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

Introduction

Collaborating with others in small groups can be effective for learning, fostering not only domain-specific knowledge but also the knowledge and skills necessary for learning and working in teams (e.g. Hattie, 2009; Pai, Sears and Maeda, 2014; Tenenbaum et al., 2020). At the same time, groups encounter challenges during collaboration (e.g. Barron, 2003; Rummel, Spada and Hauser, 2009; Strauß and Rummel, 2021) and efforts have therefore been undertaken to design support that helps groups navigate these challenges. Work within the learning sciences, especially the field of computer-supported collaborative learning (CSCL), has a long-standing tradition of developing and testing computer-based support for collaborative learning. Such work has now been expanded by research exploring the use of machine learning models, which fall under the term “generative artificial intelligence” (GenAI) in the context of collaborative learning. However, a clear picture of the landscape of GenAI in CSCL is currently lacking. This chapter therefore illustrates how generative AI systems are currently used to support groups during collaborative learning, and analyses their effectiveness in supporting collaborative learning. To this aim, we review a broad sample of current research studies in which GenAI is utilised to support collaborative learning.

To understand how GenAI can be designed to support collaborative learning, and to determine what is required to provide groups with support that is sensitive to their current needs, it is useful to elucidate how learning during collaboration occurs. Therefore, we first provide a brief overview of the core mechanisms underlying collaborative learning and illustrate why groups benefit from support. Drawing on previous work that focused on utilising computer technology to support groups, we then present the most prominent CSCL approaches to supporting groups. This allows us to place GenAI in the broader context of instructional support, to compare it to previous efforts in this field, and to identify the potentials that this technology brings to the table in terms of providing groups with personalised (i.e. adaptive) support.

The main body of this chapter describes how GenAI systems are currently used to provide groups with support. Here, we illustrate the roles that GenAI can play during collaboration (for example serving as a repository of information or as a teacher/tutor), which aspects of the collaboration can be supported by GenAI (for example providing knowledge or fostering social interaction), whether this support affects learning in terms of domain-specific knowledge and knowledge about effective collaboration, and on what grounds GenAI tools determine when and how groups can be supported.

Our review illustrates the potentials of GenAI for supporting collaborative learning and underlines the need for more systematic efforts to determine how GenAI should be designed to support collaboration, especially in terms of how it affects collaboration processes and subsequent learning. Although more research is needed, future efforts to design effective support using GenAI can leverage established insights from learning science research. We conclude the chapter with an outlook, in which we discuss potential ways forward regarding the design of GenAI support, how this technology can be combined with established types of support for groups, and how we can investigate the benefits of GenAI support and deepen our understanding of its impact on collaboration and learning processes. Importantly, we point to several issues that need to be navigated during this process.

Collaborative learning: Collaborating to learn and learning to collaborate

What is collaborative learning?

Collaborative learning can be understood as "a situation in which two or more people learn or attempt to learn something together" (Dillenbourg, 1999) and a “coordinated, synchronous activity that is the result of a continued attempt to construct and maintain a shared conception of a problem” (Roschelle and Teasley, 1995). These definitions demonstrate that collaborative learning can encompass a variety of learning settings in which learners are not merely presented with information but rather actively co-construct knowledge through interaction. Importantly, the interaction between the group members serves the co-construction of knowledge instead of simply creating artifacts such as texts, presentations, computer code, or other artifacts.

Sometimes, the term “collaborative learning” is contrasted with “cooperative learning”: While learning together is understood as “collaborative” when all group members are required to interact with each other in order to achieve their goals, it is understood as “cooperative” when the joint task can be divided into subgoals that can be achieved independently, that is, without the need for knowledge co-construction (i.e. a divide-and-conquer approach). Kreijns et al. (2024) provide a more in-depth discussion of this distinction. In this chapter, we use the “collaborative” learning to refer to different arrangements of learning in small groups, without making this explicit distinction.

The beneficial effects of collaborative learning have been repeatedly demonstrated, both for the acquisition of domain-specific knowledge and its transfer (collaborating to learn: for overviews see Hattie, 2009; Pai, Sears and Maeda, 2015; Tenenbaum et al., 2020) and for the acquisition of collaboration skills (learning to collaborate: for overviews see Radkowitsch, Vogel and Fischer, 2020; Vogel et al., 2017). An important finding from learning science research is that assigning learners to a group, and assigning them a task to solve as a group, does not guarantee learning (Kreijns, Weidlich and Kirschner, 2024). Collaborative learning encompasses the active shared construction of knowledge by the group members (Stahl, Koschmann and Suthers, 2006; Webb, 2013) which occurs through activities such as providing explanations to group members, elaborating on ideas, providing feedback on errors, posing thought-provoking questions, resolving conceptual conflicts, or cognitive modelling (King, 2007). Thus, interaction patterns are key to learning through collaboration (see Dillenbourg et al., 1995, for an “interaction paradigm”).

