🤖 AI Summary
This study addresses the questionable diagnostic validity of automated scoring by large language models (LLMs), which often appears superficially comprehensive. Leveraging the JorGPT dataset, this work pioneers a diagnostic-validity perspective to deconstruct LLMs’ structured outputs. By integrating statistical analyses, including variance inflation factor (VIF) assessment, with natural language processing techniques, it systematically compares human and machine scoring across multidimensional score redundancy, misconception detection rates, and tone bias. The findings reveal systematic deficiencies in LLM scoring, notably high sub-dimension collinearity, low misconception detection rates, and an absence of severity moderation. Furthermore, the analysis demonstrates that LLMs remain reliable primarily within procedural knowledge domains. Ultimately, this research delineates critical boundaries requiring human oversight for human–AI collaborative assessment in higher education.
📝 Abstract
As Large Language Models (LLMs) are increasingly adopted for automated grading and feedback in higher education, their structured outputs, including multi-dimensional rubric scores, detailed feedback comments, and improvement suggestions, create an appearance of thorough analytic evaluation. This study examines whether these outputs deliver what they appear to offer. Using the JorGPT dataset of 3,041 student responses to 50 open-ended computer science questions, scored by both human instructors and three commercial LLMs, we identify three systematic discrepancies between the apparent and actual quality of LLM-generated grading and feedback. The sub-dimension scores are highly correlated (r = 0.82-0.99, VIF up to 45), providing redundant rather than independent diagnostic information. The textual feedback rarely detects student misconceptions (5-7% vs. 15-31% for teachers), functioning as a coverage checklist rather than a diagnostic instrument. The feedback tone remains uniformly positive regardless of response quality, lacking the severity modulation observed in human feedback. Additionally, grading accuracy varies significantly by knowledge domain, with procedural topics most reliable. These findings provide empirically grounded guidance on which aspects of LLM-generated grading and feedback can be relied upon and which require continued human oversight.