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

The advent of generative AI in pedagogical agents is only the beginning of a broader transformation in educational technology. This section outlines future directions and a research roadmap for advancing this field. It highlights several promising avenues:

1. Authoring tools and platforms for educators to easily create and customise AI-driven tutoring content without deep technical knowledge;

2. Multimodal GenAI agents that incorporate vision, speech, and possibly other sensory inputs to create more holistic learning experiences;

3. Multi-agent and collaborative AI systems, where multiple AI tutors or AI-student peers interact with each other and learners to simulate group learning dynamics;

4. Lifelong learning companions that accompany and support learners over extended periods (across courses or years), adapting as the learner grows;

5. Cross-context adaptive deployment, ensuring these agents can transition and be effective in varied contexts (from formal classrooms to informal learning, across different subject domains and age groups). We also propose future research methodologies, including large-scale trials and longitudinal studies, to validate and refine the impact of these systems.

Finally, we emphasise that GenAI agents are an evolving technology that will require continuous evaluation of effectiveness, accessibility, and alignment with pedagogy as they develop.

Educator empowerment through authoring tools

First, for generative AI tutors to be most useful and widely adopted, educators need to be able to create and customise content easily. Relying on AI experts to build every lesson is not scalable. Therefore, a crucial area of development is teacher-facing authoring tools that leverage AI to help produce AI-driven lessons. For example, a teacher might input the learning objectives and key points for a lesson, and the system could generate a draft prompt template or a series of questions aligned with that objective. The teacher could then review, refine, and approve the AI-generated content. Alternatively, a teacher could demonstrate a desired dialogue flow once – either by conversing with a mock student or by outlining it explicitly, and the AI tutor could adopt that style. Additionally, AI could support the creation of simulations or narrative-based learning activities. For instance, if a teacher requests a scenario in which a student debates an AI acting a historical figure on a given topic, the system could generate an initial script for subsequent teacher revision. Such tools would dramatically lower the barrier to implementing customised AI tutoring across different subjects and languages.

A research path here includes understanding how teachers conceptualise AI behaviour and making interfaces that map to their thinking (e.g. some might prefer a rule-based interface, others might want to give examples and have the AI generalise – similar to programming by demonstration). Co-design with educators will be key; early studies should involve teachers using prototype authoring tools and measure outcomes like how quickly they can develop a new lesson, how effective that lesson is for students, and how comfortable the teachers feel about the level of control and transparency in the AI’s resulting behaviour. The ARCHED framework is a step in this direction as it structures AI involvement in instructional design with human oversight at each stage (Li et al., 2025). Future research can build on ARCHED to apply similar principles to real-time tutoring content creation.

Multimodal and embodied generative agents

One clear direction for future research is extending beyond text to multimodal interactions. Humans communicate and learn through a rich mix of modalities – speech, gesture, writing, drawing, etc. Future AI tutors likely will support student learning by doing the same. Already, models like GPT-4 have some multimodal capabilities (accepting image inputs, for example), and research is ongoing on integrating visual understanding with language models. A future AI-powered pedagogical agent might watch a student solve a physics problem on paper (via a camera), diagnose a misconception from their written work or diagrams, and then provide verbal guidance. Or in a virtual lab, the agent might observe how a student assembles a circuit or conducts a simulation and intervene at the right moment. Vision- enabled agents could check a student’s worked solution for errors by “seeing” it, much as a teacher would glance at a notebook. Meanwhile, speech interfaces will allow more natural use in contexts where typing is inconvenient – imagine language learners practicing conversation with an AI that not only speaks but also reads facial expressions to gauge affection (e.g. confusion).

Moreover, embodied agents in AR/VR could provide immersive tutoring – for instance, a holographic science tutor that appears in an AR headset to guide a student through a chemistry experiment in a lab. Embodiment can leverage the physical environment: for instance, in mixed reality, an agent can point to parts of a model or demonstrate with virtual objects. Multimodal agents may be designed to enrich students’ learning experience, better aligning with theories like Dale’s Cone of Experience (Dale, 1969) and Kolb’s experiential learning cycle (Kolb, 1984), which emphasise learning by doing and experiencing. Early trials of such approaches (like the DALverse platform mentioned earlier, integrating LLMs with a metaverse) have shown increased engagement and improved retention (Damasceno et al., 2024). The challenge for researchers is to seamlessly integrate modalities such that the AI can interpret and generate multi-sensory data coherently. This may involve combining specialised models (for vision, for speech) with LLMs, or training unified, multi-modal models. It also raises new questions: How does one evaluate learning in these richer environments? How to ensure the added modalities truly improve learning and are not just gimmicks? These will be crucial questions to be answered as this line of research advances.

Nonetheless, a likely near-future scenario is a tutor that can speak and listen (already feasible with speech-to-text and text-to-speech integration) and perhaps use simple graphics or diagrams on the fly (e.g. drawing a chart using data provided in teacher-prepared curriculum materials). Ultimately, multi-modal generative agents aim to mimic a human tutor not just in conversation, but in full instructional presence – writing, sketching, demonstrating, and responding to non-verbal cues.

