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

Introduction

The wide adoption of generative artificial intelligence (GenAI) – after the public release of ChatGPT in November 2022 – has triggered profound debates about their implications on education. GenAI can provide technologies that can support skill acquisition through personalised instruction and feedback, and enhance the efficiency and effectiveness of teaching practices (Giannakos et al., 2024). However, GenAI poses ethical challenges and risks as well (Kofinas, Tsay and Pike, 2025; Nikolic et al., 2023). The developments in GenAI triggered educators, education leaders, and policymakers to engage with GenAI extensively, rethink pedagogical, assessment, and governance frameworks to harness GenAI’s potential while mitigating its risks. Through these efforts, many education institutions have produced policies and guidelines to support staffand students in using generative AI (Jin et al., 2025). Similarly, many government, intergovernmental, nongovernmental, and non-for-profit organisations have also produced documents that inform GenAI adoption, responsible practices, and frameworks for professional development of educators (Cardona and Ishmael, 2023; Miao and Cukurova, 2024; Miao and Holmes 2023; OECD, 2023). Equally so, the rapid developments in GenAI have also mobilised many researchers to study implications on education and human learning (Yan et al., 2023; Zhang et al., 2024).

This chapter aims to summarise recent evidence about the implications of GenAI in human skill development and assessment. The focus will be on human skill development and assessment as they are central to education and professional development programmes. The analysis of the implications of GenAI on human skill development and assessment is particularly framed around two complementary perspectives. First, GenAI technologies offer some promising prospects for advancing our existing practices related to skill development and assessment. For example, GenAI can be used to provide interactive instructional support, provide personalised feedback at scale, and automate the creation and implementation of assessments (Yan et al., 2024; von Davier, 2026; Giannakos et al., 2024). Second, GenAI challenges our existing assumptions of our learning practices and calls for novel ways for assessment. For example, while GenAI can increase performance in certain situations, it can also limit human agency and result in overreliance on AI (Darvishi et al., 2024; Fan et al., 2024; Stadler, Bannert and Sailer, 2024). Finally, we need to strengthen research methods that are used to study human skill development and assessment in the age of GenAI to avoid challenges recently noted in the literature, such as conflation of learning and performance (Weidlich, 2025; Yan et al., 2025).

This chapter is based on the analysis of empirical evidence published in the research literature. It offers a summary of the existing evidence about effectiveness of GenAI to support existing practices for instruction, feedback, and assessment given their central roles in education and professional development. It also describes the recent conceptualisation of hybrid human-AI skills that recognises the need to support development of human skills while enhancing task performance with the use of GenAI. The chapter concludes by providing implications for practice and policy grounded in existing evidence and promising directions for future research. Box 2.1 provides a glossary of the main terms and types of generative AI and associated techniques in the field of AI in education (AIED).

Generative Artificial Intelligence (GenAI) A branch of artificial intelligence focused on creating new content, such as text, images, or audio, based on patterns learned from existing data. GenAI systems, like large language models, can generate human-like responses, creative works, and solutions in various formats.

Large Language Models (LLMs) A type of machine learning model, often based on deep learning, that is trained on vast amounts of text data. LLMs are capable of generating coherent and contextually relevant text, answering questions, and performing a wide range of language tasks (e.g. GPT, BERT).

Diffusion Models A class of generative models that create high-quality data (such as images or sound) by simulating a gradual process of adding noise and then reversing it to recover the original signal. They are particularly known for their ability to generate realistic, high-resolution images.

GPT (Generative Pre-trained Transformer) A type of large language model that uses a transformer architecture. GPT models are pre-trained on vast datasets and fine-tuned for specific tasks, capable of generating human-like text across a variety of contexts.

BERT (Bidirectional Encoder Representations from Transformers) A pre-trained transformer-based language model designed for natural language understanding tasks. Unlike GPT which is a unidirectional model, BERT processes text bidirectionally, improving its ability to understand context and meaning in sentences. Its “understanding” of text context has shown promising results for text analysis in education.

Prompt and Prompt Engineering A prompt is the input text or instruction given to a GenAI model to produce a specific output. Prompt engineering involves designing and refining prompts to achieve the desired responses or behaviours from the GenAI model, often through trial and error.

