Course project reports (CPRs) are vital for evaluating student learning outcomes, yet manual assessment is inefficient and subjective. Large language models (LLMs) have been applied to automated essay scoring but focus only on writing proficiency, neglecting critical thinking and practical competencies. To address this gap, this paper introduces PEG-Prompt, a novel prompting framework integrating the Paul-Elder critical thinking model. It assesses CPRs from six dimensions: structure, logic, coherence, originality, citation, and knowledge proficiency. The authors further optimize the framework using report key content extraction and few-shot scoring examples. A dedicated PEG-CPR dataset with 110 anonymized reports is constructed for validation. Experiments on four mainstream LLMs demonstrate that PEG-Prompt consistently reduces scoring errors and enhances alignment with human evaluations. Statistical and visualization analyses confirm significant performance improvements. The optimized approach produces detailed, human-like feedback supporting both formative student reflection and summative course assessment. This work advances AI-powered educational evaluation by combining critical thinking assessment with LLM prompt engineering.
This framework guides LLMs in evaluating CPR with respect to practical competencies, analytical reasoning, and writing skills while generating targeted feedback. This feedback facilitates students’ ability to reflect on their results and improve in their weaker areas, thereby contributing to the cultivation of higher-order intellectual traits.

The work entitled “Evaluating the Efficacy of a Multifaceted Prompt for Use with LLMs to Evaluate Course Project Reports” was published on Frontiers of Digital Education (published on April 25, 2026).
DOI:10.1007/s44366-026-0086-y