> **来源：[研报客](https://pc.yanbaoke.cn)** # THE POTENTIAL IMPACT OF ARTIFICIAL INTELLIGENCE ON EQUITY AND INCLUSION IN EDUCATION OECD ARTIFICIAL INTELLIGENCE PAPERS August 2024 No. 23 # OECD EDUCATION WORKING PAPERS SERIES OECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed herein are those of the author(s). Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcome, and may be sent to the Directorate for Education and Skills, OECD, 2 rue Andre-Pascal, 75775 Paris Cedex 16, France. This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions. Comment on the series is welcome, and should be sent to edu_contact@oecd.org. This working paper has been authorised by Andreas Schleicher, Director of the Directorate for Education and Skills, OECD. www.oecd.org/edu/workingpapers # Acknowledgements This working paper was prepared as part of the OECD Education for Inclusive Societies project. The authors would like to thank Hannah Borhan, Lucie Cerna, Shivi Chandra, Marc Fuster Rabella, Paulo Santiago and Quentin Vidal for their valuable feedback and comments. Thanks to Eda Cabbar and Daiana Torres Lima for their editorial work. # Abstract This working paper reviews the impact of artificial intelligence (AI) on equity and inclusion in education, focusing on learner-centred, teacher-led and other institutional AI tools. It highlights the potential of AI in, e.g. adapting learning while also addressing challenges such as access issues, inherent biases and the need for comprehensive teacher training. The paper emphasises the importance of balancing the potential benefits of AI with ethical considerations and the risk of exacerbating existing disparities. It highlights the need to address privacy and ethical concerns, enhance cultural responsiveness, manage techno-ability and provide continuing professional learning in AI. Additionally, the paper stresses the importance of maintaining educational integrity amidst growing commercial influence. It encourages research on AI tools' implications for equity and inclusion to ensure that AI adoption in education supports a more equitable and inclusive learning environment. # Table of contents # Acknowledgements 3 # Abstract 4 # 1 Introduction 7 # 2 Definitions, guidelines and conceptualisations 9 Definitions of artificial intelligence, equity and inclusion 9 Guidelines and frameworks related to artificial intelligence in education 11 Taxonomy to analyse the impact of artificial intelligence on equity and inclusion in education 12 # 3 Learner-centred tools to support equity and inclusion 14 Opportunities of learner-centred AI tools for equity and inclusion 14 Challenges of learner-centred AI tools for equity and inclusion 17 # 4 Teacher-led tools to support equity and inclusion 25 Opportunities of teacher-led tools for equity and inclusion 25 Challenges of teacher-led tools for equity and inclusion 30 # 5 Other institutional tools that can foster equity and inclusion 34 Opportunities of institutional tools for equity and inclusion 34 Challenges of institutional tools for equity and inclusion 35 # 6 Conclusions 37 Embracing the potential for adaptive learning while addressing privacy, ethical and accountability issues 37 Recognising the potential to enhance cultural responsiveness while keeping in mind inherent biases 37 Balancing the potential for accessibility with challenges such as techno-ability and impact on socio-emotional skills 38 Developing and improving teacher training in AI 38 Exploring how to maintain educational integrity amidst the growing commercial influence in the sector 39 Encouraging research on the implications of AI for equity and inclusion in education, and clarifying the role of institutions at the national level in its systematic implementation 39 # References 41 # Tables Table 2.1. Conceptualising equity and inclusion regarding digital technologies in education 10 Table 2.2. AI techniques and technologies 11 Table 2.3. Taxonomy of AI tools in education 13 Table 4.1. Teacher activities and AI 27 # Figures Figure 2.1. Definitions of equity and inclusion in education 10 Figure 3.1. Quantity and quality of digital resources by socio-economic profile of schools (2022) 19 Figure 4.1. Continuing professional learning needs by school characteristics (2018) 33 # Boxes Box 3.1. Algorithmic biases 22 Box 4.1. Aspects of teaching that AI could support 27 # 1 Introduction Artificial intelligence (AI) has sparked transformative possibilities in many facets of human life in the current era of rapid technological advancement. AI tools continue to make headlines, while critiques also emerge, citing algorithmic biases, privacy concerns, accountability issues, implications for equity and inclusion, and others. As a general-purpose technology, AI is expected to transform and is already changing a wide range of areas, from advertising, agriculture, and criminal justice, through education, finance, health, marketing, science and security to transport (OECD, 2019[1]). Benefits of AI use in these areas include improving the efficiency of decision making, saving costs and enabling better resource allocation (ibid.). AI might also have profound impacts on education systems, including on equity and inclusion. Therefore, this working paper delves into some debates around the connection between AI, equity and inclusion in education. By exploring the opportunities and challenges that arise as AI tools reshape the educational landscape, it aims to set the ground for a meaningful discourse on ensuring equitable and inclusive education in times of AI. To this end, the working paper has three objectives. First, it aims to provide policy makers with a categorisation of AI tools that can support equity and inclusion in education. Following Holmes and Tuomi (2022[2]), the AI tools have been categorised into learner-centred, teacher-led, and other institutional tools. This taxonomy is particularly useful when addressing the question of who the primary user or the primary beneficiary is. Second, in categorising the AI tools and providing examples, the working paper aims to highlight that AI