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

In conclusion, our findings underscore the significant promise of GenAI in enhancing educational practices related to learning and assessment. Specifically, we have demonstrated that GenAI-powered systems can directly support both students and educators, streamlining teaching activities and providing targeted assistance. However, despite these promising developments, our analysis also reveals several critical caveats that must be carefully considered when informing future practice and policy in this area. One such concern is the need for careful attention to the design of teaching practices that enable the effective use of GenAI, particularly in systems designed to directly support students, such as tutoring chatbots. For example, recent studies have shown that combining GenAI with established instructional methods (e.g. scaffolding), where GenAI agents guide students through step-by-step reasoning, can foster genuine learning and sustained performance enhancement even after removal of GenAI support (Yan et al., 2025). By contrast, unguided “answer-giving” practices, where students simply request solutions from a chatbot, have been found to undermine reflection and suppress metacognitive engagement (Fan et al., 2024; Stadler, Bannert and Sailer, 2024). As emerging evidence suggests, not all students may benefit equally from these systems. Therefore, it is essential to consider how different student subpopulations, based on factors such as socio-economic status or prior academic achievement, interact with these technologies. By identifying the specific conditions under which these subgroups may benefit from GenAI-powered systems, we can mitigate potential inequalities and ensure that AI tools support a diverse range of learners.

Moreover, while GenAI offers the potential for rapid development of tools like tutoring chatbots, one should recognise that on average general-purpose LLMs (e.g. off-the-shelf GPT systems) do not yet match the effectiveness of traditional intelligent tutoring systems, when not designed or finetuned with adequate pedagogical knowledge (Borchers and Shou, 2025). Hybrid systems that embed GenAI within educationally grounded frameworks may show more promise, but the evidence base remains limited. We still also need evidence that will compare how GenAI-powered tutors compare to their conventional counterparts in terms of their ability to provide sustained, long-term learning support. As a result, future research needs to investigate whether GenAI-based tutoring systems can effectively support learners over extended periods. In addition, there is considerable potential for GenAI to complement existing intelligent tutoring systems by enhancing their interactivity and enabling more natural language communication, which could ultimately create more personalised learning experiences. Future research should focus on exploring how GenAI can be integrated into intelligent tutoring systems, drawing on well-established educational principles to enhance these systems’ overall efficacy.

Despite the promising capabilities of GenAI to generate high-quality feedback, research has shown that students’ trust in AI-generated feedback varies considerably across contexts. In some studies, students respond positively and perceive such feedback as clear and useful, while in others they express scepticism about its accuracy or relevance. This variability in trust can influence whether learners engage with GenAI feedback, which in turn affects its potential impact on learning. To fully realise the benefits of GenAI in supporting feedback processes, future work should focus on developing teaching practices that help integrate AI-generated feedback effectively into classroom use. One promising direction is to use GenAI as a tool to help educators reflect on and refine their feedback (i.e. human or AI-generated) by checking whether it is clear, balanced, and aligned with established feedback principles before it reaches students.

GenAI shows promise in supporting educators with their daily teaching and administrative tasks. Although existing evidence grounded in more reliable measures of time spent with technology, such as usage log analysis, shows increases in efficiency for some tasks like lesson planning, qualitative studies highlight potential “blind spots” in these estimates that warrant further research. Specifically, the hidden labour educators must invest in reviewing and verifying the accuracy of AI-generated content may not be fully accounted for when relying solely on usage logs, unless they do the revisions online. All this emphasises the need for further research into how the GenAI tools can be designed to enhance, rather than complicate, teaching practices.

In the realm of assessment, GenAI offers valuable opportunities to streamline the creation of assessments and automate scoring processes. Its potential has been demonstrated in large-scale standardised tests, such as the Duolingo English Test, where GenAI can assist in generating items that meet psychometric standards. However, this level of psychometric rigor is rarely required or feasible in everyday classroom assessments. While the widely available LLMs can help teachers design questions or tasks more efficiently, its outputs still require human review to ensure pedagogical relevance, truthfulness, fairness, and alignment with learning goals. Likewise, prompt-based approaches to automated scoring, although more accessible to non-technical users, remain less reliable than fine-tuned or conventional machine learning models. Educators should therefore treat GenAI tools as a complementary aid rather than a substitute for human judgment, validating its outputs for clarity and appropriateness before classroom use. Future research should focus on developing practical frameworks that help teachers integrate GenAI tools into formative and summative assessment processes responsibly, combining the efficiency of automation with the interpretive expertise of educators. To assist educators, future work needs to focus on developing classroom assessment strategies that incorporate GenAI in meaningful ways, expanding its applicability while also ensuring that it enhances the overall learning and teaching experience for both educators and students.

As generative AI becomes an integral part of the cognitive ecosystem, the focus of skill development must shift from performing tasks that AI can automate to mastering the skills required to work with and through these technologies effectively. Hybrid Human-AI skills are not about replacing human intellect but augmenting it. They represent a suite of metacognitive, critical, and ethical competencies that enable individuals to leverage AI for enhanced performance without sacrificing the development of their own durable knowledge and judgment. Cultivating these skills is the primary defence against the “mirage of false mastery.” Key components of the Hybrid Human-AI skillset include:

Strategic AI Engagement and Prompt Crafting: This goes beyond simple “prompt engineering.” It involves the ability to analyse a task, strategically select the appropriate AI tool (or choose not to use one), and design effective prompts that guide the model towards a desired outcome. It also includes the skill of iterative refinement, critically assessing AI output and adjusting prompts to improve results.

Critical Evaluation and Verification: This is the capacity for intellectual scepticism when faced with AI-generated content. It involves actively fact-checking claims, recognising potential biases in the model’s output, assessing the credibility of information (especially in Retrieval- Augmented Generation systems), and ultimately making an informed judgment about the trustworthiness and quality of the AI’s contribution.

Creative Co-creation and Synthesis: This higher-order skill involves using GenAI not as an answer engine, but as a creative partner. It is the ability to use AI for brainstorming, exploring counterfactuals, generating diverse perspectives on a problem, and synthesising AI-generated content with one’s own knowledge to create novel work that is more than the sum of its parts.

Metacognitive Awareness and Self-Regulation: This is the crucial ability to monitor one’s own learning and cognitive processes while using AI. It includes recognising the onset of over-reliance or “metacognitive laziness,” making conscious decisions about when to delegate a task to AI versus when to engage in effortful thinking for the sake of learning, and reflecting on how AI use is impacting one’s own skill development.

Ethical and Responsible Use: This involves understanding and navigating the ethical aspects of GenAI. It includes respecting data privacy, upholding academic and professional integrity, providing appropriate attribution for AI contributions, and considering the broader societal implications of how these powerful technologies are deployed.

Finally, our study highlights a critical risk: the uncritical adoption of GenAI may inadvertently undermine the development of key human skills such as critical thinking, metacognition, and evaluative judgment, all of which are foundational to genuine expertise. This could result in what we describe as the “mirage of false mastery,” where the impressive outputs generated by AI mask the underdevelopment of essential skills, including hybrid human-AI skills (Box 2.3). The path forward, therefore, is not a rejection of technology, but a commitment to pedagogical intentionality and methodological rigor. Rather than simply asking whether GenAI “augments students’ task performance,” we must focus on how it can be used to foster deep, meaningful, and durable learning. This means reorienting our focus from GenAI-driven products to human-centred processes, ensuring that GenAI tools are designed to scaffold rather than supplant human thinking. By prioritising the development of durable, transferable skills and integrating metacognitive awareness into both learning and assessment, we can unlock the transformative potential of GenAI, creating an educational future that is not only more efficient but also authentically human.