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

Introduction

Much of the recent focus on generative artificial intelligence (GenAI) in education has considered it as a tool used individually either by a student or an instructor, through a commercial off-the-shelf chatbot designed to be an assistant. This role has considerable value (see Gašević and Yan, 2026; Li and Hu, 2026), but GenAI can be used in several other fashions, as shown in this report. This chapter considers the role of GenAI in supporting instructors and Teaching Assistants (TAs). TAs serve as a foundational support structure within colleges and universities, bridging the gap between students and academic staffand helping to sustain the quality of instruction in a wide range of disciplines. By leading discussion sections, grading assignments, answering questions, and offering individualised guidance, TAs play a vital role in shaping how students experience their courses. This dual role as both an intermediary and a mentor underscores the significance of the work that TAs do, not just in managing course logistics but in advancing student learning, engagement, and success. As higher education evolves – facing increasing enrolment pressures, budget constraints, and shifts toward online or hybrid delivery – the role of TAs is likely to expand. At the same time, TAs are themselves students, balancing these teaching responsibilities with their own scholarly activities, which can lead to time conflicts, uneven support for learners, and burnout. These challenges raise important questions about how universities can sustainably leverage the benefits TAs provide while addressing the real human limitations of time, expertise, and scalability.

Recent advances in GenAI offer a promising avenue to complement TAs’ efforts, while retaining the human element that underpins great teaching. GenAI-driven “teaching assistants,” powered by large language models (LLMs) and other advanced technologies, have the potential to handle repetitive administrative tasks, deliver targeted learning support, and provide immediate feedback to students in a way that human TAs alone simply cannot. By examining the evolving role of TAs and exploring how AI can enrich and extend their capabilities, this chapter seeks to highlight both current practices and new horizons for more equitable, accessible, and impactful teaching support. We conclude with a discussion of implications, including for policy.

Teaching assistants

The teaching assistant role

A Teaching Assistant (TA) in higher education (in countries where this role exists) is typically a graduate (Master’s and PhD) or advanced undergraduate (Bachelor’s) student who supports the primary instructor in delivering course content and assisting students. A TA’s responsibilities may vary by institution and discipline, but they generally include facilitating small-group discussions, answering student questions, and supplementing the main instructor’s efforts to create an effective learning environment (Park, 2004).In recent years, as more courses have tended to partly or fully move online, TAs also play a large role in managing discussion forums, answering student questions and supporting discussions there (Wadams and Schick-Makaroff, 2022). By handling a portion of the teaching and administrative workload, TAs play a critical role in making large or complex courses more manageable, thus enhancing the overall educational experience for both academic staffand students.

The origins of Teaching Assistants can be traced back to the late nineteenth century, when growing student enrolments and expanding research expectations prompted universities to look for ways to extend instructional capacity (Chism, 1998). In these early stages, TAs often served as informal helpers to more senior academic staff, assisting with tasks like grading or lab supervision as part of their own apprenticeship in academia. Over time, and particularly after World War II when higher education systems expanded rapidly, the role of TAs became increasingly formalised. Universities began creating structured programmes that provided clearer job responsibilities, training, and professional development opportunities, reflecting the recognition that TAs could significantly enhance both teaching and learning. This evolution laid the foundation for the modern TA role, in which graduate and advanced undergraduate students are systematically integrated into the educational process (Park, 2004).

Teaching Assistants carry out a range of tasks that collectively support both the instructor and students in higher education settings. Their responsibilities can be broadly categorised into instructional support and student engagement, though these two areas naturally overlap (Park, 2004). By taking on these roles, TAs help to foster an environment that promotes understanding, participation, and continuous feedback – key components of effective learning (Hattie and Timperley, 2007; Chi and Wylie, 2014).

In terms of instructional support, TAs frequently lead discussion sections, tutorials, or laboratory sessions, serving as facilitators who bridge theory and practice (Park, 2004). In these smaller and often more interactive settings, TAs clarify course material, demonstrate practical techniques, and encourage student participation. By adapting teaching methods to the needs of specific groups of students, TAs help maintain a dynamic and inclusive classroom atmosphere. Another critical element of a TA’s role involves assessing student work. TAs often grade assignments, quizzes, and exams under the supervision of the lead instructor (Marshman et al., 2018). This process typically includes reviewing submissions, providing constructive feedback, and highlighting areas for improvement – ideally, guiding students to developing and demonstrating deeper understanding (Marshman et al., 2018).Grading student work not only eases faculty workload but can also offer TAs valuable, instructor-scaffolded experience in evaluating academic performance, helping TAs to develop deeper understanding of student thinking (Karim, Maries and Singh, 2018).

