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ChatGPT and the frustrated Socrates
Open accessAbstract
Abstract We present a case study of a conversation between ourselves and an artificial intelligence-based chatbot ChatGPT. We asked the chatbot to respond to a basic physics question that will be familiar to most physics teachers: ‘A teddy bear is thrown into the air. What is its acceleration in the highest point?’ The chatbot’s responses, while linguistically quite advanced, were unreliable in their correctness and often full of contradictions. We then attempted to engage in Socratic dialogue with the chatbot to resolve the errors and contradictions, but with little success. We found that ChatGPT is not yet good enough to be used as a cheating tool for physics students or as a physics tutor. However, we found it quite reliable in generating incorrect responses on which physics teachers could train assessment of student responses.
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“Failures of LLMs include lack of knowledge about object attributes (e.g., size, weight, softness) (Wang et al., 2023c; Liu et al., 2022b; Shu et al., 2023; Kondo et al., 2023), spatial relationships (e.g., above, inside, next to) (Liu et al., 2022b; Shu et al., 2023; Kondo et al., 2023), simple physical laws (e.g., gravity, motion, and force) (Gregorcic & Pendrill, 2023), and object affordance”
From Large Language Model Reasoning Failures (2026) · cites this paper as Gregorcic & Pendrill, 2023
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Metadata
- Authors
- Gregorcic, B., & Pendrill, A.-M.
- Affiliations
- Uppsala University (SE)
- Lund University (SE)
- Corresponding author
- Gregorcic, Bor
- Venue
- Physics Education
- Publisher
- IOP Publishing
- Published
- 2023-03-22
- Volume
- 58
- Issue
- 3
- Pages
- 035021
- ISSN
- 0031-9120 (ISSN-L)
- 1361-6552
- OpenAlex
- W4360846784
- Cited by
- 125
- References
- 9
- Citations by year
- 2026: 18 · 2025: 42 · 2024: 37 · 2023: 28
- FWCI
- 29.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physics Education (publishedVersion, cc-by)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, large language models, Socratic dialogue, physics tutoring, academic dishonesty, incorrect responses, physics misconceptions, chatbot reliability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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