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The effects of generative artificial intelligence (GenAI) on learning in an accounting data analytics course

Author

Listed:
  • Blondeel, Eva
  • Bullock, Taylor
  • Gaskin, James
  • Schuetzler, Ryan
  • Serre, Rachel
  • Steffen, Jacob
  • Wells, Taylor M.
  • Wood, David A.

Abstract

We study how generative AI (GenAI) relates to learning in an accounting data analytics course. Using survey data from 312 junior-level students (Fall 2023), we examine help-seeking preferences (ChatGPT vs. professors/TAs/peers) and whether ChatGPT expertise is associated with course performance. Students preferred seeking help from group members most, but consulted ChatGPT more than professors or TAs, citing convenience and comfort. ChatGPT was perceived as less useful and satisfying than human support when accuracy was paramount. Regression results show that ChatGPT expertise is positively associated with course GPA, especially for students with lower prior GPAs. Findings imply that modest, structured training can help students—particularly lower-performing students—use GenAI productively. We discuss limitations (context specificity, self-report, LLM-assisted coding) and recommend scaffolding responsible GenAI use across accounting curricula.

Suggested Citation

  • Blondeel, Eva & Bullock, Taylor & Gaskin, James & Schuetzler, Ryan & Serre, Rachel & Steffen, Jacob & Wells, Taylor M. & Wood, David A., 2025. "The effects of generative artificial intelligence (GenAI) on learning in an accounting data analytics course," Journal of Accounting Education, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:joaced:v:72:y:2025:i:c:s0748575125000387
    DOI: 10.1016/j.jaccedu.2025.100987
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