Performance and failure modes of AI chatbots on a novel concept inventory on relativity in classical mechanics

TL;DR

This study evaluates advanced AI chatbots on a new physics concept inventory about relativity, revealing high accuracy but also specific failure modes mainly due to visual misinterpretations, highlighting reliability issues.

Contribution

It introduces a novel, unpublished relativity concept inventory and assesses state-of-the-art LLMs on it, uncovering their strengths and failure patterns in physics understanding.

Findings

Gemini 3 Flash achieved 97% accuracy

Models fail on some items due to visual misinterpretation

Errors are more consistent and item-dependent than student errors

Abstract

AI chatbots are increasingly used by students as study tools in physics, raising practical questions about their reliability on conceptual tasks. Existing evaluations of large language models (LLMs) on physics concept inventories rely almost exclusively on instruments that have been publicly available for years and likely appear in model training data, making it difficult to disentangle physics competence from familiarity with the test items themselves. We address this issue by evaluating three frontier LLMs (GPT-5.2, Gemini 3 Pro, Gemini 3 Flash) on the Classical Relativity Concept Inventory (CRCI), a recently developed and validated 21-item instrument on Galilean relativity that was not publicly available at the time of testing. Each item was administered 30 times per model, and all 1890 responses were qualitatively coded along three dimensions: visual interpretation, physics reasoning, and coordination. Mean accuracy was 97% for Gemini 3 Flash, 89% for Gemini 3 Pro, and 73% for GPT-5.2, compared to 62% for the student sample (N = 267). However, all three models fail completely on a small number of items. The qualitative analysis shows that these failures stem predominantly from misinterpretations of visual content rather than from deficits in physics knowledge, and that LLM errors differ structurally from those of students: when models err, they converge on a single distractor with high consistency, whereas student errors are more broadly distributed. These findings indicate that chatbot reliability on conceptual physics is item-dependent and unpredictable, with direct implications for how concept inventories are administered.