The Boiling-Frog Problem of Physics Education
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915702672195584 |
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| author | Kortemeyer, Gerd |
| author_facet | Kortemeyer, Gerd |
| contents | It is astonishing how rapidly general-purpose AI has crossed familiar thresholds in introductory physics. Comparing outputs from successive models, GPT-5 Thinking moves far beyond the plug-and-chug tendencies seen earlier: on a classic elevator problem it works symbolically, notes when variables cancel, and verifies results; attempts to prompt novice-like behavior mainly affect tone, not method. On representation translation, the model scores 24/26 (92.3%) on TUG-Kv4.0. In a card-sorting proxy using two of my comprehensive finals (60 items), its categories reflect solution method rather than surface features. Solving those same exams, it attains 27/30 and 25/30, with most misses in ruler-based ray tracing and circuit interpretation. On epistemology, five independent CLASS runs yield 100\% favorable, indicating a simulated expert-like stance. Framed as a "boiling frog" problem, the paper argues for a decisive jump: retire credit-bearing unsupervised closed-response online assessments; grade process evidence; use paper, whiteboarding; shift weight to modeling, data, and authentic labs; require transparent, citable AI use; rebuild problem types; and lean on research-based instruction and peer learning. The opportunity is to foreground what AI cannot substitute for: modeling the world, arguing from evidence, and making principled approximations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Boiling-Frog Problem of Physics Education Kortemeyer, Gerd Physics Education It is astonishing how rapidly general-purpose AI has crossed familiar thresholds in introductory physics. Comparing outputs from successive models, GPT-5 Thinking moves far beyond the plug-and-chug tendencies seen earlier: on a classic elevator problem it works symbolically, notes when variables cancel, and verifies results; attempts to prompt novice-like behavior mainly affect tone, not method. On representation translation, the model scores 24/26 (92.3%) on TUG-Kv4.0. In a card-sorting proxy using two of my comprehensive finals (60 items), its categories reflect solution method rather than surface features. Solving those same exams, it attains 27/30 and 25/30, with most misses in ruler-based ray tracing and circuit interpretation. On epistemology, five independent CLASS runs yield 100\% favorable, indicating a simulated expert-like stance. Framed as a "boiling frog" problem, the paper argues for a decisive jump: retire credit-bearing unsupervised closed-response online assessments; grade process evidence; use paper, whiteboarding; shift weight to modeling, data, and authentic labs; require transparent, citable AI use; rebuild problem types; and lean on research-based instruction and peer learning. The opportunity is to foreground what AI cannot substitute for: modeling the world, arguing from evidence, and making principled approximations. |
| title | The Boiling-Frog Problem of Physics Education |
| topic | Physics Education |
| url | https://arxiv.org/abs/2508.08842 |