Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Exploring the potential of ChatGPT for feedback and evaluation in experimental physics

Marcos Abreu, Álvaro Suárez, Cecilia Stari, Arturo C. Marti

Abstract

This study explores how generative artificial intelligence, specifically ChatGPT, can assist in the evaluation of laboratory reports in Experimental Physics. Two interaction modalities were implemented: an automated API-based evaluation and a customized ChatGPT configuration designed to emulate instructor feedback. The analysis focused on two complementary dimensions-formal and structural integrity, and technical accuracy and conceptual depth. Findings indicate that ChatGPT provides consistent feedback on organization, clarity, and adherence to scientific conventions, while its evaluation of technical reasoning and interpretation of experimental data remains less reliable. Each modality exhibited distinctive limitations, particularly in processing graphical and mathematical information. The study contributes to understanding how the use of AI in evaluating laboratory reports can inform feedback practices in experimental physics, highlighting the importance of teacher supervision to ensure the validity of physical reasoning and the accurate interpretation of experimental results.

Exploring the potential of ChatGPT for feedback and evaluation in experimental physics

Abstract

This study explores how generative artificial intelligence, specifically ChatGPT, can assist in the evaluation of laboratory reports in Experimental Physics. Two interaction modalities were implemented: an automated API-based evaluation and a customized ChatGPT configuration designed to emulate instructor feedback. The analysis focused on two complementary dimensions-formal and structural integrity, and technical accuracy and conceptual depth. Findings indicate that ChatGPT provides consistent feedback on organization, clarity, and adherence to scientific conventions, while its evaluation of technical reasoning and interpretation of experimental data remains less reliable. Each modality exhibited distinctive limitations, particularly in processing graphical and mathematical information. The study contributes to understanding how the use of AI in evaluating laboratory reports can inform feedback practices in experimental physics, highlighting the importance of teacher supervision to ensure the validity of physical reasoning and the accurate interpretation of experimental results.
Paper Structure (16 sections, 1 figure, 1 table)

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Research Design and Implementation
  3. Framework and assessment procedure
  4. AI configuration, protocol, and analysis
  5. Results
  6. Comparison of scores
  7. Analysis of AI feedback
  8. Objetives
  9. Theoretical background
  10. Experimental setup
  11. Data analysis
  12. Conclusions
  13. Overall assessment
  14. Exploratory analysis in conversational mode
  15. Discussion
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Relationship between the scores assigned in the instructor grading and those generated by the AI system (batch grading via API). Each point represents an evaluated report ($N = 57$). Spearman’s rank correlation coefficient: $\rho = 0.38$.