Zero-shot Prompting A method of prompting where the model is expected to perform a task without having been explicitly trained on that task.

First-shot Prompting A variant of prompt engineering where the model is given a single example or instruction before performing the task.

Chain-of-thought Prompting A prompting technique where the model generates intermediate reasoning steps before providing the final answer, helping improve accuracy on complex tasks.

Retrieval-Augmented Generation (RAG) A hybrid approach combining information retrieval-based techniques with generative AI models. RAG models retrieve relevant information from large databases or knowledge sources and use that data to generate more accurate and contextually informed outputs.

Intelligent Tutoring Systems AI-based educational software that provides personalised instruction and feedback to students. These systems simulate one-on-one tutoring by adapting content, pace, and difficulty based on the learner’s needs and performance.

Learning Analytics The measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimise learning and the environments in which it occurs. It leverages data to improve teaching practices and student outcomes.

Learning Analytics Dashboard A visual tool that displays real-time data and insights about learners’ progress, behaviour, and engagement. Dashboards typically provide instructors and students with actionable feedback to support decision-making and interventions.

Feedback Feedback is “a process in which learners make sense of information about their performance and use it to enhance the quality of their work or learning strategies” (Henderson et al., 2019).

Feedback Literacy The ability to understand, interpret, and effectively use feedback in the learning process. Feedback literacy involves knowing how to respond to feedback, act on it, and integrate it into one’s learning strategies.

Existing practices

Enhanced instructional support

Providing enhanced instructional support at scale is one of the most prominent areas for the use of GenAI in education (Yan et al., 2024). This is grounded in the idea of making use of GenAI for developing systems that can offer personalised learning support. The idea of personalised learning support is grounded in Bloom’s (1984) “two-sigma problem” showing the significant benefits of one-to-one instruction over other forms of instruction. Before widespread use of GenAI, the effectiveness of personalised learning support has been long studied in the literature on artificial intelligence in education (du Boulay, Mitrovic and Yacef, 2023), particularly focusing on intelligent tutoring systems (Graesser, Conley and Olney, 2012; Molenaar, 2021) and resulted in the development of many effective tutoring systems – e.g. SQL-Tutor (Mitrovic and Ohlsson, 2015), MetaTutor (Azevedo et al., 2022), and Cognitive Tutors (Koedinger and Aleven, 2015). Especially relevant to today’s attempts to provide personalised learning support are intelligent tutoring systems such as AutoTutor (Nye, Graesser and Hu, 2014) and BEETLE (Dzikovska et al., 2014) that were already designed to provide tutoring through dialogue in natural language. This research also informed the development of many commercial tutoring systems such as MATHia based on Cognitive Tutor (Almoubayyed et al., 2023) and ALEKS (Fang et al., 2018). However, rapid development of such systems still remains a challenge.

GenAI offers promising approaches that can be used for rapid development of instructional systems for personalised support. Specifically, GenAI, through the use of large language models, can be leveraged to develop tutoring chatbots. A prominent example is Khan Academy’s Khanmigo chatbot, which makes use of large language models to conduct scaffolded, Socratic-style tutoring across diverse subject areas (Khan, 2025). As one of many emerging GenAI chatbots in education, Khanmigo illustrates how these technologies can scale personalised learning support and expand opportunities for learner autonomy and exploration. However, at the time of writing of this chapter, there have been no studies published that evaluated the effectiveness of Khanmigo on learning (although at least one pre- registered RCT is ongoing in Canada).

