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[2602.18891] Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation
Summary
This pilot study explores the orchestration of LLM agents in scientific research, focusing on the generation and evaluation of multiple-choice questions (MCQs). It highlights the shift in researchers' roles and the quality of AI-generated content compared to expert-vetted ques...
Why It Matters
As large language models (LLMs) increasingly influence scientific workflows, understanding their effectiveness in generating educational content is crucial. This study provides empirical evidence on the capabilities and limitations of LLMs in research settings, informing future applications and skill requirements in AI-empowered research.
Key Takeaways
- LLM agents can effectively generate high-quality MCQs, but they fall short of expert-vetted standards.
- The role of researchers is evolving from content creation to orchestration and validation of AI outputs.
- Quality assessment reveals strengths in surface-level attributes but weaknesses in cognitive engagement and difficulty calibration.
- The study emphasizes the need for new skills in AI research operations to manage AI-driven workflows.
- Understanding the limitations of AI-generated content is essential for integrating LLMs into educational practices.
Computer Science > Computers and Society arXiv:2602.18891 (cs) [Submitted on 21 Feb 2026] Title:Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation Authors:Yuan An View a PDF of the paper titled Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation, by Yuan An View PDF HTML (experimental) Abstract:Advances in large language models (LLMs) are rapidly transforming scientific work, yet empirical evidence on how these systems reshape research activities remains limited. We report a mixed-methods pilot evaluation of an AI-orchestrated research workflow in which a human researcher coordinated multiple LLM-based agents to perform data extraction, corpus construction, artifact generation, and artifact evaluation. Using the generation and assessment of multiple-choice questions (MCQs) as a testbed, we collected 1,071 SAT Math MCQs and employed LLM agents to extract questions from PDFs, retrieve and convert open textbooks into structured representations, align each MCQ with relevant textbook content, generate new MCQs under specified difficulty and cognitive levels, and evaluate both original and generated MCQs using a 24-criterion quality framework. Across all evaluations, average MCQ quality was high. However, criterion-level analysis and equivalence testing show that generated MCQs are not fully comparable to expert-vetted baseline question...