solutions in various areas already exist, there is demand for them and they are likely already being used by educational institutions across OECD countries. New AI tools are being introduced in classrooms without much supervision or oversight in many countries. This kind of "unchecked adoption" of AI tools can result in some schools, often those that can afford the technology, reaping some of the benefits (but also potential risks) sooner than others. This leads to the final objective of this working paper, namely underscoring that the use of AI tools in education occurs mainly without systematic oversight and regulation. To this end, the paper outlines some of AI tools' significant opportunities and challenges. While opportunities and challenges are categorised based on the learner-centred, teacher-led and other institutional tools taxonomy, there is great overlap among them, and, ultimately, almost all the tools discussed were created to help students learn and address students' needs (whether directly or indirectly by, e.g. assisting teachers). In particular, challenges mentioned in one section often extend and apply to AI tools described in other sections. While the working paper does not provide an exhaustive list of AI tools, many are already present in schools, along with the opportunities and challenges they bring. As such, the question emerges to what extent policy makers should aim to support or discourage the use of the tools from a centralised perspective. While this working paper does not seek to provide a comprehensive answer at this early stage, it is the right time to ponder this question. The paper was conducted using desk-based research, mainly in English. As such, the tools presented may not be relevant in non-English-speaking countries. Nevertheless, the opportunities and challenges are likely applicable regardless of location. Furthermore, little information is available on country-level approaches to AI in education. This probably partially stems from the fact that few education systems have implemented system-level guidance or policies. Future research should place a greater focus on this aspect. Finally, the opportunities presented in this paper should be viewed more as hypotheses rather than evidence-based evaluations. Indeed, for many of the (types of) AI tools, there are only a handful of robust evaluations for the potential benefits or improvements in student learning and well-being (Holmes, 2023[3]). Where these are available, they are referenced. The working paper focuses mostly on school education. This field is evolving rapidly and new AI tools are emerging daily. Challenges outlined in this paper are also being constantly addressed. In a year, some of the content will likely be out of date. As such, caution is required when reading this analysis after a prolonged time after publication. That said, the information in this paper can be used to take stock of where the field is at the present and how the field has evolved in a few years. The working paper is structured as follows. The next section provides a framework for analysis in regard to definitions and guidelines published on AI in education, as well as a taxonomy to analyse the impact on equity and inclusion. Section 3 describes opportunities of learner-centred tools, such as adapting learning, content enrichment, support for learners with special education needs, and information and advice. However, these tools also face challenges such as ensuring access, combating techno-ability $^{1}$ , addressing bias, maintaining socio-emotional learning, and balancing AI integration with privacy and accountability concerns. In section 4, the paper elaborates on teacher-led tools. It discusses the potential of supporting teaching with AI-powered robots, curating learning materials, assisting in assessment and classroom management, identifying some special education needs, and providing continuing professional learning opportunities. Yet, these benefits are weighed against challenges like the high costs of AI tools, the need to balance commercial interests with educational objectives, and the imperative of equipping educators with the necessary AI knowledge and skills. In section 5, the paper explores institutional tools that can foster equity and inclusion, with opportunities such as increasing the efficiency of admissions, better identifying students at risk of early leaving from education and training, and data-based decisions. However, these tools present challenges, including addressing the complexities and ethical considerations involved in their implementation. The final section concludes and provides some overarching conclusions and policy implications. # 2 Definitions, guidelines and conceptualisations # Definitions of artificial intelligence, equity and inclusion Before discussing the impact of AI on equity and inclusion in education, it is necessary to define AI and explore how AI can be applied in educational contexts in general. Defining AI is a crucial yet challenging starting point in the ever-changing realm of technology. This working paper adopts the definition of the OECD as recommended by the Council on Artificial Intelligence (OECD, 2023, p. 7[4]): "a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment". Other definitions stress AI's potential meaning for society even further, e.g. referring to AI as "a set of sciences, theories and techniques whose purpose is to reproduce by a machine the cognitive abilities of a human being" (Council of Europe, 2024[5]). Given the focus of this working paper on equity and inclusion in education, it is also essential to define these (Figure 2.1). The concepts vary across the literature and in the interpretations of different education systems (Cerna et al., 2021[6]; Varsik, 2022[7]). The OECD Education for Inclusive Societies project offers a comprehensive insight into the critical elements encompassed within countries' definitions of equity and inclusion (OECD, 2023[8]). In regard to equity, the project's definition includes two complementary approaches. First, horizontal equity