Written by Omid Noroozi, Christian Schunn, Bertrand Schneider, Seyyed Kazem Banihashem
In higher education, peer learning addresses a persistent challenge: how to support large and diverse student populations when individualized teacher feedback is not feasible. Just as importantly, it can be an effective learning strategy by promoting deep engagement and critical thinking. Through peer learning, students have the opportunity to review each other’s work, exchange and debate ideas, question assumptions, and gradually develop the ability to judge quality, justify claims, collaborate productively, provide feedback, negotiate meaning, and co-construct knowledge. However, in practice, the implementation of peer learning often falls short. Many students struggle to provide meaningful feedback, doubt the fairness of peer review, feel anxious about critique, or participate only superficially. Without thoughtful scaffolding, peer learning risks becoming administratively efficient but educationally shallow.
The IJETHE collection “Technological Innovations for Facilitation of Peer Learning Processes and Outcomes”(link) addresses these critical challenges by exploring how technology can effectively support such practices. In particular, the collection aims to advance both theoretical and practical understanding of how advanced technologies such as artificial intelligence (AI), learning analytics, virtual and augmented reality, and multimodal systems can be pedagogically designed to enhance peer learning in higher education. Therefore, the collection asks a deeper question: what kind of peer learning should universities cultivate in an age of intelligent systems?
From Supporting Peer Learning to Reshaping it
Across the six studies in this collection, a clear pattern emerges: technologies are no longer just facilitating peer learning; they are also contributing to its restructuring. Findings from Topping et al. (2025) show that AI-supported peer assessment systems can assign reviewers, generate feedback, and support grading; learning analytics can detect interaction patterns and guide collaboration; multimodal tools can capture gestures and embodied engagement; and dashboards can visualize group processes in real time. These developments are powerful, and they allow peer learning to become more visible, scalable, and responsive. For example, gamified environments combined with analytics can foster deeper engagement and knowledge construction (Moon et al., 2024), while social comparison feedback can stimulate reflection and more active participation (Lu et al., 2024). For universities struggling to maintain high-quality interaction with limited resources, such innovations are understandably attractive.
Yet the collection also surfaces a critical tension. Technology may improve the management of peer learning without necessarily improving the quality of learning itself. In some cases, students gain awareness of effective collaboration without actually enacting it, suggesting a gap between knowing and doing. More importantly, the increasing role of AI raises a deeper concern: if systems begin to generate, structure, or optimize feedback, what happens to students’ own development of evaluative judgment? If peer learning becomes increasingly optimized by intelligent systems, then universities must confront a fundamental pedagogical question: are we educating students to think with others, or training them to depend on technologies that think for them? This challenge, if not properly addressed, could gradually erode one of higher education’s core aims: cultivating independent judgment through engagement with others.
This concern echoes wider debates beyond IJETHE. UNESCO has argued that AI in education should enhance human agency rather than diminish it (Holmes & Miao, 2023). OECD (2026) continues to emphasize collaboration, critical thinking, and social intelligence as essential capabilities for the future. If these are the very skills universities aim to cultivate, then peer learning becomes more, not less, important in the AI era.
The danger is therefore not technology itself. The danger is designing increasingly sophisticated systems without sufficient clarity about the educational purposes they should serve. Acknowledging this risk, the editorial of this collection carefully argues that AI should complement, not replace, human judgment and must be pedagogically grounded (Noroozi et al., 2025).
Towards the Future: Pedagogical and not Technological
Perhaps the most important contribution of this collection, and its core message, is on its insistence that the future of peer learning is not primarily a technological challenge, but a pedagogical one. Despite the sophistication of current tools, the field still lacks robust guidance on aligning technological affordances with pedagogical principles for peer learning. The editorial (Noroozi et al., 2025) identifies several critical gaps driven by the collections that reinforce this point. For example, a dominance of short-term interventions, limited evidence of long-term impact, overemphasis on student-focused designs, insufficient attention to teachers’ role in peer learning, a lack of diversity and equity considerations, and fragmented theoretical grounding.
These are not minor limitations; they point to a field that is advancing technologically faster than it is theoretically and pedagogically. As a result, there is a risk of designing increasingly sophisticated systems without fully understanding what kinds of peer learning we want to cultivate.
The collection, therefore, calls for a shift in peer learning: toward more inclusive, context-sensitive, and theory-informed designs that foreground student agency and long-term development. It also highlights the need to rethink technologies not merely as tools for analysis or automation, but as scaffolds that empower learners.
In this sense, the collection offers more than a package of innovations; it offers a direction. Peer learning is evolving into a space where students and intelligent systems interact continuously. The challenge ahead is not to maximize efficiency or automation, but to ensure that these systems strengthen, rather than replace, the fundamental students’ learning with and from others.
Bibliography
Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. Unesco Publishing.
Lu, Y., Ma, N., & Yan, W. Y. (2024). Social comparison feedback in online teacher training and its impact on asynchronous collaboration. International Journal of Educational Technology in Higher Education, 21(1), 55.
Moon, J., McNeill, L., Edmonds, C. T., Banihashem, S. K., & Noroozi, O. (2024). Using learning analytics to explore peer learning patterns in asynchronous gamified environments. International Journal of Educational Technology in Higher Education, 21(1), 45.
Noroozi, O., Schunn, C., Schneider, B., & Banihashem, S. K. (2025). Advancing peer learning with learning analytics and artificial intelligence. International Journal of Educational Technology in Higher Education, 22(1), 62.
OECD (2026). OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education, OECD Publishing, Paris, https://doi.org/10.1787/062a7394-en.
Topping, K. J., Gehringer, E., Khosravi, H., Gudipati, S., Jadhav, K., & Susarla, S. (2025). Enhancing peer assessment with artificial intelligence. International Journal of Educational Technology in Higher Education, 22(1), 3.