Examining Student And Ai Generated Personalized Analogies In Introductory Physics

Amogh Sirnoorkar, Purdue University West Lafayette

Winter Allen, Purdue University West Lafayette

N. Sanjay Rebello, Purdue University West Lafayette

Date

11/2025

AI Overview

Why it matters

Self-generated analogies can facilitate learning better than taught ones, but supporting personalized analogies in large classes is challenging. Generative AI offers potential to address this issue, yet comparisons between student and AI analogies remain scarce.

Study at a glance

Question: How do student-generated and AI-generated physics analogies compare, and what affects students' perceived ease in creating them?
Setting and data: Introductory calculus-based physics course at a large Midwestern university, Spring 2024, 839 completed student survey responses.
Method: Qualitative content analysis of open-ended survey responses and emergent coding.
AI tools: ChatGPT

Key findings

When explaining physics to second graders, only 38.5% of students spontaneously used analogies, whereas 96% of AI-generated responses used analogies.
When explicitly prompted to use their preferred everyday contexts, 95.9% of students successfully self-generated analogies spanning diverse domains such as sports, games, and leisure.
While 61% of students found explaining to second graders difficult, 52% found generating analogies in their preferred everyday contexts easy.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

Comparing abstract concepts (such as electric circuits) with familiar ideas (plumbing systems) through analogies is central to practice and communication of physics. Contemporary research highlights self-generated analogies to better facilitate students' learning than the taught ones. "Spontaneous" and "self-generated" analogies represent the two ways through which students construct personalized analogies. However, facilitating them, particularly in large enrollment courses remains a challenge, and recent developments in generative artificial intelligence (AI) promise potential to address this issue. In this qualitative study, we analyze around 800 student responses in exploring the extent to which students spontaneously leverage analogies while explaining Morse potential curve in a language suitable for second graders and self-generate analogies in their preferred everyday contexts. We also compare the student-generated spontaneous analogies with AI-generated ones prompted by students. Lastly, we explore the themes associated with students' perceived ease and difficulty in generating analogies across both cases. Results highlight that unlike AI responses, student-generated spontaneous explanations seldom employ analogies. However, when explicitly asked to explain the behavior of the curve in terms of their everyday contexts, students employ diverse analogical contexts. A combination of disciplinary knowledge, agency to generate customized explanations, and personal attributes tend to influence students' perceived ease in generating explanations across the two cases. Implications of these results on the potential of AI to facilitate students' personalized analogical reasoning, and the role of analogies in making students notice gaps in their understanding are discussed.