reflects the overall fair provision of resources to each part of an education system, providing similar resources to those alike. Second, vertical equity involves giving additional resources to disadvantaged groups or schools based on their needs. Equitable education systems are thus defined as those that ensure the achievement of educational potential regardless of personal and social circumstances, including factors such as gender, ethnic origin, Indigenous background, immigrant status, sexual orientation and gender identity, special education needs, and giftedness (Cerna et al., 2021[6]; OECD, 2017[9]). Inclusion is defined as "an on-going process aimed at offering quality education for all while respecting diversity and the different needs and abilities, characteristics and learning expectations of the students and communities, eliminating all forms of discrimination" (UNESCO, 2009, p. 126[10]). More than a particular policy or practice related to a specific group of students or individuals, this definition identifies an ethos of inclusion and communities of learners, which does not only involve an individual dimension but also a communal one. Inclusive education aims to respond to all students' needs beyond school attendance and achievement while improving all students' well-being and participation (Cerna et al., 2021[6]). # Figure 2.1. Definitions of equity and inclusion in education # Equity - Equitable education systems are those that ensure the achievement of educational potential regardless of personal and social circumstances, including factors such as gender, ethnic origin, Indigenous background, immigrant status, sexual orientation and gender identity, special education needs, and giftedness. # Inclusion - An on-going process aimed at offering quality education for all while respecting diversity and the different needs and abilities, characteristics and learning expectations of the students and communities, eliminating all forms of discrimination. Note: The definitions were adopted by the Education for Inclusive Societies (and the previous Strength through Diversity) project. Other organisations, projects, countries and researchers may use different definitions. Source: OECD (2023[8]), Equity and Inclusion in Education: Finding Strength through Diversity, https://doi.org/10.1787/e9072e21-en and UNESCO (2009[10]), Defining an Inclusive Education Agenda: Reflections around the 48th session of the International Conference on Education, https://unesdoc.unesco.org/ark:/48223/pf0000186807 (accessed on 25 March 2024). These definitions are supported by Gottschalk and Weise (2023[11]), who provide a detailed conceptualisation for defining equity and inclusion in regard to digital technologies in education (Table 2.1). Digital equity in education promotes fairness and equity in student access to digital technologies, skills, uses and attitudes. As such, digital tools for equity in education provide additional learning resources for students in need and help them participate fully in education (Gottschalk and Weise, 2023[11]). Digital inclusion, in turn, overcomes barriers to participation based on student differences. Digital technologies for inclusion are then adapted to acknowledge, accept and respect student differences. They also ensure that students feel included, promote their well-being and sense of belonging, and ensure non-discrimination (ibid.). Table 2.1. Conceptualising equity and inclusion regarding digital technologies in education <table><tr><td></td><td>In education</td><td>For equity/inclusion in education</td></tr><tr><td>Equity</td><td>Digital equity in education: Promoting fairness and equity in access to digital technologies (including hardware, software, high-quality broadband, etc.), digital skills, uses and attitudes for all students.</td><td>Digital technologies for equity in education: Using digital technologies to promote equity in education, such as providing additional learning resources for students in need to promote equitable outcomes to help them participate fully in (digital) education.</td></tr><tr><td>Inclusion</td><td>Digital inclusion in education: Overcoming barriers to participation in digital education based on student differences. This would also involve ensuring digital tools in education are designed and used to promote participation and inclusion of all learners.</td><td>Digital technologies for inclusion in education: Adapting digital technologies and learning environments to promote inclusion in education, acknowledging, accepting and respecting student differences. Using digital technologies to promote inclusion in education should aim to ensure students feel included, promote belonging and a sense of well-being, while ensuring non-discrimination.</td></tr></table> Source: Gottschalk and Weise (2023[11]), Digital equity and inclusion in education: An overview of practice and policy in OECD countries, Table 1.1., https://doi.org/10.1787/7cb15030-en. Having defined AI, equity and inclusion, it is worthwhile to explore how AI can be applied in educational contexts before moving on to more specific cases. To that end, differentiating between AI techniques and AI technologies can be a helpful approach (UNESCO, 2022[12]). The former refers to methods, approaches and algorithms used in AI to solve specific tasks or problems (Table 2.2). They are the underlying mathematical and computational processes that enable AI systems to learn, reason and make decisions. AI technologies, in turn, encompass the hardware and software infrastructure that facilitates AI systems' development, deployment and operation. Individual AI tools will then deploy AI techniques and technologies to address a particular issue. For instance, intelligent tutoring systems (section Learnercentred tools to support equity and inclusion) can use a variety of AI techniques (commonly, e.g. machine learning) to train on vast amounts of data and then deploy AI technologies (commonly, e.g. chatbots) to interact with the user. Table 2.2. AI techniques and technologies <table><tr><td></td><td>Definition</td><td>Examples</td></tr><tr><td>AI techniques</td><td>Methods and approaches used to solve specific tasks or problems, as well as underlying mathematical and computational processes that enable AI systems to learn, reason, and make decisions.