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A Cross-Disciplinary Academic Evaluation of Generative AI Models in HR, Accounting, and Economics: ChatGPT-5 vs. DeepSeek

Author

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  • Najib Bou Zakhem

    (Management & International Management Department, School of Business, Lebanese International University, Bekaa 146404, Lebanon)

  • Malak Bou Diab

    (Accounting Information Systems Department, School of Business, Lebanese International University, Beirut 146404, Lebanon)

  • Suha Tahan

    (Economics Department, School of Business, Lebanese International University, Bekaa 146404, Lebanon)

Abstract

As generative AI is being further integrated into academic and professional contexts, there is a demonstrable need to determine the performance of generative AI within specific, applied domains. This research compares the performances of ChatGPT-5 and DeepSeek on tasks in the domains of accounting, economics, and human resources. The models were provided two prompts per domain, and outputs were evaluated by academics across five criteria: accuracy, clarity, conciseness, systematic reasoning, and indicators of potential bias. The inter-rater reliability was reported using Cohen’s Kappa. From the findings, both models display differences in performance. ChatGPT-5 outperformed DeepSeek in accounting and human resources, while DeepSeek outperformed ChatGPT-5 on epistemic economics tasks. Since results have shown that ChatGPT-5 outperformed DeepSeek in two out of three domains, the research recommends a reliability-based framework to compare generative AI outputs within business disciplines and offers practical suggestions on when and how to use the models within academic and professional contexts.

Suggested Citation

  • Najib Bou Zakhem & Malak Bou Diab & Suha Tahan, 2025. "A Cross-Disciplinary Academic Evaluation of Generative AI Models in HR, Accounting, and Economics: ChatGPT-5 vs. DeepSeek," Administrative Sciences, MDPI, vol. 15(11), pages 1-22, October.
    • Handle: RePEc:gam:jadmsc:v:15:y:2025:i:11:p:412-:d:1778653
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