#Learning at Scale

Learning at Scale

2 artículos

A teaching assistant, instructor and university student discuss two proposed feedback comments beside a probability tree for two coin tosses.

Artículo de congreso2025

Improving Generative AI Student Feedback: Direct Preference Optimization with Teachers in the Loop

Juliette Woodrow, Chris Piech, Sanmi Koyejo

Proceedings of the 18th International Conference on Educational Data Mining

Teaching assistants' choices can help adapt AI feedback to a particular course. In a university probability course, researchers trained a model using comments that staff selected, edited, or wrote themselves. Experts preferred its feedback to GPT-4o in a blind comparison, while classroom staff still frequently wrote their own responses. The study demonstrates a feasible feedback workflow and stronger teacher preference, but it did not test whether students learned more or used the feedback more effectively.

Teacher AgencyAI FeedbackHigher Education

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University learners and a tutor discuss a cell worksheet with written reasons and a confidence judgment beside a practice-question interface.

Artículo de congreso2025

Artículo de congreso 192

How Adding Metacognitive Requirements in Support of AI Feedback in Practice Exams Transforms Student Learning Behaviors

Mak Ahmad, Prerna Ravi, David Karger, Marc Facciotti

Proceedings of the Twelfth ACM Conference on Learning @ Scale

A large biology course tested several forms of practice-exam feedback, including GPT-4o explanations and textbook links. No significant overall difference emerged in subsequent exam performance between feedback conditions. Students nevertheless described useful changes in how they reasoned, judged confidence, and studied. Because every condition required an explanation and confidence judgment, the trial cannot isolate those requirements as the cause of learning gains. It offers a careful distinction between reported study changes and measured achievement.

Learning at ScaleBiology EducationMetacognition

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