</td><td>Machine learning algorithms, deep learning, supervised and unsupervised learning, neural networks.</td></tr><tr><td>AI technologies</td><td>Hardware and software instruments, frameworks and platforms that enable the implementation of AI techniques to create AI applications.</td><td>Autonomous agents (avatars, chatbots, robots), image and speech recognition, natural language processing.</td></tr><tr><td colspan="3">Source: UNESCO (2022[12]), K-12 AI curricula: a mapping of government-endorsed AI curricula, https://unesdoc.unesco.org/ark:/48223/pf0000380602 (accessed on 15 January 2024).</td></tr></table> # Guidelines and frameworks related to artificial intelligence in education Given the globalised nature of technology developments, policies addressing the use of AI to foster equity and inclusion in education are not necessarily constrained by country borders. Furthermore, international policy frameworks can influence national directives. This section outlines some prominent guidelines and frameworks focusing on AI in the context of equitable and inclusive education. Within the OECD, the Council's Recommendation on Artificial Intelligence lays the foundation for how governments and other actors can develop a human-centric approach to trustworthy AI (OECD, 2023[4]). As a legal instrument, its principles represent a common aspiration for OECD countries. In regard to equity and inclusion, the first principle targets inclusive growth, sustainable development and well-being. Countries are called upon to consider how AI can advance "the inclusion of underrepresented populations, reducing economic, social, gender and other inequalities" (OECD, 2023, p. 7[4]). Furthermore, the OECD Secretariat has joined forces with Education International, a global federation of teacher unions, to develop nine opportunities, guidelines, and guardrails for the effective and equitable use of AI in education (OECD, 2023[13]). These aim to help educational stakeholders navigate some of the fast-moving developments in AI, and a notable focus is on equity of access and use (ibid.). The United Nations Educational, Scientific and Cultural Organisation (UNESCO) also provides several guidelines and frameworks on AI in education. The Recommendation on the Ethics of Artificial Intelligence marks a consensus among 193 member states concerning the core values, principles and policies that should drive the advancement of AI (UNESCO, 2022[14]). It outlines practical approaches, such as tools, methodologies and initiatives intended to maximise AI's beneficial influence on society while mitigating associated risks (ibid.). Moreover, the Beijing Consensus on Artificial Intelligence and Education codifies the agreements on the ethical use of AI in education (UNESCO, 2019[15]). It is complemented by guidelines for policy makers on leveraging the opportunities and addressing the challenges and risks associated with AI and education (UNESCO, 2021[16]). The guidelines outline the definitions, techniques and technologies of AI, and analyse some emerging trends and implications of AI for teaching and learning. More recently, UNESCO published guidance on generative AI in education and research, marking the first attempt to create a global standard for the use of generative AI (UNESCO, 2023[17]). Additionally, UNESCO is working on AI competency frameworks for students and teachers, as well as on a global survey on the governmental use of AI as a public good for education, including existing AI competency frameworks and continuing professional learning programmes on AI for teachers (UNESCO, 2023[18]). Looking at other international organisations, the United Nations Children's Fund (UNICEF) provides policy guidance on AI for children, presenting recommendations for building AI policies and systems that uphold child rights (UNICEF, 2021[19]). The policy guidance advocates for children's rights within both government and private sector, and seeks to enhance awareness of how AI systems can support and compromise children's rights (ibid.). These guidelines go beyond education and present a more holistic discussion on how AI can impact children's lives. Furthermore, the European Commission launched the Digital Education Action Plan (2021-2027), a policy initiative that, among other things, includes ethical guidelines on the use of AI and data in teaching and learning (European Commission, n.d.[20]). The guidelines are designed to help teachers and educators understand AI tools' potential in education and raise awareness of possible risks (ibid.). Some national examples of guidelines and frameworks for AI in education can also be found. The United States Department of Education published recommendations on the future of teaching and learning in the context of AI. The recommendations also focus on using emerging AI technology for digital equity and inclusion (U.S. Department of Education, Office of Educational Technology, 2023[21]). In England (United Kingdom), the Department for Education published a policy paper on using generative AI in the education sector, including large language models like ChatGPT and Google Bard (Department for Education, 2023[22]). It discusses the potential of these tools to reduce workload and enhance teaching while cautioning about their limitations and the need for professional judgment to ensure content accuracy and appropriateness (ibid.). It also emphasises the importance of data privacy, intellectual property rights and the integration of AI into formal assessments and future skills training (ibid.). In Norway, the Directorate for Education and Training provides regularly updated guidance on integrating AI in schools, emphasising the need for schools to evolve with society and technology (Directorate for Education and Training, 2024[23]). It highlights the rapid development of AI, along with its