Panel Weighs AI's Effect on Student Learning
Trinity College psychology and neuroscience experts discussed how artificial intelligence is reshaping how students learn at a fall symposium.
Updated on Nov. 3, 2026 in Artificial Intelligence
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Trinity College hosted a fall symposium on AI and the Science of Human Learning on October 27 in Austin Arts Center's Goodwin Theater. Four psychology and neuroscience experts examined how AI tools are affecting student learning.
Why it matters
Panelists argued that AI can reduce the mental processing students need to learn, since the learner no longer processes the information directly. The discussion bears on how college courses, assignments, and AI policies are designed in Hartford and beyond.
Under Trinity's credit-hour standard, one credit equals three hours in class and nine hours of engaged learning outside it, and panelist Michael Grubb said AI can now complete the assignments designed to fill those outside hours in seconds. Holt has said research on AI's impact on learning is far from over.
The players
Trinity College
A private liberal arts college in Hartford, Connecticut, which hosted the symposium.
University of Connecticut
The public state university whose faculty member Roslyn Holly Fitch sat on the panel.
The details
Moderated by Professor Laura Holt, chair of the psychology department, the panel included Sarah Raskin, Michael Grubb, and Roslyn Holly Fitch of the University of Connecticut. Raskin described repetition, spaced out and supported by sleep, as the key way to learn new information, and Fitch said the prefrontal cortex is still developing into the early twenties, making unrestricted AI use in college especially problematic.
Timeline
October 27: Trinity fall symposium on AI and human learning held in Austin Arts Center's Goodwin Theater.
November 3, 2026: Trinity Tripod published an article on the symposium.
Big Picture
The panel's critique tests Trinity's own credit-hour standard, which assumes nine hours of engaged learning outside each three-hour class, a model that AI now undermines by completing those assignments in seconds.
Students who rely on AI to finish outside-of-class assignments may be bypassing the repetition and spaced practice that panelists said build lasting memory. Instructors may begin requiring AI policies in syllabi and returning to blue books or in-class open-paper quizzes.
The takeaway
Panelists agreed that AI policies should be stated in every syllabus, and that the learning science behind repetition and sleep still applies to how students should study. Readers can apply the same logic by spacing out practice rather than relying on tools to store information for them.
Further reading
For more on how these tools are changing classrooms, see Artificial Intelligence coverage.
Source note: This article includes information reported by Trinitytripod.
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