An important prerequisite for fruitful interaction between the group members is social interdependence, that is, each group member can only achieve their goals if they assist the other group members to achieve their own goals (Johnson and Johnson, 2009). Besides interacting to co-construct knowledge, groups have to perform several other tasks, which lay the groundwork for continuing co-construction, for instance maintaining a shared understanding (common ground, Baker et al., 1999; Clark and Brennan, 1991; Hadwin, Bakhtiar and Miller, 2018), actively participating in discussions (Hrastinski, 2008; Matschke et al., 2014), or regulating the collaboration (Hadwin and Oshige, 2011; Järvelä and Hadwin, 2024). Besides these beneficial processes, research has also identified pitfalls that may hamper collaborative learning (for overviews see Kreijns, Weidlich and Kirschner, 2024; Nokes-Malach et al., 2019; Strauß and Rummel, 2021).

Evidently, collaborative learning is complex and challenging for learners. Therefore, learning science research has explored how to help groups engage in effective interaction that eventually affords learning. In the next section, we provide an overview of the rationales that guide the design of such collaboration support and illustrate the most relevant types of adaptive support, starting with support that does not rely on GenAI. This overview serves as the basis for analysing current implementations of GenAI and discussing future pathways for the development of GenAI support for collaborative learning.

Computer support for collaborative learning: Adaptive scaffolding

Providing groups with adaptive (personalised) support is not a new concept. The earliest efforts in CSCL stem from the 1990s and investigated support that is not adaptive (Jeong, Hmelo-Silver and Jo, 2019; Rummel, 2018). While even such non-adaptive support has been shown to enhance the beneficial effects of collaborative learning (Chen et al., 2018; Jeong, Hmelo-Silver and Jo, 2019; Lou, Abrami and d’Apollonia, 2001; Talan, 2021), adaptive support has been a constant theme in the discourse around collaboration support.

The conceptual basis for providing groups with support that is tailored to their needs is the concept of scaffolding. Scaffolding evokes the metaphor of scaffolds during the construction of buildings, denoting that learners are given the means with which to accomplish hitherto unattainable learning tasks (Kollar et al., 2025; Reiser and Tabak, 2014). Essential during scaffolding is that the learner performs all parts of the task on their own, despite receiving support (“independent activity”, see Reiser and Tabak, 2014). Scaffolds come from a more knowledgeable other, such as a teacher, learning materials, or a digital learning environment. For instance, scaffolds can model the problem-solving process, direct attention to important aspects of the task, or elicit explanations (Reiser and Tabak, 2014). Given that every learner enters a learning situation with different prior knowledge, a tenet of scaffolding is adaptivity. The same is true for groups of learners. Such adaptive (or personalised) support is adjusted to the relevant characteristics of the learner, the group or their interaction, with characteristics being “relevant” if they are expected to affect groups’ ability to achieve their goal (e.g. acquiring new domain-specific knowledge, Tetzlaff, Schmiedek and Brod, 2021). This resonates with works in CSCL, especially research on external collaboration scripts (Fischer et al., 2013; Kollar, Wecker and Fischer, 2018; Fischer et al., 2007) as research on expertise and collaborative learning scripts has shown that not all groups benefit from the same amount of support. Rather, support that is too fine-grained can be expected to lead to lower performance in learners who already have high competence (expertise reversal effect, Kalyuga, Rikers and Paas, 2012; over-scripting, Dillenbourg, 2002) whereas learners with little prior experience may struggle to collaborate effectively when they receive too little support (“underscripting”: Dillenbourg, 2002). Adapting the support to a group’s needs is thus seen as crucial.

Designing for adaptive support

When designing support for collaborative learning, researchers and developers have to make a number of decisions, for which the framework of CSCL design dimensions presented by Rummel (2018) can serve as a conceptual guide (Figure 4.1).

First, developers must decide what the support is ultimately expected to help groups achieve, that is, the goal of the support. This goal may be acquiring new domain-specific knowledge, acquiring collaboration skills, having satisfaction with the collaboration, or other relevant outcomes. Next, it has to be decided how the instructional support can achieve this goal. Rummel (2018) refers to this as the target of the support. For instance, in order to co-construct knowledge that each group member did not hold before, the group may receive support that targets their interaction, for instance prompting groups to engage in a discussion (see Weinberger and Fischer, 2006). The same goal may be addressed by helping groups monitor their understanding and support each other repair misconceptions. Besides other dimensions such as timing, addressee, or availability of the support, it also has to be decided whether the support is implemented in a fixed (every groups receives the same support at the same time), adaptable (each group can decide which support they receive) or adaptive way (a system decides under which circumstances a group receives which degree of support).

The foundation for an adaptive system is a model of relevant prerequisites for collaborative learning and desired states or processes that can occur during collaboration (Deiglmayr and Spada, 2010). This model underlies the processes performed by the automated support system. The early works on adaptive support for collaborative learning comes from the 2010s and leveraged techniques that represent “good old-fashioned”, or symbolic, artificial intelligence – they were rule-based.

