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In the wake of ChatGPT: early reflections on marking open-book online accounting assessments

Oluwatoyin Esther Dosumu et al.

Accounting Education2025https://doi.org/10.1080/09639284.2025.2487487article
AJG 2ABDC A
Weight
0.46

Abstract

The release of ChatGPT-3.5 as a large-scale, easily accessible Generative Artificial Intelligence (GenAI) tool for students to use makes it harder for accounting academics to ignore the ‘wicked problem’ that AI potentially creates for disruption in accounting education. We used a retrospective collaborative autoethnographic (RCA) methodology and critical reflection theory in our reflective perceptions-based study of the marking process for an open-book online accounting exam that took place in January 2023. Concerned about the impact of ChatGPT-3.5 on student answers, we drew on our collective experiences to manage the uncertainty we felt, drawing up some guidelines to help us navigate through the marking process. We did not perceive high use of ChatGPT-3.5, noting that it was not capable of producing high-quality answers and finding that exam marks were lower than in previous years. Given the rapid improvements in GenAI tools since then, we reflect on how accounting assessment should embrace the new educational paradigm that a powerful AI revolution imposes. We consider how to manage continuously evolving disruptive AI for accounting assessment going forward and include a brief reflection of changes we have made to incorporate AI in teaching, learning and assessment on our module for following years.

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https://doi.org/https://doi.org/10.1080/09639284.2025.2487487

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@article{oluwatoyin2025,
  title        = {{In the wake of ChatGPT: early reflections on marking open-book online accounting assessments}},
  author       = {Oluwatoyin Esther Dosumu et al.},
  journal      = {Accounting Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/09639284.2025.2487487},
}

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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