Conference Paper (preprint): “Your Students Don’t Use LLMs Like You Wish They Did”

The preprint linked below will be presented at the 64th Annual Meeting of the Association for Computational Linguistics (ACL).

Title

Your Students Don’t Use LLMs Like You Wish They Did

Authors

Sebastian Kobler, Matthew Clemson, Angela Sun, Jonathan K. Kummerfeld
Affiliation: The University of Sydney (All Authors)

Source

via arxiv
DOI: 10.48550/arXiv.2604.23486

Abstract

Educational NLP systems are typically evaluated using engagement metrics and satisfaction surveys, which are at best a proxy for meeting pedagogical goals. We introduce six computational metrics for automated evaluation of pedagogical alignment in student-AI dialogue. We validate our metrics through analysis of 12,650 messages across 500 conversations from four courses. Using our metrics, we identify a fundamental misalignment: educators design conversational tutors for sustained learning dialogue, but students mainly use them for answer-extraction. Deployment context is the strongest predictor of usage patterns, outweighing student preference or system design: when AI tools are optional, usage concentrates around deadlines; when integrated into course structure, students ask for solutions to verbatim assignment questions. Whole-dialogue evaluation misses these turn-by-turn patterns. Our metrics will enable researchers building educational dialogue systems to measure whether they are achieving their pedagogical goals.

Direct to Abstract + Link to Full Text

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