challenges and opportunities, and stresses the importance of addressing these issues immediately and over the long term (ibid.). The Directorate also outlines specific advice for schools on incorporating AI into education, including updating curricula to prepare students for a future influenced by AI, emphasising critical thinking and ethical considerations, and fostering a culture of experimentation and evaluation in pedagogical practice (ibid.). # Taxonomy to analyse the impact of artificial intelligence on equity and inclusion in education Having defined the concepts and outlined some available guidelines and frameworks, this section explores which conceptualisations and taxonomies are available in the literature. For instance, Pons (2023[24]) differentiated between the impacts inside and outside the classroom. Chen, Chen and Lin (2020[25]) considered the functions of AI in administration (e.g. AI can perform some administrative tasks faster or more cost-effectively and can help teachers in data-driven work), instruction (e.g. analyse course materials, help create learning plans), and learning (e.g. uncover learning shortcomings, apply intelligent adaptive interventions). The primary focus of this working paper is to help policy makers orient themselves in the vast array of tools and their impacts on equity and inclusion in education. To this end, the authors adopted and adjusted the taxonomy by Holmes and Tuomi (2022[2]). The rationale for this taxonomy of AI tools in education – categorising them into learner-centred, teacher-led-and other institutional tools (Table 2.3) – is primarily based on each tool's primary beneficiary and intended application. This taxonomy allows for a clearer understanding of how AI is applied in different facets of the educational ecosystem, addressing distinct challenges and objectives in each sector. Furthermore, this taxonomy comes with a helpful categorisation of the vast amount of AI tools (Holmes, 2023[3]; Holmes and Tuomi, 2022[2]).<sup>2</sup> This provides a solid base to elaborate on the more specific focus of this working paper on equity and inclusion in education. While this categorisation might be helpful in some contexts, overlaps exist among the categories. For instance, learner-centred AI tools indirectly benefit and support teachers, as they can save them time. Furthermore, it could be argued that most, if not all, of the tools discussed in this working paper have been developed to improve student academic and well-being outcomes. Table 2.3. Taxonomy of AI tools in education <table><tr><td></td><td>Purpose</td><td>Examples of AI tools</td></tr><tr><td>Learner-centred tools to support equity and inclusion</td><td>Designed to enhance the learning experience of students.</td><td>Intelligent tutoring systems, AI-enabled simulations, AI-enabled tools to support students with special education needs, etc..</td></tr><tr><td>Teacher-led tools to support equity and inclusion</td><td>Assist teachers in their instructional and administrative roles.</td><td>AI-powered robots, assistants with assessment and classroom management, continuing professional learning coaches, etc..</td></tr><tr><td>Other institutional tools that can foster equity and inclusion</td><td>Aimed at addressing broader institutional objectives such as improving operational efficiency and managing admissions.</td><td>Smart admission systems, tools for identifying at-risk students and assistants with data-based decision making.</td></tr></table> Note: Categories can overlap in regard to purpose and examples. Source: Holmes and Tuomi (2022[2]), State of the art and practice in AI in education, https://doi.org/10.1111/ejed.12533. Learner-centred AI tools (section 3) are designed to enhance students' learning experience. They can provide adaptive learning and offer support in areas where students may struggle. This category includes tools like intelligent tutoring systems, AI-enabled simulations, AI-enabled tools to support students with special education needs and others. These technologies have not necessarily been designed for students and to be used by students (Holmes and Tuomi, 2022[2]). Instead, they were often repurposed for learning (ibid.). Teacher-led AI tools (section 4) assist teachers in their instructional and administrative roles. They are designed to streamline tasks like assessment, curation of learning materials and classroom management, thereby enhancing teaching efficiency and effectiveness. AI-powered robots, tools that enable smart curation of learning materials, assistants with assessment and classroom management, tools that help identify some special education needs, and continuing professional learning coaches fall into this category. Finally, other institutional tools (section 5) aim to address broader institutional objectives, such as improving operational efficiency and managing admissions. They can be used at a higher administrative level and impact the institution as a whole. Examples include smart admission systems, tools for identifying students at risk of early leaving from education and training, and assistants with data-based decision making. # 3 Learner-centred tools to support equity and inclusion Learner-centred AI tools are designed to improve students' educational experiences. They aim to enable tailored learning experiences and furnish assistance in subjects where students might face difficulties. They have the potential for adaptivity, enriching content, assistance in learning, and informing and advising students. However, these tools also come with several challenges. These include access disparities, dangers of techno-ability, various inherent biases, socio-emotional implications, and privacy and accountability concerns. # Opportunities of learner-centred AI tools for equity and inclusion Learner-centred AI tools have the potential to mark a transformative moment in education, opening doors to new opportunities for equity and inclusion. Intelligent tutoring systems exemplify this shift, offering adaptive learning experiences that have the potential to enhance educational