Special Issue 2026 - Call for submissions

Call for submissions

Special Issue: AI-assisted Feedback in Tertiary Education: Mechanisms, Effectiveness, and Motivational Pathways

Guest Editors:

Shen Ba (The Education University of Hong Kong)

Lan Yang (The Education University of Hong Kong)

Zi Yan (The Education University of Hong Kong)

Chee Kit Looi (The Education University of Hong Kong)

Dragan Gašević (Monash University)

 

Special Issue: Background and Rationale

AI-assisted feedback (AIFB) has rapidly expanded across tertiary and post-school education, driven by advances in learning analytics, intelligent tutoring systems, and more recently, generative AI. Empirical studies increasingly report positive associations between AIFB and learning outcomes. However, the field remains conceptually fragmented, with limited accumulation of evidence that systematically tests how and why AIFB works across contexts and across diverse theoretical, methodological, and disciplinary traditions.

A recent systematic literature review of 129 empirical studies (Ba et al., 2025) synthesised AIFB literature (2014-2023) and identified recurring design features, feedback mechanism, and learner processes across studies, proposing an integrated conceptual framework grounded in the Self-System Model of Motivational Development (SSMMD). This framework conceptualises AIFB as a learning environment characterised by macro-level mechanisms (data sources and AI models), meso-level mechanisms (feedback focus: task, process, self-regulation, self), and micro-level mechanisms (feedback complexity: basic, intermediate, elaborated), which shape learners’ perceptions, actions, and learning outcomes. Importantly, this synthesis reflects patterns emerging from diverse empirical traditions rather than prescribing a single theoretical approach.

While the review documents promising trends, it also highlights a critical gap: relatively few studies have explicitly tested this integrated framework or examined mechanism-to-process-to-outcome pathways in a theory-informed manner. Evidence remains uneven across learner actions (e.g., self-, co-, and socially shared regulation), motivational processes, and comparative feedback designs. Moreover, limited integration exists across competing or complementary theoretical perspectives.

This Special Issue aims to consolidate and advance the field by inviting scholarly work that addresses conceptual fragmentation and strengthens cumulative knowledge by testing, refining, or extending theory-informed accounts of AIFB mechanisms, learning processes, and outcomes, including but not limited to synthesis-based models.

Through this special issue we seek to:

  1. Advance theory-informed empirical testing of AIFB mechanisms grounded in diverse and complementary conceptual frameworks.
  2. Examine learner-centred pathways, linking AIFB environments to learner perceptions, regulatory actions, and learning outcomes across contexts and populations.
  3. Improve methodological clarity and transparency in AIFB research, including reporting of AI models, feedback design features, and analytic strategies.
  4. Support responsible and context-sensitive adoption of AIFB in tertiary and post-school education.

 

We are inviting scholarly work related to these themes:

1. Design Features, Technological Capabilities, and Feedback Processes in AIFB

Studies examining how AI system features, data sources, algorithms, interface design, and feedback generation processes shape learners’ engagement, understanding, and use of feedback, including but not limited to mechanism-based analyses.

2. Learner engagement, motivation, and regulation in AIFB contexts

Empirical investigations of how AIFB influences learner perceptions, emotions, motivation, agency, self-, co-, and socially shared regulation, and feedback uptake across diverse theoretical perspectives and learning contexts.

3. Feedback quality, personalization, and learning impact in AI-assisted Environments

Comparative and evaluative studies examining the quality, adaptivity, personalization, and pedagogical value of AI-generated feedback, including task-, process-, self-regulation-, and self-level feedback, as well as varying levels of feedback elaboration.

4. Implementation, effectiveness, and impact of AIFB

Empirical studies evaluating the design, implementation, scalability, and effectiveness of AIFB systems, instructional models, or institutional initiatives across tertiary and post-school settings.

5. Learning, motivation, and human-AI interaction processes

Empirical research examining how AIFB shapes learners’ cognitive, motivational, emotional, and behavioral processes, including engagement, agency, feedback uptake, and regulatory activity.

6. Methodological innovation in researching AIFB

Empirical studies employing/advancing mixed methods, learning analytics, longitudinal designs, or multi-site data to strengthen inference and generalizability.

7. Generative and multimodal AI for educational feedback

Empirical investigations of large language models and multimodal AI tools used to generate, personalize, or scaffold feedback.

8. Personalization, equity, and inclusivity in AIFB

Empirical research examining adaptive feedback, fairness, accessibility, and differential effects across diverse learner groups and contexts.

9. Replication, extension, and model-testing studies

Replication or extension studies that test the robustness, boundary conditions, or transferability of integrated AIFB models.

10. Ethical, practical, and policy dimensions of AIFB

Empirical analyses examining ethical design, transparency, institutional constraints, and responsible implementation of AIFB.

Conceptual papers will be considered only if they make a clear and substantive contribution to theory development or directly support empirical testing, refinement, or replication of AIFB research.

 

Manuscript Submission Instructions

Manuscripts addressing the special issue’s focus should be submitted through the AJET online manuscript submission system. Please review the Author Guidelines and Submission Preparation Checklist carefully, and prepare your manuscript accordingly. Information about the peer review process and criteria is also available for your perusal.

NOTE: When submitting your manuscript, please include a note in the field called ‘Comments for the Editor’ indicating that you wish it to be considered for the special issue. Please direct questions about manuscript submissions to Shen Ba (bas@eduhk.hk).

Deadlines for authors

Submission deadline: 30 June 2026

Decision on manuscripts: September 2026

Revised/final manuscripts: December 2026

Articles ready for copyediting: January 2027

Expected Publication: March/April 2027