Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education
Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analogies compare an unfamiliar concept to something familiar, but judging whether the comparison holds requires knowledge of both. GenAI may also embed assumptions about who the learner is. GenAI education research centers on output correctness, leaving students' critical reception of analogies largely unexamined. Method: We investigate how students evaluate the accuracy, appropriateness, and assumptions in GenAI-generated analogies, and their perceptions of interest-personalized versus generic technical explanations. Ten students with CS2 exper...
| Authors | Seth Bernstein, Naaz Sibia |
|---|---|
| Primary category | cs.HC |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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