Submit a Manuscript to the Journal

Educational Psychology

For a Special Issue on

Unpacking the "Black Box" of AI-assisted Feedback: Students’ feedback perceptions, emotions, actions, and literacies

Abstract deadline
30 November 2023

Manuscript deadline
29 March 2024

Cover image - Educational Psychology

Special Issue Editor(s)

Lan Yang, Department of Curriculum and Instruction, The Education University of Hong Kong
[email protected]

Zi Yan, Department of Curriculum and Instruction, The Education University of Hong Kong
[email protected]

Chee Kit Looi, Department of Curriculum and Instruction, The Education University of Hong Kong
[email protected]

Ming Ming Chiu, Department of Special Education and Counselling, The Education University of Hong Kong
[email protected]

Dragan Gasevic, Department of Human Centred Computing, Monash University
[email protected]

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Unpacking the "Black Box" of AI-assisted Feedback: Students’ feedback perceptions, emotions, actions, and literacies

This special issue brings together empirical perspectives and empirical research across disciplines regarding AI-assisted, AI-generated, or AI-enabled feedback (hereafter summarized as AIF) and student learning. Specifically, these studies will show how AIF systems (e.g., ChatGPT) and conditions enhance students’ feedback perceptions, motivation, and interpretation of feedback, responses, and learning outcomes. With international collective efforts primarily in educational assessment and psychology, we aim to unravel how AIF affects students’ positive psychological development in experiencing, interpreting, and utilizing AIF to promote self-directed learning.

While there is ample evidence to suggest that effective use of feedback can improve student achievement (Hattie's 2017 synthesis work of 80,000 independent studies), little is known about how students perceive, interpret, and use feedback given by external agents (humans and AI-assisted systems) to enhance their learning. To advance feedback research, researchers argue two essential shifts in research paradigms: from focusing on feedback providers to focusing on feedback receivers (e.g., Hattie & Clarke, 2018; Yang, 2021), and from focusing on performance information for receivers to examining a learner-centered feedback process that supports self-directed learning (e.g., Boud & Molloy, 2013; Winstone et al., 2022).

Recently, the prevalence of ChatGPT and other AI-assisted feedback systems highlights the need for further research into their effectiveness (see Bearman et al., 2023) and impact on student learning process and outcomes. However, little empirical research has been done to explore students’ experiences of using AI-assisted feedback systems (e.g., ChatGPT) and the various aspects of AI-assisted feedback investigated from students' viewpoints. This includes examining their perceptions, interpretations, emotions and use of AI-assisted feedback. The special issue aims to address these gaps by examining various aspects of AI-assisted feedback from the students' perspective.

We welcome submissions on the following topics, but is not limited to:

  1. Students' perceptions and interpretations of AI-generated feedback
  2. Students’ emotions in seeking and receiving AI-generated feedback and associated emotion regulation strategies
  3. How students' academic self-concept and motivation shape their responses to AI-generated feedback
  4. How students’ feedback literacy in enabling their feedback seeking through AI-enabled feedback systems and feedback uptake
  5. Comparing AI-assisted feedback and teacher feedback regarding effectiveness, student perceptions, and outcomes
  6. How AI-generated feedback affects student self-regulation, metacognition, learning strategies, and career-related outcomes (e.g., career adaptability and career development self-efficacy)
  7. Ethical considerations and potential biases in AI-assisted feedback systems
  8. Responsible AI-assisted feedback systems: design, implementation, and effectiveness in enhancing student learning
  9. Challenges and future directions for research on AI-assisted feedback in educational assessment designs
  10. Reviews by using a systematic approach and meta-analysis techniques to examine the topic of this SI


Bearman, M., Ajjawi, R., Boud, D., Tai, J. & Dawson, P. (2023). CRADLE Suggests… assessment and genAI. Centre for Research in Assessment and            Digital Learning, Deakin University, Melbourne, Australia. doi:10.6084/ m9.figshare.22494178

Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment & Evaluation in higher education, 38(6), 698-712.

Hattie, J. (2017). Backup of Hattie’s Ranking list of 256 influences and effect sizes related to student achievement. Visible Learning.

Hattie, J., & Clarke, S. (2018). Visible learning: Feedback. Routledge.

Winstone, N., Boud, D., Dawson, P., & Heron, M. (2022). From feedback-as-information to feedback-as-process: a linguistic analysis of the feedback literature. Assessment & Evaluation in Higher Education, 47(2), 213-230.

Yang, L. (2021). The role of feedback orientation in converting external feedback to learning opportunities for implementing assessment-as-learning in the context of feedback. In Z. Yan & L. Yang (Eds.), Assessment as learning: Maximising opportunities for student learning and achievement (pp. 53–75). Routledge.

Submission Instructions

We invite researchers from various disciplines, including educational psychology, educational assessment, educational technology, and computer science and associated fields to contribute original research articles. Experimental and quasi-experimental research designs are urgently needed to reliably evaluate AIF systems' impact and effectiveness on students' interpretations, emotions, and use of feedback. We also welcome mixed-method research combining quantitative measurements with in-depth qualitative insights. Purely qualitative research designs may not be considered given this journal's methodological scope. All submissions should follow the journal's author guidelines and will undergo rigorous peer review to ensure quality and relevance. We anticipate valuable contributions will advance our understanding of how AI-assisted feedback can be effectively and ethically used to promote students’ learning.

Abstract submission

Interested authors are invited to submit their abstracts online to Dr. Lan Yang, Prof. Zi Yan, Prof. Chee Kit Looi, Prof. Ming Ming Chiu and Prof. Dragan Gasevic, Guest Editors of the Special Issue c/o Educational Psychology by email: [email protected]. Please entitle the email subject as ‘Abstract Submission: Special Issue on The AI-assisted Feedback: Students' feedback perceptions, emotions, actions, and literacies.

  • Please attach the abstract in a separate file (dox/docx/pdf).
  • Please include the full list of author(s), primary affiliation and primary contacting email.
  • No word limit at this stage but the submission system has a word limit of 150 words.

Instructions for AuthorsSubmit an Article