As for student engagement in their learning process, in many modern courses – particularly those with hybrid or fully online components – TAs serve as key points of contact on discussion forums (Wadams and Schick-Makaroff, 2022). By responding to questions, facilitating conversation, and sharing clarifications from the instructor, they help maintain an active and supportive online learning community. This work often extends to moderating peer-to-peer exchanges, ensuring that discussions remain on topic and respectful. In addition, TAs often hold regular office hours and meet with students to allow students to seek in-depth explanations, review feedback, or discuss academic challenges (Park, 2004). These mechanisms often provide learning support beyond what instructors can offer just-in-time and on- demand, particularly for research-active senior academic staffor large courses.

Teaching Assistants provide a range of benefits to higher education institutions, more senior academic staff, and students. Universities often find that using TAs is a cost-effective means of managing large course enrolments while still providing individualised support to students, a topic of constant interest to administrators when university budgets are under pressure. For more senior academic staff, TAs offer substantial advantages by relieving some of the workload associated with teaching, grading, and administrative duties. By delegating tasks such as discussion facilitation, assignment feedback, and routine course management, faculty members can devote more time to developing innovative curricula, advancing their research agendas, and mentoring students (including the TAs) at higher levels. In addition, TAs often introduce diverse perspectives or novel approaches to instruction, encouraging a collaborative environment in which both senior academic staffand TAs refine teaching strategies (Begley et al., 2019). Finally, students also benefit significantly from the involvement of TAs. In many cases, TAs are more available to answer questions outside of regular class times, and their support on online discussion forums can be accessed asynchronously, providing a flexible option for students who need extra help. TAs’ relative proximity to the student experience - whether by age, academic journey, or shared disciplines - can also result in a peer-mentorship-like atmosphere during office hours and informal interactions. As such, TAs’ greater relatability and students’ perception that they are more understanding (Kendall and Schussler, 2012) can ease anxieties and foster a sense of community, ultimately enhancing the overall learning experience.

However, several challenges have been noted for current practices involving Teaching Assistants. Regarding instructional support, many TAs may for example lack pedagogical training or skill (Nicklow, Marikunte and Chevalier, 2007). This lack of formal preparation can undermine the quality of their instruction, as they may be uncertain about how to present information clearly. Moreover, TAs sometimes adopt a surface-level approach to grading, focusing on relatively simplistic aspects of correctness rather than attempting to provide feedback that guides students toward deeper conceptual understanding (Marshman et al., 2018). Compounding these issues is the fact that TAs typically possess less subject-specific expertise than full academic staff (for example, see Karim et al., 2018), which can limit their ability to answer complex questions or provide advanced guidance.

Furthermore, TAs often face challenges around workload and time constraints. Many TAs must balance teaching responsibilities with personal academic obligations, such as coursework, research projects, and preparation for required examinations (Park, 2004). Some TAs may find it difficult to invest the necessary time in class preparation, grading, or providing substantive feedback to students. This overload can also lead to high stress and exhaustion, reducing their effectiveness as a TA while also impairing their other work and personal success (Berta and Pembridge, 2019).This is exacerbated by the uneven training and faculty support provided to TAs. Some instructors involve TAs extensively in designing lesson plans, assessment rubrics, or instructional materials, while others may provide only minimal training and mentorship (Begley et al., 2019). Furthermore, many TAs do not have access to teaching mentors other than the instructor. This lack of support can leave TAs uncertain about expectations or best practices, making it harder for them to support students without spending large amounts of their time.

Hence, while TAs fulfil crucial roles in supporting learning and engagement, and help bridge the gap between senior academic staffand students, there are several aspects in which current practices are not optimal for either TAs or students.

