13:30 - 14:30, in Room 216
In this discussion, we will ask our panelists to discuss the impact of AI in our classrooms, both present and future, and how best we can utilize this emerging technology to improve student learning.
| Panelist | Panelist | Panelist | Moderator |
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| Ms. Eileen Malick Technology Teacher at Benedictine Schools of Richmond |
Prof. Darcy Mays Executive Associate Dean for Administration and Analytics, College of Humanities and Sciences, Virginia Commonwealth University |
Mr. Michael Mailey MS Science Department chair and Physical, Earth and Space Science teacher, Collegiate School, Richmond |
Prof. Richard Joh Assistant Professor, Department of Physics, Virginia Commonwealth University |
Topics to be discussed:
The Future of Education
- How might generative AI shift the balance between analytical derivation, conceptual reasoning, and computational modeling in undergraduate and graduate physics curricula?
Pedagogy, Assessment & Learning Design
- What pedagogical models (e.g., flipped classrooms, mastery-based progression, inquiry-driven labs) are most compatible with AI-enhanced learning environments, and how should we rethink course design accordingly?
- How can AI be leveraged to move beyond traditional assessment, enabling evaluation of higher-order skills such as physical intuition, model building, and interdisciplinary reasoning?
- In experimental courses, how might AI-driven simulations and real-time data analysis tools complement or even transform hands-on laboratory experiences without diminishing their pedagogical value?
Integration, Infrastructure & Research
- What infrastructure — technical, curricular, and institutional — is necessary to effectively integrate AI tools into upper-division and graduate-level physics instruction?
- How might collaboration between physics educators and AI researchers accelerate the development of domain-specific models that support complex tasks like symbolic derivation, experiment design, or data interpretation?
Ethics, Academic Integrity & Policy
- With the rise of powerful generative models, how should institutions rethink academic integrity policies and course expectations, particularly in areas like problem-solving assignments, computational projects, and take-home exams?
- What strategies can departments adopt to ensure equitable access to AI tools and prevent widening disparities in student learning outcomes?
Please contact Samanthi Wickramarachchi if you have any questions about this Panel Discussion.
