OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education

In response to generative AI, educational researchers and developers are exploring novel ways to adaptively support collaborative learning using this new type of machine learning model (usually LLMs). To determine how best to design effective support, and to establish for which learning outcomes such support has proven beneficial, we analysed a broad sample of studies. Overall, we identified and illustrated the potential roles of GenAI support in collaborative learning settings – often higher education settings. Upon closer scrutiny of the studies, we saw that GenAI systems were often designed to assume the role of a tutor or facilitator, or to function as a repository of information (akin to a search engine), while the role of artificial group members was explored less frequently. The support targeted different aspects of the collaboration in order to promote collaborative learning, for example by providing domain-specific information, facilitating discussions, eliciting reflective thinking, or nudging equal participation of all group members. Moreover, a significant proportion of the studies ultimately relied on human intelligence to implement evaluation criteria and to specify rules to govern the desired pedagogical behaviour of the generative AI (rather than relying on the LLM to make suggestions).

Given the small number of studies that have systematically investigated the benefits of GenAI support for knowledge acquisition, it is currently somewhat difficult to determine how these potentials of GenAI to support collaborative learning can best be leveraged. In terms of promoting the acquisition of domain-specific knowledge, results are mixed, with two of the three studies that focused on domain-specific knowledge as an outcome finding small to medium effects, although these studies targeted a very specific domain, namely computer programming (Lin et al., 2024; Naik et al., 2024; Naik et al., 2024). Studies that focused on learning to collaborate – instead of collaborating to learn – were especially rare. However, Darmawansah et al. (2025) reported promising findings with respect to supporting students’ argumentation skills.

Our overview shows that the potentials of GenAI are indeed being explored, and conceptual and empirical work continues. Previous research on collaborative learning has already accumulated a wealth of insights into boundary conditions for effective collaboration. These insights represent a fruitful basis for continuing research on adaptive support that leverages GenAI, especially LLMs. Most importantly, collaborative learning scenarios should create social interdependence (Johnson and Johnson, 2009), while support is most beneficial when it can elicit interaction processes cognitive, metacognitive, motivation or affective processes that benefit the knowledge construction in the group.

Conceptually, it is important to discuss which roles GenAI can realistically play in the process of detecting and diagnosing collaboration processes, how it can generate adequate instructional support, and how it may be integrated into existing well-researched approaches to supporting collaboration. Empirically, it will be beneficial to explore whether GenAI models actually provide the support that designers are expecting, whether the supporting actions are in line with beneficial actions identified by previous research, and whether supporting actions by GenAI elicit beneficial collaboration processes. Ideally, the conceptual and empirical perspectives should be combined in order to design and test collaboration support. In the final section, we share ideas on designing collaboration support as well as essentials for the design of future studies in this field. In doing so, we hope to encourage researchers and developers to collaborate and to continue charting out the potentials of GenAI for collaborative learning.