Case Study 6: Accountancy in Academia – AI in Accounting Education

7 May, 2026

SCENARIO

You are a Professor of Accounting at a well-regarded university, responsible for curriculum development. The university encourages the adoption of new technologies, and the department has recently licensed a third-party AI platform designed to assist students with accounting exercises, provide instant feedback on assignments, and check for plagiarism. It is hoped that that the availability of a bespoke system that is free to students, will reduce students secret use and increasing reliance on publicly available AI tools which are prone to errors.

While the tool promises personalised learning and reduced grading workloads, you observe several issues during a pilot phase in your advanced financial accounting module:

The AI’s automated feedback on complex case study assignments is often generic and occasionally inaccurate, failing to grasp nuanced arguments or context-specific applications of accounting standards.
The AI plagiarism checker flags several submissions from international students whose sentence structures differ from typical native English patterns, leading to stressful and time-consuming investigations that ultimately find no actual plagiarism.
Students begin to overly rely on the AI for generating answers, potentially hindering the development of their critical thinking and professional judgment skills.
Concerns are raised about the privacy implications of student assignments and data being processed by the external AI vendor – the terms of service are vague regarding data usage for model improvement.
There’s debate among faculty about whether using such AI tools aligns with the professional competencies and ethical scepticism they aim to instil in future accountants.

Ethical Considerations

Integrity

Is it honest and straightforward to present the AI tool to students as a reliable learning aid or assessment tool if its feedback can be inaccurate or its plagiarism checks biased? How should its limitations be communicated to maintain integrity?

Objectivity

Is the AI tool applying assessment or feedback criteria objectively, or does it exhibit biases (e.g., against certain writing styles)? Could faculty reliance on AI grading compromise their objective assessment of student work?

Professional Competence and Due Care

Are educators exercising due care by ensuring the AI tool genuinely supports learning outcomes and fair assessment, rather than hindering them? Do faculty have the competence to evaluate the AI’s suitability, limitations, and outputs effectively? Does using the tool risk failing in the duty to develop students’ own competence?

Confidentiality

Is student data, including potentially sensitive assignment content and personal information, adequately protected and handled confidentially when processed by the third-party AI vendor? Have data privacy implications and compliance (e.g., GDPR) been fully assessed and addressed?

Professional Behaviour

Does the use of this AI tool uphold the academic standards and reputation of the accounting programme and the university? Does it adequately prepare students for the ethical and professional demands of the accountancy profession, including developing their own judgement? Could inappropriate use discredit the educational mission?

Possible Course of Action

Evaluate AI Accuracy: Conduct a systematic evaluation of the AI tool’s accuracy across different types of assignments and compare its feedback/grading against human expert assessment.
Address Bias: Work with the vendor to understand and mitigate any identified biases in the AI algorithms, particularly in plagiarism detection. Implement supplementary checks or alternative tools if necessary.
Define AI’s Role: Clearly define the AI tool’s role as supplementary support, not a replacement for human teaching, feedback, or final assessment. Emphasise critical thinking and caution students against over-reliance.
Ensure Human Oversight: Implement policies requiring faculty review and moderation of AI-generated feedback and plagiarism flags, especially for high-stakes assessments.
Review Data Privacy: Engage the university’s legal and data protection officers to thoroughly review the AI vendor’s data privacy policies and ensure compliance. Seek explicit student consent if required and explore options for data anonymisation or minimisation.
Develop Usage Guidelines: Create clear guidelines for students and faculty on the appropriate and ethical use of the AI tool, including its limitations and potential pitfalls.
Provide Training: Train faculty on how to use the AI tool effectively and ethically, including interpreting its outputs, identifying potential issues, and providing supplementary human feedback.
Establish Appeal Process: Ensure a clear and accessible process exists for students to appeal AI-driven assessments or flags.

Recommendation

Recommend a cautious and phased approach to integrating the AI tool into the accounting curriculum. Its use should be primarily formative (practice and feedback) rather than summative (final grades) until its accuracy and fairness are rigorously validated. Implement strong human oversight mechanisms, ensuring faculty retain final responsibility for assessment. Prioritise transparency with students about the tool’s capabilities and limitations. Crucially, ensure robust data privacy agreements are in place with the vendor, and develop clear ethical usage guidelines for both students and staff. Advocate for ongoing evaluation of the tool’s impact on learning outcomes and professional skill development.