Review of AI teaching assistants (AI TAs) and their use

Could generative AI (GenAI) technologies improve things for both TAs and students? The emergence of GenAI models has created a great deal of enthusiasm for the potential of a wide range of educational benefits. One area of rising interest has been the creation of AI Teaching Assistants–tools that extend the capabilities of human TAs. AI Teaching Assistants (AI TAs) use computational methods – in many recent cases GenAI (Sajja et al., 2024; Yetı̇şensoy and Karaduman, 2024; Liu et al., forthcoming) but, before that, machine learning and previous-generation natural language processing (NLP) (Khosravi et al., 2021; Shermis and Burstein, 2003) – to perform tasks that were typically in the purview of human TAs, although in many cases beyond what was feasible for human TAs. Their scope has included streamlining routine administrative tasks, providing targeted learning support or rapid formative assessment, and empowering human TAs and instructors with information and insights about their students, enhancing the overall effectiveness of instructional delivery. While they sometimes take over what used to be human tasks, these systems are not intended to replace human educators, but instead to provide support 24/7 and free up valuable time for TAs and instructors to focus on more complex, critical, and high-impact aspects of teaching.

Over the past few decades, the use of AI in educational technology has evolved from simple automation tools – like basic quiz generators and grading scripts (see discussion in Brooks, 2023) – to advanced AI systems that can process language and consider context, allowing them to respond to students’ questions and needs in real time. The previous generation of intelligent tutoring systems and question answering systems could offer sophisticated support (Nye, Graesser and Hu, 2014; Goel and Polepeddi, 2018), but was highly expensive to author (Aleven et al., 2006), often necessitating intense focus on only a single aspect of adaptivity (Baker, 2016). The contemporary use of GenAI, sometimes combined with previous-generation machine learning, creates the potential for a qualitative leap forward in functionality and sophistication, at much lower development cost.

These technologies, when used to complement instructors, can take on repetitive tasks – such as answering common questions and some parts of the assessment of student work – thereby providing immediate, round-the-clock support to learners and eliminating the bottleneck that often occurs when TAs or instructors are not available – for instance, for an online learner working from a different time zone than the instructors.

At the same time, human TAs and instructors can allocate their expertise to higher-order pedagogical activities, such as facilitating in-depth discussions, offering mentorship, and providing customised feedback for unusual cases and learning challenges. This synergy ultimately helps institutions maintain quality education at scale, addresses the labour-intensive aspects of teaching, and ultimately supports instructors in finding time for high-value personal interaction with learners. In the following section, we will discuss some of the ways that AI TAs can support learners, human TAs, and instructors.

Administrative and logistical support is an area of responsibility for current human TAs that is easy and fairly non- controversial to replace. By automating more administrative processes such as course enrolment or the monitoring of completion of assignments, instructors can devote more time to pedagogical planning and personalised student engagement (Ahmad et al., 2022). AI TAs can also manage course communications by sending out timely reminders for assignments, examinations, and events, ensuring students remain informed and minimising the risk of missed deadlines (Pardo et al., 2018). Streamlining these tasks can allow human instructors and TAs to focus on higher-level teaching responsibilities, such as curriculum development and individualised feedback. Beyond routine administrative tasks, AI systems can support course logistics by monitoring student participation and promptly alerting instructors to potential engagement and performance issues (Wise and Jung, 2019; Khosravi et al., 2021), as well as distil insights from discussions on course forums for instructors.

Another potential area of application for AI TAs is instructional support. AI Teaching Assistants can provide supplementary explanations or resources tailored to students’ individual needs. For example, if a student expresses confusion about a specific topic, these systems can supply targeted materials, such as a textual explanation, brief video tutorials, interactive modules, or suggested readings (Sajja et al., 2024; Essel et al., 2022; Yetı̇şensoy and Karaduman, 2024). Such a system can provide more attention to customising learning for a given student than would be feasible for even the most dedicated human Teaching Assistant. By engaging in follow-up discussion, an AI TA based on a chatbot can support a student in ways that would be infeasible with a static resource. In addition, just as current GenAI can recommend resources to a learner, it can also assist educators with content curation and lesson planning, suggesting how to communicate topics more effectively (Karpouzis et al., 2024).

Furthermore, AI TAs can support instructors in evaluating students’ progress, particularly when it comes to formative assessment. Summative assessment through AI has been used in some applications but still needs to meet a higher bar for reliability and fairness (Bulut and Beiting-Parrish, 2024). Formative assessments to inform instructors or support learners can be used safely due to the lower stakes. There is a long history of using automated assessments; decades of work assessed learners with automated quizzes and multiple-choice items (Brooks, 2023) and a previous generation of NLP afforded short-answer grading (Gao et al., 2024) and automated essay scoring (Shermis and Burstein, 2003). Previous work also enabled the generation of new items, through procedural templates for instance (Kurdi et al., 2019). However, the advent of generative AI has made it possible both to generate new items in sophisticated, tailored ways (Yadav and Tseng, 2023; IIITD et al., 2024; Sajja et al., 2024) and to offer much more detailed, rich feedback on complex artifacts created by students (Chiang et al., 2024; IIITD et al., 2024; Stahl, Biermann and Wachsmuth, 2024; Teng et al., 2024). Automated announcement tools can then deliver personalised updates to students, supporting both performance and self-regulation (Lim et al., 2021). By making it feasible to offer detailed feedback in a much timelier fashion, students are likely to revise their understanding and adapt in ways that align with the course goals (Graham, 2023).

