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

One of the most pressing areas of application for GenAI in education is the provision of automated feedback. Feedback represents a persistent challenge in higher education, where increasing student numbers are not matched by proportional increases in teaching resources (Paris, 2022). It is also a challenge at school level in contexts where student/teacher ratios are high, or when teachers teach a subject with few curriculum hours (and thus many classes and students). This structural tension has made it difficult to offer timely, targeted, and individualised feedback at scale (Pardo et al., 2017) that follows principles of effective (Hattie and Timperley, 2007; Henderson et al., 2019) and learner centred feedback (Ryan and Henderson, 2021). Feedback can improve learning progression (Wisniewski and Zierer, 2020) and support development of relationships between students and educators (Dai, Tsai and Gašević, 2025; Henderson, 2015). As shown in the remainder of this section, GenAI holds strong promise for enabling the rapid and scalable generation of feedback across multiple modalities, with the potential to enhance feedback scalability, quality, and even feedback literacy (Nieminen, 2023; Baker, 2026)

GenAI has been found to be promising to offer feedback on students’ written products in higher education. In a recent study, Dai et al. (2024) compared feedback generated by large language models to that provided by human tutors. Their study compared feedback on readability, similarity of positive and negative points identified, and levels feedback was provided on. The levels of feedback were grounded in Hattie and Timperley’s (2007) seminal framework that distinguishes feedback on task (correctness), process (learning strategies), self-regulation (monitoring learning), and self (personal traits and motivation). In this framework, higher-level feedback, particularly at the process and self-regulation levels, is widely recognised as more educationally valuable and a key indicator of feedback quality because it supports deeper learning and learner autonomy. The Dai et al. (2024) findings revealed that GenAI (i.e. GPT-3.5 and GPT-4) tended to produce more readable and stylistically polished feedback with quite a large effect size (d = 1.79) than feedback produced by human educators. This finding was somewhat unsurprising, given that human assessors often operate under strict time constraints and offer rather succinct feedback. However, the study also revealed limited alignment between what GenAI produced feedback and human tutors identified as strengths and weaknesses in student work according to a rubric. Dai et al. (2024) also showed that GPT (particularly GPT-4) models were able to produce feedback that offered guidance about future choice of learning strategies (i.e. process level feedback) in over 97% of feedback instances. Interestingly, this was higher than what was observed in human-provided feedback which was on process level in about 80% of feedback instances. However, GPT-4 was much less able to produce feedback on the self-regulation level, which was only in 17% of cases. However, even that was higher than that provided by human tutors who only offered self-regulation level feedback in 11% of cases. This just highlights the challenge of providing feedback on self-regulation levels where learners are guided to monitor their own learning. This challenge is particularly important in the age of GenAI as discussed later.

GenAI can also be used to generate feedback guiding students based on the insights of predictive modelling. Early prediction of students at risk of failing or dropping out have been at the core of research and practice in learning analytics for a long time (Molenaar, 2021; Gašević et al., 2016; Wang and Mousavi, 2022). However, translating insights from predictive modelling to actionable feedback has received much less attention. A notable example with much success in improving student learning and experience was the OnTask system that allowed educators to manually write rules to generate personalised feedback based on student data (Pardo et al., 2017). Although much more efficient than manual feedback writing at scale, it still could not translate granular insights of predictive modelling to actionable feedback (e.g. advice on which practice exercises to take). To address this challenge, Liang et al. (2024) proposed an approach for transforming insights from predictive modelling to personalised feedback with the use of GPT-4 which was rated by experienced educators as “readily applicable to the course” (Liang et al., 2024, p. 75) and higher on readability, relational characteristics, and specificity than human-provided feedback. However, at present, future research is needed to assess the effectiveness of such personalised feedback on learning outcomes, student retention, and the extent to which students actually use and act upon the AI-generated feedback.