outcomes for a diverse student body. Similarly, AI-enabled simulations can enrich content, making learning more engaging and culturally rich, thereby catering to a varied student demographic. For learners with special education needs, AI tools can provide additional support and equalise access to educational content. Furthermore, AI-powered tools, such as chatbots, have the potential to play a role in promoting inclusivity. They can offer rapid, universal access to information and support mental health. As these technologies evolve, they might play an increasingly significant role in fostering inclusive and equitable learning environments. # Adapting learning Adaptivity in learning, sometimes referred to as "personalisation", has been highlighted as one of the most defining features of AI tools (Khosravi et al., 2022[26]). In particular, intelligent tutoring systems (ITS) can significantly advance educational technology, combining AI techniques with pedagogical methods to tailor instructional activities to individual learner profiles. These systems adjust content, pace and difficulty level in real-time, responding to the unique characteristics, needs and performance of each student (Conati et al., 2021[27]; Keles et al., 2009[28]; Mousavinasab et al., 2018[29]). Indeed, adaptive learning is a significant advantage of ITS (de la Higuera and Iyer, 2024[30]). Such adaptability can result in more inclusive education responsive to the varied learning requirements of a diverse student body. For example, Carnegie Learning's adaptive learning platform provides a customised learning experience that aims to adapt in real-time to each student's interactions. Khan Academy's Khanmigo offers AI one-on-one tutoring to students by, e.g. mimicking a writing coach by giving prompts and suggestions to move students forward as they write, debate and collaborate. Furthermore, individuals for whom English is not their first language can benefit from AI tools that rewrite the text into grammatically correct and stylistically appropriate English – provided that they understand, access, navigate, expertly prompt, corroborate, and ethically and effectively incorporate text generated by AI tools (Warschauer et al., 2023[31]). Another key opportunity ITS presents is catering to gifted students (Johns Hopkins Center for Talented Youth, 2023[32]). These systems could provide enriched content along with adapted enrichment activities (Johns Hopkins Center for Talented Youth, 2023[32]; Pons, 2023[24]). Such an approach matches gifted students' academic abilities, and promotes independent exploration and research, thus fostering a conducive learning environment for their skills and talents (Rutigliano and Quarshie, 2021[33]). This tailored approach has the potential to help struggling students catch up academically so they do not remain disadvantaged due to educational setbacks. Indeed, there is some emerging evidence suggesting that ITS help disadvantaged students and ethnic minorities (Huang et al., 2016[34]). This can potentially address the gap in educational equity, as these students often lack access to individualised support that can be pivotal in their academic development (OECD, 2020[35]). However, more research of higher quality is needed, with a recent meta-analysis reporting mixed results (Wang et al., 2023[36]). In particular, a lack of research is visible on the heterogeneous effects of ITS on diverse learners. An indirect effect of ITS can be alleviating some tasks performed by school staff members, enabling them to focus on more complex aspects of teaching and learning. This can enhance the quality of education and contribute to a more sustainable workload for educators. For instance, Carnegie Learning's technology aims to support teachers by providing detailed insights into student performance, enabling them to intervene more effectively and efficiently. While system-level implementation of ITS remains rare, some countries, such as Austria, Korea, Luxembourg and Türkiye, are pioneering these (OECD, 2023[37]). # Enriching content AI-enabled simulations, encompassing game-based learning, chatbots, virtual reality (VR) and augmented reality (AR), can offer interactive and immersive experiences that enhance learning. The integration of AI-enabled simulations, tailored to cultural specificities, has the potential to make curriculum content more tangible and engaging. For instance, in medical sciences, a VR heart anatomy system enhanced students' anatomy learning experience and understanding, compared to traditional physical models (Alfalah et al., 2018[38]). Varjo can help medical students prepare for challenging real-life scenarios. Chatbots, such as ChatGPT, were used in interactive medical simulations, such as forming independent diagnostic and therapeutic impressions over an entire patient encounter (Scherr et al., 2023[39]). In science and history education, AI-enabled simulations can foster the exploration of scientific phenomena, historical events and cultural practices that are difficult or impossible to replicate in a physical classroom (Holmes, 2023[3]). This aspect is particularly valuable for overcoming budgetary, geographical and physical constraints limiting educational experiences. Several private companies offer solutions. Google Virtual Field Trips, among others, aims to enable students to experience various environments: history and natural history, geography, arts, science and technology. Other reviews have shown that AI-enabled simulations can enhance learning and memory, although more research is needed (Papanastasiou et al., 2018[40]; Pellas, Dengel and Christopoulos, 2020[41]). In particular, studies need to be conducted using more robust designs and with control groups placed in appropriate settings (e.g. comparing AI-enabled simulations with older simulation tools such as 2D simulations). Furthermore, AI-enabled simulations can provide a supportive environment for students to develop essential skills such as problem-solving, social interaction and collaboration (Dai and Ke, 2022[42]; Wu et al., 