arXiv subjects

Nikhil Sanjay Borse

Publications and source records attributed to Nikhil Sanjay Borse.

2 recordsLinked to original sources

arXiv2026

Probing AI-generated physics solutions and preparing students to critique them

This study examines Artificial Intelligence (AI)-generated physics solutions from two connected perspectives: how prompt design shapes these solutions and how students can be prepared to critique them. Using a rotational-mechanics problem, we adapted a problem-classification framework to examine prompt variations, evaluating OpenAI's o4-mini responses with the Minnesota Assessment of Problem Solving (MAPS) rubric. Well-specified prompts improved solution completeness; underspecified and multimodal prompts exposed weaknesses in physics reasoning and correctness. In the student-evaluation phase, 24 introductory physics lab groups evaluated an o4-mini solution to this problem after either independently solving a related problem or critiquing its AI-generated solution with MAPS-based reflection questions. Problem-solving-only groups exhibited uncritical or misconception-based critiques; MAPS-guided groups identified more expert-aligned issues, including skipped numerical procedures and undefined notation. Together, our findings contribute to physics education research by showing how AI-generated solutions can ground both model-reasoning benchmarks and improved student critique of that reasoning through MAPS-based reflection.

physics.ed-ph↗

arXiv2025

Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis

Qualitative analysis is typically limited to small datasets because it is time-intensive. Moreover, a second human rater is required to ensure reliable findings. Artificial intelligence tools may replace human raters if we demonstrate high reliability compared to human ratings. We investigated the inter-rater reliability of state-of-the-art Large Language Models (LLMs), ChatGPT-4o and ChatGPT-4.5-preview, in rating audio transcripts coded manually. We explored prompts and hyperparameters to optimize model performance. The participants were 14 undergraduate student groups from a university in the midwestern United States who discussed problem-solving strategies for a project. We prompted an LLM to replicate manual coding, and calculated Cohen's Kappa for inter-rater reliability. After optimizing model hyperparameters and prompts, the results showed substantial agreement ($κ>0.6$) for three themes and moderate agreement on one. Our findings demonstrate the potential of GPT-4o and GPT-4.5 for efficient, scalable qualitative analysis in physics education and identify their limitations in rating domain-general constructs.

physics.ed-ph↗