GenAI can also be used to check the quality of feedback to promote best practices at scale. Previous research demonstrates the potential of the use of conventional machine learning to recognise whether human produced feedback followed established models for feedback. For example, Osakwe et al. (2022) used a XGboost machine learning model trained on established linguistic features (e.g. cohesion or use of cognitive words) to identify self, task, and process levels of feedback with accuracy values of 0.87, 0.82, and 0.69, respectively. In a recent study, Aldino et al. (2024) evaluated the performance of GPT-3.5 with zero-shot prompts to identify elements of learner-centred feedback on a large dataset of feedback messages (>16k) in higher education. GPT-3.5 showed some promising results with accuracy in the range of 0.53-0.97 across the seven attributes of learner-centred feedback. However, GPT-3.5 was consistently outperformed by conventional machine learning models (i.e. XGBoost and Random Forest) based on linguistic features (e.g. cohesion and word count), while BERT almost always performed reliably (accuracy 0.91-0.99) (see Box 2.1 for definition of BERT). Higher accuracy of traditional machine learning over ChatGPT was also shown in evaluation of the quality of peer feedback (Hutt et al., 2024). Similarly, Dai et al. (2025) showed that GPT-4o was able to identify nine out of ten relational characteristics of feedback with an average accuracy exceeding 80%. For example, the model successfully recognised feedback that acknowledged students’ strengths, offered balanced critical comments, and included actionable suggestions for improvement. Yet, they found no significant increase in the use of few-shot prompting strategies over zero-shot prompting. These findings suggest that while GPT prompting approaches offer a promising and accessible entry point due to their lower technical barrier, achieving consistently high accuracy still requires conventional machine learning methods and language models like BERT.

The differences in performance between GenAI and human educators create new opportunities to complement and enhance the effectiveness of human tutors. For example, GenAI can provide positive and negative points in feedback, which human educators can use as suggestions to enhance their own feedback drafts. This is also suggested by Lu et al. (2024) who argue that GenAI can offer immediate and personalised feedback on lower- order concerns in written products such as grammar, vocabulary, and sentence structure. Their premise is that this may allow teachers to focus on higher-order thinking skills, content depth, and argumentation, where human judgment remains crucial. The results of the Dai et al. (2024) study indicate that GenAI can help enhance human feedback with effective feedback practices (Hattie and Timperley, 2007). This hybrid approach holds potential to enhance efficiency without compromising pedagogical judgment and future research and practice should evaluate its effectiveness. Despite all these promises, specialised tools that promote this hybrid approach for educators are in early days. For example, Feedback Copilot was developed to incorporate principles of co-design to create effective user interfaces that incorporate the use of GenAI (Pozdniakov et al., 2023). Efficacy of Feedback Copilot is yet to be evaluated in practice and highlights the important research gap and direction for future research.

A growing body of research has explored how students perceive and respond to feedback generated by GenAI, particularly in comparison to human feedback. Studies have shown that students tend to act more readily on feedback from human instructors than from GenAI tools (Zou et al., 2025; Lu et al., 2024). Students often found GenAI feedback to be specific and clear though, especially in technical tasks (Ouyang et al., 2024; Roest, Keuning and Jeuring, 2024; Zhang et al., 2024). However, several studies also highlight concerns regarding the perceived usefulness and trustworthiness of GenAI-generated feedback (Escalante, Pack and Barrett, 2023; Er et al., 2024). Although these studies differ in focus and methodological design, ranging from quasi-experimental evaluations of learning outcomes (Escalante, Pack and Barrett, 2023) and randomised controlled comparisons of instructor and AI feedback (Er et al., 2024) to large-scale perception studies in higher education contexts (Nazaretsky et al., 2024), they consistently point to lower perceived usefulness and trust in AI-generated feedback relative to human feedback.

Overall, current evidence suggests that while GenAI feedback can match human feedback in measurable learning outcomes, it does not replicate its pedagogical value or social credibility. For example, Escalante et al. (2023) found no significant difference in learning outcomes between students receiving feedback from GPT-4 and those receiving tutor feedback, although participants were evenly split in their perceptions of usefulness. While this might appear to suggest functional equivalence, comparable performance does not imply pedagogical interchangeability. As shown in the recent meta-analysis by Kaliisa et al. (2025) across 41 studies, AI-generated and human feedback yield statistically similar learning gains, yet students perceived human feedback as more credible and meaningful. This distinction points to the broader role of feedback in shaping motivation, evaluative judgment, and learner trust, dimensions that remain difficult for GenAI systems to reproduce even when outcome measures are equivalent. Similarly, Er et al. (2024) reported that human feedback was perceived as significantly more useful, and students who received it showed greater improvement in lab scores in Java programming. In a related study, Nazaretsky et al. (2024) found that students’ perceptions of feedback varied depending on the provider’s identity. When the feedback source was unknown, students rated AI feedback more favourably; however, when the source was revealed, they placed greater trust in human feedback. Although highly relevant to trust, the effects of hallucinations in GenAI on feedback uptake has received little attention in the literature and warrant future attention. Perceptions of fairness have also been somewhat contradictory: while some studies found that GenAI feedback was rated as fair by students (McGowan, Anderson and Smith, 2024; Rudolph et al., 2024), other studies observed the opposite (Er et al., 2024).

