Wzorce rekomendacji dydaktycznych generowanych przez sztuczną inteligencję w nauczaniu rachunkowości – analiza jakościowa odpowiedzi modelu ChatGPT
Patterns of AI-generated teaching recommendations in accounting education: A qualitative analysis of ChatGPT responses

Summary/Abstract: Purpose: The aim of the paper is to identify patterns of teaching recommendations generated by generative artificial intelligence (AI) tools in relation to the design of accounting education in higher education. Methodology/approach: The study employs a qualitative research approach based on an analysis of responses generated by the ChatGPT language model to a set of structured prompts concerning instructional design. The analysis covered nine accounting courses. The empirical material was coded and analysed using qualitative content analysis supported by MAXQDA software. Findings: The results indicate that the recommendations generated by the model most frequently concern student-active teaching methods, particularly teamwork, case analysis, and moderated discussion. In terms of assessment, the model’s recommendations most frequently point to evaluation methods that allow for the verification of students’ reasoning, such as open-ended questions and tasks requiring justification of answers. AI is primarily presented as a tool to support analysis and reflection rather than replacing students’ cognitive activity. Research limitations/implications: The study is exploratory and based on AI-generated content; therefore, the findings do not constitute an empirical evaluation of the effectiveness of the proposed teaching methods. Originality/value: The paper contributes to the literature on generative AI in higher education by identifying patterns of AI-generated teaching recommendations and interpreting them within the context of learning theories, understood as a set of concepts that explain the mechanisms of knowledge acquisition, as well as the roles of the learner and the teacher in the educational process.

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