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

Generative AI has expanded the pedagogical capabilities of artificial agents, allowing them to move beyond the conventional role of previous AI tutors dispensing knowledge and providing feedback. In the Socratic Playground (SPL) and similar systems, agents can fluidly adopt roles such as mentor, peer or emotional coach depending on the context and learner needs. This section explores these expanded roles and the new capabilities that enable them, illustrating how an LLM-driven agent can shift from being a mere content deliverer to a multifaceted educational partner.

Mentors and coaches

GenAI agents can act as mentors that guide learners through open-ended problems or projects. In SPL’s essay-writing scenario, for instance, the agent does not simply provide facts or correct answers; instead, it mentors the student in critical thinking by asking probing why and how questions about the student’s essay arguments. This approach, grounded in the Socratic method, aims to foster learner reflection and reasoning in a manner analogous to the guidance of a human mentor. Since the agent can dynamically generate follow-up questions based on the student’s previous response, the dialogue feels tailored and intellectually challenging. Empirical observations suggest that SPL’s Socratic agents effectively scaffold deeper reflection – students are prompted to explain their reasoning, consider counterpoints, and refine their ideas, rather than passively receiving information (Zhang et al., 2024). This form of question-driven scaffolding, adaptive to individual learners’ interactions, marks a shift in capability: the agent moves beyond the role of lecturer to that of a personalised cognitive coach, guiding learners in cultivating metacognitive strategies for learning.

Beyond academic guidance, GenAI agents are also capable of motivational coaching. Through sentiment analysis of learner input, an agent can detect frustration or confusion and respond with encouragement, praise for effort, or strategy suggestions. This appearance of emotional attunement, made possible by LLMs’ language capabilities, allows the agent to assume the role of an affective coach, which may boost the learner’s confidence and perseverance, as suggested in pertinent research (Córdova-Esparza, 2025). In short, a well-designed GenAI agent can be simultaneously a cognitive mentor and an emotional coach, blending intellectual support with empathy in a manner that outperforms static, pre-programmed AI tutors.

Crucially, shifts between different mentoring or coaching roles could occur fluidly. The same AI tutor may transition from giving hints, to asking reflective questions, to offering encouragement, to even letting the student lead the explanation, all in one session. This versatility was nearly impossible with rigid AI tutors, but LLMs make it feasible to improvise contextually. The ability to track discourse progression, coupled with the capacity to identify patterns and make predictions, when rigorously designed and implemented (e.g. via the additional instructions to examine students’ expressed affections during conversations), allows the agent to infer when to switch roles – for instance, adopting a supportive stance during moments of frustration as an affective coach and transitioning to a more directive role once confidence is restored. By leveraging memory of preceding turns (oftentimes passed on to the GenAI agent as the context of conversations), the agent can follow pre-determined instructions to assess whether the learner is ready for increased autonomy or, alternatively, in need of scaffolded tasks and motivational feedback to bolster confidence. This capacity contributes to the humanisation of the interaction, aligning it more closely with authentic pedagogical exchanges observed in tutor–learner contexts.

Peers and companions

Generative pedagogical agents can also play the role of a peer-like collaborator, fostering collaborative engagement and co-construction of knowledge. GenAI agents can engage in less formal, more dialogic interactions that resemble peer learning or collaborative problem-solving. For example, an agent might take on the persona of a learning partner who works through a problem alongside the student, occasionally saying “Here’s how I think about it, what do you think?” rather than always instructing. This peer role leverages the conversational nature of LLMs to create a two-way exchange where the learner feels more agency. Studies have shown that multi-agent or multi-role interactions can expose learners to diverse viewpoints and promote critical thinking (Park and Seo, 2025; Wang et al., 2025). In one notable demonstration, GenAI agents were used to simulate different participants (a student, a teacher, a parent) engaging in a classroom-style discussion, thus providing a learner with multiple perspectives in dialogue (Li, Xie and Lee, 2024). While that example involved separate AI agents for each role, a single GenAI agent could also approximate a peer by sometimes prompting the student to teach it or by playing devil’s advocate. In fact, SPL incorporates a feature akin to a teachable agent mode (inspired by learning-by-teaching paradigms): the agent prompts the learner to articulate a concept or to teach it back to the system. By briefly acting as the novice who needs an explanation, the agent encourages the learner to articulate and thereby solidify their understanding – a strategy supported by educational research on learning by teaching (Bargh and Schul, 1980; Debbané et al., 2023). Generative AI enables this role-play by producing plausible queries and misunderstandings for the student to address, simulating a peer who learns from another peer’s explanations.

Researchers are also experimenting with entirely new archetypes of AI learning companions made possible by generative models (Han et al., 2025). For illustration, one can make the GenAI tutor behave like:

• a “reflection partner” agent which prompts learners at the end of a lesson to reflect on what they learned, what they found difficult, and how they overcame challenges. By asking metacognitive questions and perhaps sharing its own “thoughts” (generated from pedagogical prompts), the agent may foster the learner’s self-reflection and self-regulation habits (Guan et al., 2024); • a “cross-domain companion” that accompanies a learner across different subjects and contexts, helping to connect insights from one domain to another. As LLMs are trained on broad knowledge, a single agent can potentially discuss history in history class and switch to physics in science class, all while remembering the student’s general learning profile. This could enable continuity in mentorship that spans multiple disciplines and learning periods, essentially acting as a personalised learning companion over the long term. While still theoretical, early work on long-running GenAI agents with continuous memory points toward the feasibility of AI companions that persist and evolve alongside the learner (Park et al., 2023). Moreover, with ones’ learning profiles forming a portion of their personal world models, such a cross-domain companion may be further extended into individuals lifelong learning companions (Krinkin, 2026 (forthcoming)); • a “motivational interlocutor” oriented toward sustaining learner engagement and motivation. In this role, the AI agent may periodically revisit the learner’s goals, highlight progress achieved, or contextualise the material in relation to the learner’s personal interests – a task that LLMs can attempt by drawing on broad knowledge across domains such as sports, music, or popular culture. Through such personalisation and the maintenance of a positive tone, the agent seeks to reinforce and sustain the learner’s intrinsic motivation.

In all these expanded roles, the key enabler is the GenAI agent’s capacity for real-time adaptation and rich interactive communication. Whereas traditional static AI tutors relied on scripted praise or generic feedback, a GenAI agent can adapt its motivational messages and adjust task difficulty in response to individual learner behaviours (e.g. offering more gentle encouragement to a student who has made several consecutive errors). This adaptivity yields a richer and more socially attuned educational experience, one that more closely approximates the nuances of human tutoring and peer collaboration. Students interacting with these agents are not just passively receiving information but are actively engaged in a relational experience – conversing with a mentor/peer figure that responds to them, remembers prior exchanges, and adapts accordingly.

Early user studies and anecdotal evidence from the SPL demonstration suggest that learners frequently perceive the GenAI agent as “listening” or “understanding” them to a greater extent than prior e-learning tools. This suggests that the psychological presence of the agent is enhanced; it feels less like a programme and more like a conversational partner, which can increase student willingness to persist in learning tasks.

Of course, these new capabilities also bring new challenges. Ensuring the agent’s responses remain pedagogically sound while it improvises as a peer or coach is an ongoing area of research. Nonetheless, the expanded roles and adaptivity afforded by generative AI clearly have the potential to make AI pedagogical agents far more than animated digital tutors – turning them into mentors, coaches, and collaborators that enrich the social, cognitive and metacognitive dimensions of learning.