Multi-agent systems and collaborative learning

Another exciting frontier is the use of multiple agents to enrich educational interactions. Rather than one AI tutor and one student, we could have scenarios with several AI characters and one or more students. For example, a multi-agent system might include an AI tutor plus an AI peer learner; the human student can then participate in a group dialogue. This could simulate collaborative problem-solving or Socratic debates, exposing students to diverse viewpoints. (Park et al., 2023) successfully developed “generative agents” that interact with each other to simulate human-like social behaviour. Researchers like Li et al. (2024) have proposed multi-agent frameworks (EduMAS) for educational support. One agent might propose a solution, another critique it, and the human student could be asked to arbitrate or contribute, thereby learning through a rich discussion. Alternatively, multiple agents could take on specialised roles: one focusing on content hints, another on motivational encouragement, and a third maybe representing a historical figure or stakeholder in a debate (imagine learning civics by having AI agents role-play different political viewpoints in a discussion with the student).

These multi-agent interactions, if well-orchestrated, can model productive dialogue patterns and expose learners to argumentation and perspective-taking that is hard to achieve with a single tutor. However, designing multi-agent systems is inherently complex as the systems must be designed in ways that preserve consistency and prevent confusion (the agents must not overwhelm or contradict in a detrimental way). Research will need to explore optimal designs: What is the ideal number of agents within a multi-agent system? What role combinations are effective? Some early studies outside of education suggest multi-agent debates can improve answer accuracy by agents critiquing each other (Park and Seo, 2025), but in education the goal might be more to model peer discussion or provide contrast.

Team Tutoring, where AI supports collaborative groups of students (Sottilare, Graesser and Sinatra, 2018), is related; the AI could moderate or participate in a student group discussion, making sure everyone contributes (this uses multi-agent in the sense of AI + multiple humans). Overall, harnessing multiple AI agents to facilitate social learning is a promising direction, aligning with socio-constructivist theories that knowledge is often built through discourse.

Lifelong learning companions

Envisioning further ahead, generative AI agents could become continuous learning companions that support an individual over years, across many domains. Instead of separate tutors for math, science, writing, etc., one AI (or an integrated system) could accompany a student through their educational journey, maintaining a long-term model of their interests, strengths, and weaknesses. This idea resonates with the concept of personal AI assistants and also ties into educational initiatives for personalised lifelong learning (Krinkin, 2026 (forthcoming)). For example, a student’s “AI mentor” might help them in middle school algebra, then later adapt to help with high school physics, remembering that the student struggled with calculus concepts and proactively reinforcing those when they appear again. It could even extend beyond formal schooling: as the student goes to university or job training, the same AI knowing their learning history could tailor new learning experiences effectively.

Implementing this raises many questions – technical (how to store and update the long-term learner model securely), pedagogical (how to ensure continuity leads to cumulative benefits and not compounding of earlier biases), and ethical (ownership of that data, the right to “reset” or change one’s AI companion to avoid pigeonholing based on early performance). Research by Tong and Hu (2024) on self-improving adaptive instructional systems and by others on neuro-symbolic architectures (such as the NEOLAF AI service for education in the United States (Tong et al., 2023) is trying to tackle the idea of AI that can improve itself and adapt over time, which is related to building a durable lifelong tutor. A roadmap for this could involve pilots where the same AI system is used across multiple courses or grade transitions, observing if retention or transfer improves because the AI can remind the student of previous knowledge or adapt to their cumulative profile.

A possibility is that a lifelong AI companion could also foster lifelong learning habits – by being present beyond the classroom, it might encourage curiosity, recommend learning opportunities, or help with personal projects (like an AI that helps a student interested in music by finding them educational resources or setting practice goals). Essentially, it would blur the lines between formal and informal learning support.

Cross-context and domain transfer

Another future direction is ensuring that generative agents can adapt to various educational contexts easily. At present, considerable effort is required to adapt systems to each new domain or use-case. In the long run, more generalist AI tutors might rapidly adapt to and learn new content. Few-shot learning capabilities of LLMs are promising here – perhaps a tutor can be given a single lesson text or some examples of questions and answers in a new subject and then operate as a tutor in that subject. The robustness across contexts also includes adapting to different educational levels (e.g. using simpler language for a 5th grader versus a college student – something LLMs can do to an extent by role prompting). It also includes cultural context adaptation: a global AI tutor might need to know local curricula or examples relevant to the learner’s environment. Research might explore how to embed contextual knowledge without a complete retraining – maybe by plugging in local knowledge bases or letting the system be easily fine-tuned by local educators.

Additionally, cross-context might involve the tutor being used in different settings: formal class vs. after-school vs. workplace training. We should explore how it needs to adjust its style (formal vs. casual, directive vs. self-directed learning mode) depending on context. The ultimate vision is an AI tutor framework that is as flexible as a human teacher who can teach different subjects, age groups, and adapt teaching style – a very remote goal, but research in transfer learning and multi-domain training of AI models is making progress.