GenAI feedback has also shown potential to support important metacognitive processes. For instance, Tang et al. (2024) demonstrated that structured GenAI feedback on writing tasks significantly improved students’ self-assessment accuracy, which is a key skill for independent learning. However, this potential is not always realised. Jin et al. (2025) found that students with low feedback literacy engaged only minimally with a GenAI-based support tool, often due to a mismatch between the tool’s responses and their expectations. These findings suggest that the impact of GenAI feedback depends not only on its technical qualities but also on learners’ readiness to interpret and apply it effectively. As Zhan and Yan (2025) argue, fostering feedback engagement in a GenAI context requires the explicit development of students’ feedback literacy, including skills in prompt engineering, evaluative judgment, and metacognition, to facilitate deeper and more meaningful interaction with GenAI in feedback practices. Future research should aim to (a) investigate the extent to which feedback literacy of students can be promoted to more effectively and critically engage with AI-generated feedback and (b) understand whether feedback literacy enables learners to improve their learning outcomes when using AI-generated feedback.

GenAI can support generation of feedback in different modalities that goes beyond textual feedback. For example, learning analytics offers dashboards as an alternative and cost-effective approach to provide feedback based on analysis of student data (Matcha et al., 2020). However, learning analytic dashboards have not achieved their full potential (Kaliisa et al., 2024). One of the main reasons for this is relatively limited data visualisation literacy of educators and students to understand and translate insights from different statistics and charts into action (Donohoe and Costello, 2020; Pozdniakov et al., 2023). To address the limitations in visualisation literacy, GenAI can offer two complementary approaches.

First, GenAI can provide a layer guiding educators and learners to improve their abilities to comprehend dashboards accurately. For example, Yan et al. (2024) developed a tool called VizChat, which allows students and educators to interact with a chatbot to help them understand the data shown in the dashboard by asking questions (Figure 2.1). When configured in a proactive mode (i.e. used scaffolding questions), VizChat significantly enhanced the comprehension of learning analytic dashboard compared to both passive chat mode (i.e. responding to student queries a la ChatGPT) and standalone scaffolding (Yan et al., 2025). Importantly, these benefits continued to persist even when the students did not have access to proactive VizChat. Building on these promising results, future research should investigate the extent to which learners and educators can transform insights obtained from learning analytic dashboards into effective learning and teaching practice thanks to GenAI.

Second, GenAI can be used to generate feedback in other forms than text, for example in the form of data comics. Data comics follow established principles of comic strip genres (e.g. Manga) and are generated by prompting multimodal language models to generate images based on analytic insights (Milesi et al., 2024). Data comics were applied in simulation-based learning for healthcare professionals, where student nurses engage in highly collaborative learning scenarios in physical spaces (Martinez-Maldonado et al., 2023). Data comics (see Figure 2.2) aim to present feedback in a more accessible, emotionally engaging format. Qualitative evidence suggests that data comics can improve student motivation and reflective engagement; some students even reported feeling seen or valued (Milesi et al., 2024). However, some students in higher education found this approach as not sufficiently professional and potentially perpetuating biases (e.g. all nurses generated in data comics were women and the doctor was a man). Expanding positive aspects of GenAI-powered data comics, future research is needed to understand their effectiveness across different educational contexts and levels, while minimising potential negative effects. The same idea could also be applied to the AI generation of video clips based on the multimodal learning analytics.

