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

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.

Ähnliche Arbeiten