Artificial Intelligence Use in Feedback: A Qualitative Analysis


  • Toh Yen Pang RMIT University, Australia
  • Alex Kootsookos RMIT University, Australia
  • Chi-Tsun Cheng RMIT University, Australia



AI, Artificial Intelligence, Qualitative Method, Feedback, Higher Education, ChatGPT, Bard


Feedback, particularly the formative or ‘feed-forward’ type is important for students in higher education to understand their errors and improve their expression and clarity of ideas. While technology-assisted feedback modes, e.g., audio or video are prevalent, ensuring their efficacy and succinctness, particularly for non-English-speaking background (NESB) educators can be challenging. This study investigates the attitudes and experiences of NESB educators in the School of Engineering of RMIT University, with a focus on their use of AI-assisted tools for providing feedback to students in higher education settings. Utilising a survey, the researchers examined how personal and linguistic attributes influenced feedback strategies and explored the educators' perspectives on integrating AI tools, such as ChatGPT and BARD, in their teaching practice and to enhance student engagement with the feedback they received. Through thematic analysis the findings reveal that personal background and linguistic proficiency significantly influenced the provision of feedback. Furthermore, even though educators had different levels of familiarity with AI-assisted tools, there was a general consensus on the potential utility of these tools for improving feedback provision. These will require targeted staff training, careful human oversight to ensure quality and avoid bias, and customised AI training to align feedback with individual teaching styles.


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How to Cite

Artificial Intelligence Use in Feedback: A Qualitative Analysis. (2024). Journal of University Teaching and Learning Practice, 21(06).