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

As illustrated in the previous section, there is a long-standing tradition of employing computer technology to provide groups with adaptive scaffolding. Research and development in this field was reinvigorated when the company OpenAI provided public access to their LLM GPT 3.5 in the form of “ChatGPT”. The introduction of LLM platforms such as OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Co-Pilot, Anthropic’s Claude, Mistral’s Le Chat, or DeepSeek’s Deepseek have been touted as “a new type of pedagogical scaffolding” (Gong et al., 2024) with the potential to reshape (Boudia, C. and Bengueddach, 2024) and revolutionise education (Lai et al., 2024; Yan et al., 2024). Despite not being dedicated educational technology, expectations that this technology can achieve a more thorough personalisation of support than previous attempts are high. The hope is that past efforts can be continued with adaptive support that is more flexible than what has hitherto been possible.

For the context of collaborative learning, authors highlight the capabilities of GenAI (usually LLMs) to process inputs from different modalities such as text, speech, or images, as well as to retain the context of the conversation and generate adequate responses (Jong et al., 2024), such as (almost) human-like, coherent texts (Boudia, C. and Bengueddach, 2024; Cress and Kimmerle, 2023) or other content that is similar to human-created material (Lai et al., 2024). Furthermore, these tools are expected to perform automated assessments of the collaboration and provide immediate, tailored, adaptive scaffolding (Gong et al., 2024) in the form of feedback (Lai et al., 2024; Suciati et al., 2024) guidance, and correction of errors (Güner and Er, 2025; Chu, Xu and Zhai, 2024). Eventually, this adaptive support is expected to lead to improved learning through collaboration.

As described above, to provide adaptive support, a system has to achieve three goals: 1) detect relevant characteristics of the group members, the group as a whole, and the collaboration process; 2) formulate a diagnosis about the current state of the collaboration by contrasting it with a desired goal state; and 3) select and deploy scaffolds (see Molenaar, 2022).

Generative AI may detect and diagnose tasks. Like approaches from computational linguistics (see Rosé et al., 2008), generative AI LLMs have natural language processing capabilities that can make a valuable contribution to the analysis pipeline within collaboration support. For example, Zheng et al. (2021) used an LLM to label data from the interaction in groups. After labelling (i.e. categorising (or annotating) different collaborative actions according to a coding schema), the support used production rules to generate support for the groups. This use case for LLMs hinges on the accuracy with which the model assigns labels to collaboration data (e.g. audio, video, computer log files). Thus, an essential question is to what extent LLMs are capable of labelling data reliably based on a coding schema. Some studies found acceptable labelling performance across multiple labels when compared to human coding (as indicated by interrater reliability scores greater than 0.7, e.g. Amarasinghe et al., 2023), while others reported lower overall performance (Huang et al., 2025; Yin et al., 2025) or high performance only for certain labels (Wang et al., 2023). Thus, there is potential to integrate LLMs into the analysis that lays the foundation upon which to provide groups with adaptive support. But there are still obstacles to overcome, especially in terms of consistently accurate diagnoses so that groups do not receive inadequate support due to misclassifications. In this context, Wong et al. (2025) recently demonstrated that the accuracy of automatic coding can be further improved by using models that are able to leverage inputs from multiple modalities, such as speech (audio) and written text.

What roles can generative AI assume in order to support collaborative learning, andwhat is being supported?

In this chapter, we focus on how researchers and developers have leveraged GenAI models to provide groups with scaffolds and to what effect. Therefore, we collected a broad sample of articles from scientific journals and conference proceedings that reported on studies in which generative AI systems were implemented in collaborative learning scenarios. Notably, we observed that studies in this field tended to use LLMs as the foundation of support, while rarely leveraging them to expand the capabilities of already established types of collaboration support, such as collaboration scripts or pedagogical agents. To gain a clearer picture of the landscape of GenAI support for collaborative learning, we analysed these studies and determined 1) which role a support system may assume during collaboration to scaffold the collaboration, 2) which aspects of the collaboration are scaffolded (i.e. the dimension “target” in the framework proposed by Rummel, 2018), 3) whether the tools are effective in supporting knowledge acquisition during collaborative learning, and 4) on what grounds the adaptive behaviour of the tools is designed.

Inspecting current studies on collaborative learning with generative AI, we derived four different ways in which generative AI was integrated into collaborative learning settings in order to afford or support the collaboration. These are characterised below, with examples provided (see Figure 4.2 for an illustrative overview).

First, there are studies in which GenAI (i.e. LLMs) served as a repository of information, which groups can query to obtain information that can be used to solve their tasks (e.g. Chu, Xu and Zhai, 2024; Darmawansah et al., 2025; Feng, 2025), thus serving the function of a web-search. For example, Darmawansah et al. (2025) implemented ChatGPT to support groups during argumentative knowledge co-construction. In the first phase of the collaborative activity, groups were tasked to research information, which they subsequently used to develop arguments. During this phase, groups could search for information about different topics using ChatGPT or other sources. In the subsequent phases of the activity, students discussed using their arguments.