Feedback on learning processes (e.g. goal setting, strategy use, and self-monitoring) is underrepresented in existing literature on AI in education (Gašević, Greiffand Shaffer, 2022). While learning is never fully transparent, learning analytics has made substantial progress in visualising and interpreting the otherwise invisible dynamics of the learning process, which can provide educators and learners with actionable insights into how learning unfolds. With advances in learning analytics, we can now analyse fine-grained trace data such as clickstreams, mouse movements, and other digital traces of student activity to identify cognitive, metacognitive, affective, and motivational processes (Molenaar et al., 2023). Existing research in learning analytics has also shown that such approaches can offer insights into nuanced details about learning strategies learners used (Lämsä et al., 2025). Moreover, existing research has shown that learning processes can explain more variance in student essay scores than linguistic essay properties (e.g. text cohesion) that are commonly used in automated essay scoring (Raković et al., 2022). However, translating insights from the underlying representations of data analytic models – e.g. process maps, networks, or descriptive statistics – requires considerable data literacy, which can be a barrier for many educators and learners.

GenAI holds a strong potential to support feedback practices on learning process due to their ability to combine insights from data analytic models about learning processes with instructional information and subject matter content. By combining all these perspectives, GenAI can produce contextually relevant and personalised learning support – e.g. feedback or scaffolds – that aim to guide learners to improve their learning processes and performance (Thomann and Deutscher, 2025; van der Graaf et al., 2023). As outlined in Box 2.2, LLMs can be prompted with insights of real-time analytics of processes of self-regulated learning along with information about principles for effective feedback (Hattie and Timperley, 2007) and relevant content information, to generate personalised scaffolds (Li et al., 2025).

The potential of process feedback has profound implications in the age of GenAI. As students can now easily use GenAI tools to produce polished final products, it becomes increasingly important to assess how students engage with the learning process, rather than focusing solely on the end result. Moreover, process feedback can highlight important critical challenges - learners may face when using GenAI (e.g. metacognitive laziness and overreliance). The transformative potential of process assessment is further discussed below.

The FLoRA platform is a suite of integrated tools that allow students to engage in a range of self-regulated learning activities (Li et al., 2025): highlighting text, searching for information, taking notes, planning, using tools like ChatGPT, and more. All these interactions are logged and analysed using algorithms developed to map student actions onto cognitive, metacognitive, affective and motivational processes. These insights can then be used in real-time analytics to understand whether students use effective learning strategies, plan their learning, and regularly monitor how they progress with respect to task expectations. The insights from analytics can then be fed into LLMs to generate timely process feedback for learners. Such LLM-enabled feedback is more adaptive and context-sensitive than earlier rule- based systems (see the Instruction Panel in Figure 2.3). LLMs can interpret information about the learner, the task, and prior actions to generate personalised scaffolds in natural language, which learners can understand and act on. LLMs can also contextualise feedback to the topics and content learners are studying, make it easier to scale across different subject domains and tasks. Such process feedback can be delivered at predefined moments or triggered dynamically based on the observed learning processes. Li et al. (2025) showed that students tend to follow these GenAI-produced scaffolds closely and thus improve their learning strategies, which were previously shown to be positively associated with improved performance in reading and writing tasks. Similarly, Alnashiri et al. (2026, under review) showed that personalised feedback targeting particular processes of self-regulated learning (e.g. monitoring) is associated with improved writing performance and post-test knowledge in secondary education.

The success of systems like FLoRA demonstrates how GenAI can extend personalised learning support beyond individual classrooms and subjects. As GenAI tools become increasingly embedded in students’ learning activities, concerns have emerged about overreliance and metacognitive laziness, where learners depend on AI-generated responses rather than actively monitoring and directing their own learning. Self-regulated learning is therefore central to maintaining students’ lifelong adaptability and capacity to engage critically with AI-powered tools, yet teachers often struggle to observe and support these processes across diverse classrooms. Traditional feedback and scaffolding approaches are rarely scalable and tend to overlook subtle individual differences in learners’ strategies and needs. By integrating real-time analytics with LLM-based feedback, GenAI systems can help bridge this gap, providing timely, context-aware support that complements teachers’ work and sustains students’ metacognitive engagement. Such scalable and individualised guidance is particularly important as education systems seek to foster independent and reflective learners in the age of AI.