2019[43]). These environments can be conducive to students with particular special education needs. For instance, Brain Power, an AR system empowering people with autism, aims to help to teach these individuals social and cognitive skills. AR solutions can also help students with disabilities to play and exercise with their peers. iGYM, for instance, is an AR designed for school and community-based sport or recreation facilities seeking to provide novel and accessible ways for people with motor disabilities and their non-disabled peers to play and exercise together (Graf et al., 2019[44]). AI-enabled simulations can also play a role in enhancing cultural diversity and individualising learning contexts. For instance, these technologies can promote the appreciation of Indigenous and minority cultures (Reihana et al., 2023[45]). Culturally contextualised digital technologies can enable more meaningful learning experiences for students from diverse backgrounds. Indeed, Google Arts & Culture can provide educators and students with extensive cultural content, including collections on Black and Indigenous history and culture in the United States. This platform utilises interactive camera features, making learning about cultural artefacts engaging and dynamic. # Assisting learners with special education needs AI-enabled tools designed to support learners with special education needs (SEN) are technologies that assist in overcoming a range of visual, auditory, physical and cognitive impairments (Holmes, 2023[3]). A growing body of literature emphasises the role of AI in facilitating special needs education (Gottschalk and Weise, 2023[11]; OECD, 2021[46]; Vincent-Lancrin and van der Vlies, 2020[47]). These AI tools aim to adapt to individual needs and abilities, offering learning experiences potentially tailored to each student's unique skills and requirements (Hopcan et al., 2022[48]). They can make learning experiences more accessible and enhance the educational process for students with various disabilities, impairments and difficulties. By employing AI-enabled tools, educators can significantly improve the accessibility of educational content and experiences for these students (Holmes, 2023[3]). One of the potential benefits of these AI tools is the facilitation of including students with SEN in standard classroom settings. Integrating AI tools into the classroom can allow students with SEN to participate alongside their peers to a greater extent, contributing to a more diverse and inclusive learning community. These tools have the potential to assist the students in accessing the curriculum, and also enrich the educational experience for all students by fostering an environment of diversity and mutual understanding. For example, students with visual and auditory impairments can benefit from AI tools that provide customised support. A notable advancement in this area is the development of AI assistive devices for learners with hearing impairments. Microsoft Translator, for instance, has created a device equipped with a headset that translates speech signals into written captions in real-time. This device employs deep learning and AI technologies, including VR and AR, to deliver a customised hearing experience featuring sound scene analysis, sound protection, real-time language translation, etc. (Roach, 2018[49]). Moreover, the tool supports translation to over 60 languages, making it beneficial to many students who do not speak the language of instruction with or without SEN (ibid.). Similarly, Deaf AI is developing digital sign language interpreters for real-time interpreting of voice to sign languages. Other tools utilise AI to foster social communication skills in children with autism spectrum disorders (OECD, 2021[46]). ECHOES, for instance, is a technology-enhanced learning environment where young learners can explore and practise skills needed for successful social interaction, such as sharing attention with others, turn-taking, initiating and responding to bids for interaction (Bernardini, Porayska-Pomsta and Smith, 2014[50]). By integrating playful activities within a virtual "magic garden" and interaction with a virtual character named Andy, ECHOES operates on the SCERTS model principles of Social Communication, Emotional Regulation, and Transactional Support (ibid.). This approach demonstrates the potential of AI tools in enhancing educational experiences for students with SEN by providing environments that stimulate their unique learning requirements (Porayska-Pomsta et al., 2018[51]). Evaluation of the ECHOES environment highlighted a nuanced increase in social initiations from children, both towards human partners and the AI agent, underscoring the effectiveness of AI in engaging students with some SEN in meaningful educational interactions (ibid.). # Informing, advising and supporting students AI-powered chatbots are tools designed to simulate interactive conversations with human users by adapting to new information and user interactions (Holmes, 2023[3]). Chatbots in education can provide quick and universal access to information (Okonkwo and Ade-Ibijola, 2021[52]). Students may sometimes prefer to use chatbots for information retrieval over traditional counselling methods. For instance, many students would like not to have sexual education content delivered by familiar teachers as it could "blur boundaries and introduce awkwardness into the teacher-pupil relationship" (Pound, 2017, p. 1[53]). To this end, the Roo chatbot aims to provide users with answers related to sexual education. Such chatbots can offer immediate access to information and can keep students engaged and motivated (ibid.). The implications this could have for education are yet to be determined. On the one hand, chatbots could make sexual education more comprehensive and less "awkward". On the other hand, the potential lack of alignment with official curricula could be viewed as problematic. From an equity standpoint, chatbots can present a budget-friendly solution for equitable distribution of information and assistance. They can give all