Evidence on the effectiveness of GenAI to enhance instructional support is still emerging and offers mixed support. For example, a randomised controlled trial conducted at Harvard University showed the significant effects (0.73-1.3 standard deviations) of an AI Tutor – ChatGPT powered system – over those attending in-person active learning classes in an undergraduate physics course (Kestin et al., 2025). The World Bank has recently reported the findings of a randomised controlled trial in nine secondary schools in Nigeria (De Simone, 2025). In the trial, students were randomised in the treatment group that received access to Microsoft Copilot based on GPT-4 in an after-school programme and the control group who did not have access. The students in the treatment group received teacher instructions on how to use Copilot including the prompts and worked in pairs with other students. The results showed the positive effects of this intervention with the effect size of 0.31 standard deviations. However, this effect size was lower than the average effect size noted in the meta-analyses of the effectiveness for intelligent tutoring systems – i.e. 0.42–0.57 standard deviations according to Ma et al. (2014) and 0.66 according to Kulik and Fletcher (2016). This suggests that past AI tutors might be more effective, albeit the difference in context. Nevertheless, the World Bank study findings aligns with the range observed in promising computer-assisted learning interventions reviewed by Escueta et al. (2020), who identified effect sizes between 0.18 and 0.63 standard deviations for personalised and adaptive programmes, particularly in mathematics. The World Bank study also showed that the students with high prior academic performance (as also shown by Lehmann et al. (2025) and high socio-economic status particularly benefited from the interventions. While these findings come from a context where socio-economic disparities are likely more pronounced than in most OECD countries, they still suggest that GenAI-based tutoring systems may disproportionately benefit certain groups of students. Future research in diverse educational settings is needed to corroborate this pattern.

The way how a GenAI-based tutoring system is configured and used may have profound implications on learning. In a large-scale field experiment in high school math classrooms, Bastani et al. (2024) found that while GPT-4-based tutors improved performance during use (up to 127%), students who used a standard chatbot akin to ChatGPT performed worse (17% lower performance) than the control group once access to the chatbot was removed. The control group students did not use any GenAI-based instructional support in addition to the conventional classroom instruction. This negative effect of GenAI use was mitigated by a version designed with learning safeguards, suggesting that poorly configured systems may undermine long-term learning.

Similarly, Lehmann et al. (2025) showed that a ChatGPT-based tutor for Python programming had no overall effect on learning, but its impact depended on usage patterns. Students who heavily relied on the ChatGPT-based tutor tended to cover a broader range of optics but developed shallower understanding, while those who used it to complement learning gained deeper understanding. The use of the ChatGPT- based tutor also widened performance gaps between students with high and low prior knowledge. In summary, the Bastani et al. (2024) and Lehmann et al. (2025) studies highlighted the importance of instructional strategies embedded in the design of GenAI-based instructional systems and the way how students use them are two key factors that need to be considered in research and practice. Future research should also examine how best to combine generative and conventional AI models, since, adding LLMs investigate effectiveness of different pedagogical approaches GenAI-based instructional systems use and factors (e.g. metacognitive skills) that explain different usage patterns of students. It is also important to examine how conventional and generative AI models can be effectively integrated. Such integration should enhance human learning by combining the strengths of each layer while minimising risks, including hallucinations that may compromise reliability (Qian et al., 2026).

Effective instructional support from GenAI-powered systems requires alignment with the proven experience in developing intelligent tutoring systems. Although frequently mentioned in recent developments, GenAI-powered instructional systems do not follow a typical architecture of intelligent tutoring systems. At the core of intelligent tutoring systems are learner models, tutor models, domain models, and user interface (Nkambou, Bourdeau and Mizoguchi, 2010). Most GenAI–based instructional systems primarily make use of large language models to cover functions of all these four components. While user interfaces through natural language interaction can be quite advanced with LLMs, the support for the other three components is less obvious. Although the functions of the domain and tutor models can be performed by LLMs to some extent, there is presently limited research and evidence on how their quality can be assured. Specifically, due to the stochastic nature of LLMs, they cannot guarantee reliability of information covered in the domain model due to tendency to hallucinate (Ji et al., 2023). Existing research shows that LLMs can easily be distracted and inconsistently comply with the instructions provided in the underlying prompts (Hwang et al., 2025; Liu et al., 2025; Zhao et al., 2024). Therefore, future research is needed to assess the extent to which LLMs can consistently comply with a particular tutoring strategy to offer long-term effects. Moreover, future work is needed to develop effective computational approaches that can increase compliance of LLMs to promote effective tutoring strategies over time. Finally, there is very little evidence in the literature that existing GenAI-based instructional systems offer any learner models. They are precisely needed to understand individual student needs based on the tracing of their knowledge development (Abdelrahman, Wang and Nunes, 2023) and learning approaches they take to provide adaptive and personalised support. Recent evidence by Borchers and Shou (2025) shows that LLM-only tools offer only limited adaptivity compared to conventional intelligent tutoring systems. Future research is needed to address these critical architectural needs and identify effective ways of the integration of LLMs within tutoring architectures to enhance instructional effectiveness.

