Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation

The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity and difficulty, and generating new items guided by learning goals. The research spans four university courses—two theory-focused and two application-focused—covering diverse cognitive levels according to Bloom’s taxonomy. A balanced dataset ensures representation of question categories and structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, and a fine-tuned LLM—are developed and evaluated against a meta-evaluator, supervised by human experts, to assess alignment accuracy and explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights for integrating generative AI into university assessment processes. Beyond exam-specific applications, this methodology provides a foundational approach for the broader adoption of AI in post-secondary education, emphasizing fairness, contextual relevance, and collaboration. The findings offer a comprehensive framework for aligning AI-generated content with learning objectives, detailing effective integration strategies, and addressing challenges such as bias and contextual limitations. Overall, this work underscores the potential of generative AI to enhance educational assessment while identifying pathways for responsible implementation.