Generative AI can further be a source for personalised learning material (Naik et al., 2024), that is, a tool that collects information about the group, its collaboration, or its results, and generates additional learning material that the group can use. For instance, Naik et al. (2024) implemented GenAI in a collaborative scenario where groups first developed a solution for a problem and then contrasted their solution to an alternative solution, subsequently discussing trade-offs between the two solutions. In this case, the group’s solution was processed by ChatGPT using prompts from the researchers, who tasked the model to create an alternative solution, a so-called contrasting case. Contrasting cases present learners with alternative solutions to the problem (i.e. cases). By contrasting their own solution to another case, learners can gain a deeper understanding of the underlying principles of the correct solution to the problem (Alfieri, Nokes-Malach and Schunn, 2013). The group then had to discuss the merits and drawbacks of its own and the generated solution. In other studies, generative AI systems were used as tools that performed tasks for the groups, such as generating narrations or images based on inputs from the group (Wei et al., 2025).

Other studies used GenAI to intervene and scaffold the collaboration (Cai et al., 2024; Feng, 2025; Liu et al., 2024), thereby assuming the role of a teacher or facilitator. In this role, GenAI is positioned outside of the group, monitors the collaboration, and provides support. For example, Cai et al. (2024) implemented a chatbot based on ChatGPT, which followed pre-specified rules to facilitate active participation during collaboration. To this end, the system monitored the participation and alerted the group if the participation was uneven, prompting the group to ensure that all group members contributed. Furthermore, the bot scripted the discussion based on the stages suggested by Tuckman (1965) to promote knowledge construction, for instance by asking follow-up questions and steering the conversation back on-topic if necessary.

Feng (2025) described a system that only provided support upon request. Specifically, groups could ask the system to provide summaries of or feedback on the group’s discussion. An analysis of the interaction between group members and the chatbot revealed that most of the groups’ requests were cognitive interactions, such as asking task-related questions or requesting the chatbot to perform tasks like formatting outputs. In other examples where GenAI assumed this role, the system was designed to promote reflection about the collaboration process, for instance by monitoring a group’s interaction in terms of group norms and promoting reflection about the interaction in the group (Ko and Foltz, 2025), by helping supported groups to interpret and reflect upon information from a learning analytics dashboard during a debriefing session after a collaboration phase (Damsa et al., 2025), by providing feedback on texts that were created during a knowledge-building activity (Anderson et al., 2025), or by facilitating the process of providing peer feedback (Greisel et al., 2025).

A GenAI system may also assume the role of a tutor or dialogue partner. This role relates to that of a teacher, but instead of providing guidance on the interaction in the group, the GenAI system serves as a partner in a dialogue aimed at developing domain-specific knowledge. For example, Ahlström et al. (2025) described a collaborative learning scenario where groups interacted with digital characters in a virtual reality (VR) environment (e.g. a hurricane evacuee or a climate scientist) in order to collect information that was needed to solve a task. These characters represented a storytelling element and were not designed to act as pedagogical agents (Ahlström et al., 2025).

A more widespread implementation of pedagogical dialogue partners can be found in arrangements where an individual learner interacts with an artificial agent acting as a tutor or partner for Socratic dialogue (e.g. Dang et al., 2025; Goda et al., 2024; Song et al., 2025). As one such example, Goda et al. (2024) described a chatbot-based system that was structured around a set of principles for Socratic dialogue, such as providing learners with structured questions that are expected to elicit critical thinking and analyse their own reasoning instead of providing direct answers to students’ questions. In a similar vein, Dang et al. (2025) developed an LLM chatbot for interaction during a mixed-reality learning activity. The chatbot was designed to make use of the conversation history between learner and chatbot in order to provide personalised responses that sought to promote critical thinking, such as asking open-ended questions and encouraging elaboration and reflection (Dang et al., 2025). We wish to note that this application of generative AI arguably represents a fringe case within the broader landscape of collaborative learning, which traditionally emphasises interaction between at least two human learners. We include it here since the interaction with an artificial interaction partner can elicit cognitive processes conducive to learning that can also be found in collaboration between human learners (e.g. eliciting argumentation and self-explanations). However, a separate discussion about whether these contexts truly represent collaborative learning is required.

Finally, generative AI may also be implemented as an artificial group member. In this role, the system is positioned as part of the group and participates in the learning activity, for example by contributing domain-specific knowledge to dialogues (e.g. contribution by Hernandez-Leo et al., 2025; Liu et al., 2024; Zhu et al., 2023) or by facilitating interaction (e.g. An et al., 2024; Liu et al. (2024) implemented support for learning about interdisciplinary collaboration: students were assigned the role of different experts and then collaborated to solve a problem in a team. In one group, a conversational agent based on ChatGPT functioned as a peer group member. This agent played an engineer and provided respective expert knowledge to the discussion in the group. Similarly, Hernandez-Leo et al., (2025) implemented an artificial peer in a knowledge-building activity. This agent participated in the activity and was designed to elicit discussions and promote critical thinking skills by submitting its own answers to the knowledge-building environment, rating other students’ answers, and participating in the knowledge-building discussion.