Enhancing instructional support does not necessarily need to be done through providing direct instruction to students. Teachers can also be beneficiaries of GenAI for tasks related to preparation for teaching and during the actual act of teaching. For example, GPTeach is an interactive teacher training tool that enables novice educators to practice teaching with GPT-simulated students. Evaluations of GPTeach have shown that it can enhance teachers’ preparedness and confidence, offering valuable practice opportunities tailored to varied teaching scenarios (Markel et al., 2023). Relatedly, Tutor Copilot is a GenAI-powered system that provides real-time, expert-like guidance to tutors during live tutoring sessions (Wang et al., 2025). This approach is particularly relevant for supporting students by mobilising a less experienced workforce and addressing the issue of teacher shortages. In a randomised controlled trial, Wang et al. (2025) evaluated the effectiveness of Tutor CoPilot. The study involved 900 tutors and 1 800 K-12 students from historically under-served communities. Results indicated that students whose tutors had access to Tutor CoPilot were 4 percentage points more likely to master topics, with the most significant benefits observed among students of lower-rated tutors, who experienced a 9 percentage point improvement. Additionally, tutors using Tutor CoPilot were more inclined to employ high-quality pedagogical strategies, such as asking guiding questions, and less likely to provide direct answers. Although the studies with GPTeach and Tutor CoPilot show much promise, future research is needed to understand the uptake and effectiveness of such systems in diverse educational and international contexts. Equally important is future research to understand how effectively tutoring practices supported by Tutor CoPilot are internalised by teachers over time as part of their professional development, and whether they may lead to overreliance on GenAI, potentially hindering the development of teachers’ human teaching skills.

GenAI can support teachers in a range of tasks, with mixed results regarding their effectiveness and efficiency. Although lesson planning is frequently discussed as one of the key areas of teaching practice that can benefit from the use of GenAI (Moundridou, Matzakos and Doukakis, 2024), evidence about its effectiveness is still emerging. For example, Dennison et al (2025) evaluated Shiksha Copilot, an AI- assisted lesson planning tool deployed in schools in India. In a large-scale mixed-methods study, including interviews, surveys, and usage logs, the study found that teachers used Shiksha Copilot to meet administrative documentation needs and support their teaching. The use of the tool was associated with a reduction in lesson planning time, with small to large effect sizes (Cohen’s d = 0.371 – 0.658), and lowered teaching-related stress (Cohen’s d = 0.436), while promoting a shift toward activity-based pedagogy. However, systemic challenges, such as staffing shortages and administrative demands, constrained broader pedagogical change. In contrast, Selwyn et al. (2025) conducted interviews with teachers about their experiences with GenAI tools for administrative tasks in Sweden and Australia, highlighting the significant work teachers self-report investing in reviewing, repairing, and reworking AI-generated outputs. Their findings suggest that the promise of time-saving in AI tools may overlook the complex professional judgments teachers must make regarding pedagogical appropriateness, social relations, and educational value. However, Selwyn et al.’s (2025) findings are based on self-reports (i.e. interviews), which do likely not reliably estimate time spent on technology use (Parry et al., 2021). Usage log analysis, on the other hand, offers a more accurate and less biased approach. In contrast to the Dennison et al. (2025) study, which is grounded in usage log analysis to provide more reliable usage time estimates, the Selwyn et al. (2025) study highlights the need to account for the hidden labour of teachers that may not be captured by usage logs. Yet, given that Dennison et al. (2025) compared GenAI-supported lesson planning with a non-GenAI baseline, some of this hidden labour may already have been reflected in their analysis. This highlights the importance of fair and contextually comparable evaluation frameworks that consider how GenAI tools are implemented and how teacher time use is measured across studies. Given the essential role teachers play in education, this underscores the importance of exploring design principles, organisational adoption strategies, and the broader implications of adopting GenAI technologies for teaching support.