students instant access to essential details like class timings, venue information, submission deadlines and educational materials. EduBot by INNODATATICS, for instance, can offer help-desk support in, e.g. courses and curriculum at a school or higher education institution. In addition to providing real-time responses, it features speech recognition and emotion analysis that reads the user's emotions and aims to respond appropriately. CareerChat, a chatbot powered by AI, aims to provide career support services to students, and save time, energy and resources for career development professionals, thus enabling them to help students more effectively (Hughes, 2023[54]). AI-enabled tools, including chatbots, are also being used to detect and support student health issues. These tools can analyse various data sources, such as behaviour patterns, sleep quality, heart rate and academic performance, to identify signs of mental health struggles or well-being issues (Holmes, 2023[3]). Implementing such tools can be particularly beneficial in disadvantaged areas, where resources fostering well-being might not be easily accessible. Indeed, chatbots can offer 24/7 non-judgmental listening, providing information about available resources, coping strategies and guidance to appropriate professional help where needed (ibid.). This round-the-clock availability ensures that students have constant access to support, which is particularly important in times of crisis or when immediate help is required. Confidentiality and anonymity are often cited as advantages of chatbots, particularly for those seeking support and information without the fear of stigmatisation (Abd-alrazaq et al., 2019[55]). This aspect is crucial in creating an inclusive and supportive educational environment where all students feel comfortable seeking help. For instance, the ADMIN project by the Institute of Educational Technology is creating a chatbot assistant that can enable more effective access to support by providing an alternative to filling in forms. By supporting dialogue, the assistant aims to guide the student to provide information that helps the educational institution understand their needs, allow them to ask questions and understand more about the available support. While chatbots show potential in this area, more research is needed to confirm their clinically significant effects and safety (ibid.). # Challenges of learner-centred AI tools for equity and inclusion Integrating AI in education confronts significant challenges in ensuring equity and inclusion. Issues of access and the digital divide spotlight the need to bridge technological gaps and address socio-technical factors contributing to the AI divide. Concurrently, techno-ability might necessitate the involvement of disabled individuals in AI development to create inclusive and empathetic educational tools. Compounding these challenges are inherent biases in AI, reflecting societal prejudices, and requiring a diverse and critically aware approach to AI implementation. Equally critical are the socio-emotional implications of AI in education, including the potential reduction in human interaction and its impact on social skills and mental health. Finally, integrating AI in educational settings raises essential data privacy and security concerns, emphasising the need for informed consent, transparent AI systems and robust privacy protection regulations. This section examines these complex issues, underscoring the importance of navigating these challenges to realise the potential of learner-centred AI tools in creating equitable and inclusive educational environments. # Accessing AI tools The increasing integration of AI-powered education technologies can present challenges for equity and inclusion in education, mainly due to varying degrees of access to the technology. This so-called "digital divide" can present challenges in terms of technical components, socio-technical and social factors (Carter, Liu and Cantrell, 2020[56]). Technical factors, such as technology availability, broadband speed and computational data, are crucial for the effective use of AI tools. These technologies often require internet access for optimal functionality, as the internet enables AI systems to access databases, online resources and real-time data essential for up-to-date information. For instance, cloud-based AI services and interactive tools depend heavily on internet connectivity to offer adaptive, real-time and enriched educational experiences (ibid.). However, schools with high shares of socio-economically disadvantaged students already report a more significant lack of and inadequate quality of digital resources (Figure 3.1). On average across OECD, almost $30\%$ of students in disadvantaged schools had principals who reported a lack of digital resources, or an inadequate or poor-quality thereof in 2022. In contrast, less than $20\%$ of students in advantaged schools had principals who reported similar concerns. # Figure 3.1. Quantity and quality of digital resources by socio-economic profile of schools (2022) Percentage of students in schools whose principal reported a lack of digital resources (panel A), or inadequate or poor-quality digital resources (panel B), to some extent or a lot Panel A: Lack of digital resources (e.g. desktop or laptop computers, Internet access, learning-management systems or school learning platforms) Advantaged schools Disadvantaged schools Advantaged schools Disadvantaged schools Panel B: Inadequate or poor-quality digital resources (e.g. desktop or laptop computers, Internet access, learning-management systems or school learning platforms) Note: * Caution is required when interpreting estimates because one or more PISA sampling standards were not met (see Reader's Guide, Annexes A2 and A4 in OECD (2023[57])) The PISA Index of Economic, Social and Cultural Status (ESCS) measures the schools' socio-economic profile. A socio-economically disadvantaged (advantaged) school is in the bottom (top) quarter of the ESCS index in the country. Sorted in descending order of the percentage