By skyforbes 
This study investigates how ChatGPT can aid in evaluating laboratory reports in Experimental Physics through two distinct interaction modalities: an automated API-based evaluation and a customized ChatGPT setup mimicking instructor feedback.
Key insights from the research include:
- Consistent Formal Feedback: ChatGPT demonstrated strong performance in assessing structural and formal aspects of lab reports, consistently recognizing the adherence to scientific conventions such as clarity in objectives and proper organization.
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Variable Technical Accuracy: While effective in formal evaluation, the model struggled with the technical reasoning and interpretation of experimental data. Instances of incorrect feedback were identified, reflecting limitations in its ability to analyze certain mathematical and graphical content.
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Modal Limitations: The automated API mode exhibited challenges in processing non-textual information like diagrams and equations, leading to incomplete or inaccurate evaluations. The customized mode provided richer feedback but lacked reproducibility.
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Teacher Supervision Essential: The study emphasizes the necessity of instructor oversight to ensure the accuracy of physical reasoning and experimental interpretations, suggesting that AI should complement, rather than replace, human evaluators.
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Potential for Insight: The ability of ChatGPT to highlight trends in students’ reporting weaknesses could assist educators in making informed instructional changes for future courses.
Overall, the research underscores the potential of AI like ChatGPT to enhance existing assessment practices in physics while recognizing the critical role of human judgment in interpreting its feedback.
Explore the full breakdown here: Here
Read the original research paper here: Original Paper