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How generative AIs support selection and evaluation in complex decision tasks: insights from academic paper review

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  • Hao Yu
  • Ye Hou
  • Yuxian Liu
  • Yuan Li

Abstract

This study investigates the role of generative large language models (GLLMs) in supporting complex selection and evaluation tasks within the academic paper review process. Using empirical data from management journal submissions, we compared the performance of six leading GLLMs (Claude 3.5, GPT-4O, Gemini 2.5, Deepseek-R3, Moonshot-V1 (kimi), and Qwen-Long) against human editors and reviewers. The results show that, at the editorial screening stage, GLLMs can help editors identify manuscripts with low publication potential, with aggregated model scores closely matching human editorial decisions. At the review stage, comments generated by the union of any three GLLMs from six GLLMs can cover over 61% of issues raised by human reviewers and are rated as superior by management professors. These findings demonstrate that GLLMs can complement human judgment in multi-stage, knowledge-intensive decision processes, improving both the efficiency and quality of academic paper reviews. The study expands the application boundaries of generative AI in management research evaluation and offers practical insights for integrating GLLMs into scholarly review workflows.

Suggested Citation

  • Hao Yu & Ye Hou & Yuxian Liu & Yuan Li, 2025. "How generative AIs support selection and evaluation in complex decision tasks: insights from academic paper review," Journal of Management Analytics, Taylor & Francis Journals, vol. 12(3), pages 435-449, July.
    • Handle: RePEc:taf:tjmaxx:v:12:y:2025:i:3:p:435-449
    DOI: 10.1080/23270012.2025.2537410
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