In order to provide groups with adaptive support during the collaboration, a system has to collect information about the collaboration, process this information to determine whether the group requires support, and then deploy adequate interventions that help the group achieve their goals (Deiglmayr and Spada, 2011; Molenaar, 2022; Walker, Rummel and Koedinger, 2009).

Following Molenaar (2022) in the first step, a system has to collect information about the learners, the groups and the collaboration process (detect), and process this information into indicators that represent relevant insights into characteristics and processes during collaboration (e.g. the distribution of relevant domain-specific knowledge among the group members). One of the early approaches to automatically collecting and processing relevant information about the collaboration process was leveraging text classification approaches, a group of machine learning techniques from computational linguistics (Rosé et al., 2008). Based on their experiences from an interdisciplinary project, Rosé et al. (2008) discussed how text classification can be utilised to analyse transcripts of collaborative dialogues in order to automatically analyse the process of knowledge co-construction using a multidimensional coding scheme (instead of merely analysing the content of the dialogue). This ability to automatically assess collaboration processes in terms of relevant events (e.g. knowledge co-construction) then provided the basis for deciding upon groups’ need for support. The authors urged great care when developing the indicators to be used by an automated system to assess the collaboration process. For instance, it is essential that the indicators are externally valid, and actually represent what they aim to represent (Rosé et al., 2008).

These indicators inform the system about the current state of the collaboration and are then compared to a desired goal-state (diagnose). Depending on the outcome of this comparison, support may be offered (act) (see Molenaar, 2021; Molenaar, 2022). Deiglmayr and Spada (2010) underlined the importance of developing rules that govern the behaviour of the support system. Specifically, they illustrate production rules (IF-THEN rules) that specify which support is offered under which circumstances (also see Bernacki, Greene and Lobczowski, 2021; Radkowitsch et al., 2021). These rules can target critical moments (for example, “IF state is detrimental, THEN offer support A”) or opportune moments (“IF potential for beneficial interaction, THEN offer support B”: see Deiglmayr and Spada, 2010).

Following the concept of scaffolding, systems that support collaborative learning are designed to only provide support as long as the group requires it. As a consequence, the degree of support is gradually reduced (or if necessary increased) as the competence of the learner or the group changes (Reiser and Tabak, 2014). Such fading can be realised, for instance, by removing certain action prompts after they have been shown for a set number of times (Wecker and Fischer, 2011). Thus, support in (and for) collaborative learning (i.e. scaffolding) is not available indefinitely, but only so long as learners require it.

After illustrating the foundations of the mechanisms behind learning during collaboration and how support for these processes can be conceptualised, we turn to the most central approaches to providing groups with adaptive support. Here, we begin with providing an overview of well-established approaches.

Different approaches to providing adaptive support

Methods for supporting groups include:

1. adaptive collaboration scripts (Edwards et al., 2025; Karakostas and Demetriadis, 2011; Rummel, Mullins and Spada, 2012; Rummel et al., 2008);

2. adaptive tutoring systems that have been modified to accommodate collaborative learning and tutoring (Diziol et al., 2010; Walker, Rummel and Koedinger, 2009; 2011; 2014; Sottilare et al., 2017); or

3. virtual agents in the form of collaborative conversational agents (for an overview see Earle-Randell et al., 2025, and for examples: Araujo et al., 2025; Burkhard et al., 2022; Vizcaíno, 2005) and chatbots (for general overviews see Kuhail et al., 2023; Wollny et al., 2021).

These approaches aim to facilitate interactions that are conducive to knowledge co-construction or that inhibit undesired interaction patterns (Earle-Randell et al., 2025; Fischer et al., 2013; Vizcaíno, 2005; Vogel et al., 2017) while providing learners with opportunities to internalise beneficial interaction patterns (Fischer et al., 2013; Kollar, Fischer and Hesse, 2006). Besides approaches that aim at directly fostering the collaboration in groups, some approaches instead are designed to help the teacher to monitor and orchestrate the students’ collaboration (Chounta and Avouris, 2016; Han et al., 2021; van Leeuwen, Teasley and Wise, 2022; Yang et al., 2022).

These systems often use production rules (IF-THEN rules) to generate diagnoses of the collaboration or decisions about the need for support based on production rules. Despite the effectiveness of adaptive collaboration support, over the years it has not been implemented on a broad scale. One reason for this may be the complex boundary conditions for effective collaborative learning which have to be understood well in order to formulate production rules. Moreover, assessing the state of the collaboration in a valid and automated manner is far from trivial (see work on collaboration analytics, for example Wise, Knight and Shum, 2021).

The recent introduction of chatbots that leverage large language models (LLMs) has sparked a new conversation about providing groups with adaptive support, with often high hopes regarding their ability to provide groups with adaptive support.