junior level course that combines biochemistry and thermodynamics, for the past 4 semesters (FA23, WN24, FA24, WN25).

    Homework drafts are a tool to help students build good habits by starting assignments early and scaffolding their work over a longer period of time. Homework drafts can reduce the anxiety associated with starting a new assignment and ensure student readiness for in-class Participation/small group problem solving. Other benefits of their implementation in BME 221 have included neater looking/better organized homework submissions, increased attendance at office hours (OH) and better distribution of OH attendance throughout the week (not just on the due date), and improved test-taking skills (i.e., breaking down problems).

    This session will include logistical tips for adding homework drafts to your class (e.g., timing, how to score) and an example of student-facing instructions that can be freely used/adapted by faculty colleagues. The session will also include anonymized examples of strong and weak homework draft submissions from real BME 221 students (i.e., what do these things actually look like) and proof that my students do not hate them based on feedback in teaching evaluations!

    BBB 1690
    Mon 11:30 am - 12:30 pm
    Presentation
    docs.google.com

  • Concurrent I: On-Demand Learning for Experiential Education: ESTRH + Maizey AI Video

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    This presentation introduces the Engineering Student Teams Resource Hub (ESTRH), a centralized Canvas platform designed to enhance experiential engineering education at Michigan. Developed as a part of the Electric Vehicle Center (EVC) educational initiative, the ESTRH consolidates information pertinent to student teams empowering deeper learning through hands-on, student-led, project-based work.

    The ESTRH is tightly integrated with the U-M Maizey AI assistant—a tool similar to UM-GPT but designed to prioritize a selected dataset curated by our team. Maizey is available 24/7 and trained specifically on information relevant to student teams, providing students with fast, accurate, and trustworthy answers to their questions. It also cites its sources, enabling students to verify information and explore deeper on their own terms.

    We’re now working to expand the dataset by incorporating course materials and technical content provided by faculty. In addition, we’re aiming to build stronger connections between student teams and professors by enabling Maizey to recommend who to reach out to, and what courses to take, when a student needs help solving a problem in a specific discipline. To do this, we are seeking faculty who are open to having their areas of expertise and contact information referenced by Maizey—helping students navigate not just what they need to learn, but who at Michigan can help them learn it.

    BBB 1620
    Mon 11:30 am - 12:30 pm
    Presentation
    docs.google.com

  • Concurrent II: Creating Transparent Assignments: Using AI to Enhance Student-Centered Instruction Video

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    Explore how integrating generative AI into assignment design can enhance transparency and support student-centered learning environments. In this interactive session, participants will practice using AI for designing course assignments that clearly communicate course norms, expectations, and evaluation criteria. Participants will leave with the tools needed to better support student-centered instruction in their course.

    Instructions for participants: Participants should bring their laptops and a digital copy of a course assignment to this session.

    BBB 1620
    Mon 1:30 pm - 2:30 pm
    Presentation
    docs.google.com

  • Concurrent II: FeedbackWriter: A Human-AI Collaborative Approach that Helps TAs Provide Feedback Video

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    The rise of large language models (LLMs) presents exciting opportunities for transforming how tutoring systems are authored, yet current tools often fail to accommodate educators' pedagogical goals or expertise. This talk explores a series of research efforts that develop and evaluate human-AI collaborative interfaces for efficient and effective authoring of educational content. Drawing from three key projects—ReadingQuizMaker, ScienceChat, and NoteCoPilot—this work demonstrates how interface design can empower instructors to steer AI outputs, retain cognitive engagement, and reduce hallucinations. Across studies with educators and students, findings suggest that teachers prefer systems that give them control over when and how to use AI, support iterative refinement, and allow expression of domain knowledge.

    BBB 1690
    Mon 1:30 pm - 2:30 pm
    Presentation
    drive.google.com

  • Panel: Student Voices on Generative AI: Findings on Usage and Attitudes among CoE Undergraduates Video

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    Panelists from the Technical Communication program will present initial findings from a year-long study on CoE undergraduate student attitudes towards and uses of generative AI. The presentation draws from more than 30 interviews with undergraduates in order to understand our students’ perspectives towards this emerging and ubiquitous technology. Among the themes addressed in the presentation include student perspectives on how generative AI affects idea generation, creativity, writing style, authorship and ownership of work, and the context of students’ own learning goals and experiences. The panelists will use these themes to generate conversations about how students conceptualize the opportunities, limitations, affordances, and challenges of generative AI in their engineering curricula. This panel will also invite attendees to think about how this research impacts their policies and pedagogical approaches to GenAI use in their courses.

    BBB 1670
    Mon 2:45 pm - 4:00 pm
    Presentation
    docs.google.com
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