While assuming these different roles, the support can scaffold different aspects of the collaboration to achieve its goal, that is different targets (Rummel, 2018). In the following, we present examples of different targets that we identified in the literature (Figure 4.3). Notably, in some studies, groups were free to choose how they used the GenAI tool (e.g. An et al., 2024; Feng, 2025; Wei, Li and Lan, 2024). Not all of the studies explicitly reported the target of the support, in which case the target(s) of the support can only be assumed based on the reported results or illustrative screenshots provided by the authors.

Support can target cognitive aspects of the collaboration, for instance by providing information or resources that the group needs to solve its task and learn (e.g. Chu, Xu and Zhai, 2024; Darmawansah et al., 2025; Feng, 2025; Lin et al., 2024; Zhu et al., 2023) implemented a chatbot based on ChatGPT that acted as a peer during the collaboration. One version of the chatbot in this study provided disciplinary knowledge that was necessary to solve the collaborative task. In other studies, the LLM-based system aimed to elicit cognitive processes such as elaborating on new information (e.g. Dang et al., 2025).

There are also approaches, where GenAI tools are provided so that groups offload tasks to them. For instance, Wei et al. (2025) provided groups with a combination of different GenAI tools (ChatGPT, Midjourney, Runway) which performed different tasks for the group, such as creating storyboards, images, and videos. These approaches cannot necessarily be conceptualised as scaffolding.

Other authors explored how generative AI can scaffold socio-cognitive interaction patterns that afford the co-construction of knowledge in the group (e.g. An et al., 2024; Darmawansah et al., 2025) or promote reflection processes (e.g. Naik et al., 2024). For instance, the systems by Lin et al. (2024) and Naik et al. (2024) highlighted certain parts of the groups’ solutions and asked the group members to reflect on their solution and explain concepts included in the solution.

Metacognitive aspects of the collaboration have often been scaffolded by providing groups with feedback about their work (e.g. dal Ponte, Dushyanthen and Lyons, 2023; Feng, 2025; Gong et al., 2024). Dal Ponte et al. (2023) presented a system that evaluated a group’s solution and then provided the group with feedback about the quality of the solution. Groups could then react to the feedback and revise their solution. Moreover, when groups were freely able to choose how to use an LLM chatbot, some explicitly requested feedback on their written solution (Gong et al., 2024).

Another popular method of providing support in the context of feedback is peer feedback (Hornstein et al., 2025; Huisman et al., 2019). Greisel et al. (2025) presented two approaches on how generative AI may facilitate this process: The first approach (contribution by Greisel et al., 2025) consists of a system that processes a student-written text and generates a message with feedback for this text, while another student (the peer reviewer) also reads the text and creates their own message with feedback. Subsequently, the peer reviewer reviews both feedback messages and creates a feedback message that combines the machine-generated message and the human-generated message, before sending the feedback message to the student who created the text. In the second approach, an LLM provided feedback on a peer reviewer’s feedback message. With the help of this machine-generated feedback, the peer reviewer then revised their own feedback before sending it to the original student. Noroozi et al. (2025) further noted that an LLM may also help learners to reflect on the peer feedback they have received or to revise their text using peer feedback.

Another way in which generative AI systems can provide feedback relates to promoting collaborative reflection about the collaboration process. The system described by Ko and Foltz (contribution in Greisel et al., 2025) monitored the collaboration in terms of previously negotiated group norms and provides examples from the collaboration process so that the learners can reflect upon their collaboration after collaborating.

Some systems target social aspects of the collaboration, such as the participation of the group members (Cai et al., 2024; Gong et al., 2024; Liu et al., 2024) or off-topic discussions (e.g. Cai et al., 2024). As described above, the systems implemented by Cai et al. (2024) or Liu et al. (2024) monitored the amount of participation in the groups and prompted groups to regulate the distribution of participation if it was uneven. Similarly, An et al. (2025) implemented an artificial group member that monitored the interaction to detect moments of uneven participation, prompting inactive group members to contribute to the discussion.

The system by Cai et al. (2024) additionally monitored the content of the conversation to identify off-topic talk. If off-topic conversations were detected, the system would prompt the group to return to the main task.

Other systems focused on guiding and orchestrating the collaboration on a behavioural level, for example by leading the group through the task (Cai et al., 2024; Lin et al., 2024; Liu et al., 2024). For instance, Liu et al. (2024) designed a system that moderated the collaboration by guiding group members through the different phases of the collaborative task, explained tasks and orchestrated turn-taking during discussions. The system presented by Lin et al. (2024) served as a summarisation aid to create feedback for the group and propose potential further topics with which the group may familiarise itself. Specifically, based on the contributions of the group members, the system created feedback messages that summarised (the model’s “understanding” of) what the group members already understood and how they could proceed with their task.

Finally, some systems were designed to support socio-emotional aspects during collaboration, which includes motivating students, regulating emotions during learning, or giving compliments. While studies such as those reported by Dang et al. (2025) and Feng (2025) investigated the interaction between learners and generative AI systems in terms of socio-emotional processes, studies in which generative AI is deployed to specifically promote beneficial socio-emotional processes or states are rare.