# Superstudent intelligence in thermodynamics

arXiv: https://arxiv.org/abs/2506.09822 · HTML: https://mlsanity.com/paper/2506.09822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning](https://mlsanity.com/paper/2203.16797/) (2022)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models](https://mlsanity.com/paper/2502.00334/) (2025)
- [MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education](https://mlsanity.com/paper/2106.07340/) (2021)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction](https://mlsanity.com/paper/2407.18011/) (2024)
- [GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes](https://mlsanity.com/paper/2409.15981/) (2024)
- [Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassing Human Performance and Implications for Educational Assessment](https://mlsanity.com/paper/2505.09438/) (2025)
- [Effective and Scalable Math Support: Evidence on the Impact of an AI- Tutor on Math Achievement in Ghana](https://mlsanity.com/paper/2402.09809/) (2024)
- [KnowTD-An Actionable Knowledge Representation System for Thermodynamics](https://mlsanity.com/paper/2407.17169/) (2024)
- [Using Large Language Models for Solving Thermodynamic Problems](https://mlsanity.com/paper/2502.05195/) (2025)

## Built on by (most cited first, 1 of 1)

- [ThermoQA: A Three-Tier Benchmark for Evaluating Thermodynamic Reasoning in Large Language Models](https://mlsanity.com/paper/2604.19758/) (2026)

## Abstract (arXiv)

In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics exam is a difficult hurdle for most students, where they must show that they have mastered the fundamentals of this important topic. Consequently, the failure rates are very high, A-grades are rare - and they are considered proof of the students' exceptional intellectual abilities. This is because pattern learning does not help in the exam. The problems can only be solved by knowledgeably and creatively combining principles of thermodynamics. We have given our latest thermodynamics exam not only to the students but also to OpenAI's most powerful reasoning model, o3, and have assessed the answers of o3 exactly the same way as those of the students. In zero-shot mode, the model o3 solved all problems correctly, better than all students who took the exam; its overall score was in the range of the best scores we have seen in more than 10,000 similar exams since 1985. This is a turning point: machines now excel in complex tasks, usually taken as proof of human intellectual capabilities. We discuss the consequences this has for the work of engineers and the education of future engineers.