# Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters

arXiv: https://arxiv.org/abs/2308.06088 · HTML: https://mlsanity.com/paper/2308.06088/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark](https://mlsanity.com/paper/2304.11085/) (2023)
- [GPT-3-driven pedagogical agents for training children's curious question-asking skills](https://mlsanity.com/paper/2211.14228/) (2022)
- [Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education](https://mlsanity.com/paper/2301.08771/) (2023)
- [Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?](https://mlsanity.com/paper/2104.12920/) (2021)
- [Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation](https://mlsanity.com/paper/2306.06386/) (2023)

## Built on by (most cited first, 16 of 16)

- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Fine-tuning ChatGPT for Automatic Scoring](https://mlsanity.com/paper/2310.10072/) (2023)
- [Multimodality of AI for Education: Towards Artificial General Intelligence](https://mlsanity.com/paper/2312.06037/) (2023)
- [Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring](https://mlsanity.com/paper/2411.16337/) (2024)
- [Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course](https://mlsanity.com/paper/2407.05216/) (2024)
- [Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education](https://mlsanity.com/paper/2401.00832/) (2024)
- [Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring](https://mlsanity.com/paper/2407.18328/) (2024)
- [Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns](https://mlsanity.com/paper/2502.15140/) (2025)
- [Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large](https://mlsanity.com/paper/2405.05444/) (2024)
- [Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols](https://mlsanity.com/paper/2502.12842/) (2025)
- [Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs](https://mlsanity.com/paper/2404.18978/) (2024)
- [MiRAGE: Misconception Detection with Retrieval-Guided Multi-Stage Reasoning and Ensemble Fusion](https://mlsanity.com/paper/2511.01182/) (2025)
- [Enhancing LLM-Based Data Annotation with Error Decomposition](https://mlsanity.com/paper/2601.11920/) (2026)
- [Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis](https://mlsanity.com/paper/2508.03396/) (2025)
- [Thinking Like a Student: AI-Supported Reflective Planning in a Theory-Intensive Computer Science Course](https://mlsanity.com/paper/2511.01906/) (2025)
- [KidsArtBench: Multi-Dimensional Children's Art Evaluation with Attribute-Aware MLLMs](https://mlsanity.com/paper/2512.12503/) (2025)

## Abstract (arXiv)

Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = .60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.