AI Teaching Assistants embedded in course platforms can effectively serve as a first point of contact for students, promptly addressing frequently asked questions about the syllabus, assignment deadlines, and other logistical concerns (Liu et al., forthcoming). By referencing a structured knowledge base, these systems can also respond to content-related inquiries, offering supplementary explanations or clarifications (Liu and M’Hiri, 2024; Maiti and Goel, 2024; Miroyan et al., 2025; Teng et al., 2024; Liu et al., forthcoming). Questions can be asked in an external platform (Alsafari et al., 2024; Teng et al., 2024), within the course discussion forum (Liu et al., forthcoming; Miroyan et al., 2025; Haaben, 2024 (Unpublished Manuscript)), a learning management system (LMS) (Maiti and Goel, 2024), or in the context of a learning activity itself (Lee et al., 2023). These tools can save considerable time for instructors and human TAs (Sinha, 2024; Miroyan et al., 2025).

More importantly, the immediate, round-the-clock availability of these tools supports learners who may need help outside of conventional office hours; for example, one study of an AI TA embedded into a discussion forum found that students received responses significantly more often on weekends with the AI TA than during the previous (only human TA support) semester (Liu et al., forthcoming). While such a system cannot respond to all student queries, it can provide support in many cases. In other cases where a query requires more nuanced interpretation or context–such as complex conceptual misunderstandings or unique personal circumstances–AI TAs can escalate the matter to human TAs, thus ensuring students receive appropriate and thorough support. This triaging function can help manage the flow of incoming questions, reducing the volume of simpler queries that human TAs and instructors must handle. As a result, educators are freed to spend more time providing personalised feedback, guiding higher- level discussions, and engaging students in meaningful academic interactions. While this type of question answering functionality was available even before the advent of LLMs, it required considerable engineering (e.g. Goel and Polepeddi, 2018) compared to the relative ease of deployment now possible.

One of the key steps to moving these types of advancements from one-offresearch projects to scalable solutions benefitting a large number of learners is ensuring they integrate seamlessly with existing educational infrastructures. Many AI-based tools of this type so far require learners and instructors to use separate platforms rather than being integrated directly into their primary learning management systems (LMS) or discussion forums. This lack of integration or interoperability can create a fragmented user experience, requiring additional sign-ins, duplicating data entry, and making it harder to track student progress across multiple systems. In contrast, compatibility with widely used LMSs (e.g. Canvas, Moodle, Blackboard) and discussion forum platforms (Piazza, Discourse, phpBB, vBulletin, Flarum) would allow AI TAs to seamlessly access course materials, participation records, and student performance data. Such interoperability not only streamlines the user experience but also supports richer analytics and more effective, personalised interventions, ultimately strengthening the teaching and learning process.

Another key step for making these systems usable at scale will be efforts to engineer the human-computer interactions of these systems to facilitate their use by busy human TAs and instructors. Currently, the process of integrating course resources varies in complexity between tools, and the degree of uptake can vary considerably between instructors (Maiti and Goel, 2024). There are several ways to accomplish this, including shared folders, access to learning management systems as a simulated student, or tools for uploading resources – but whichever approach is chosen, it must be low-effort for human beings. It should also be easy to continually update these resources, as changes to course materials and syllabi will often occur within a semester and across semesters for courses that are offered on a regular basis.

In addition, onboarding and even training is needed for the human TAs and instructors who will collaborate with an AI TA. They will need to understand enough about how the system works, what it can do, and what its limitations are, to ensure that they implement it effectively in their courses. By clearly communicating which queries or tasks the AI TA should handle versus those that call for human expertise, institutions can maintain quality control while maximising efficiency. Over time, incorporating continuous improvement and feedback loops can further refine these boundaries. For instance, platforms should support human TAs and instructors in regularly reviewing the AI TA’s responses to student inquiries. Student and instructor feedback collected through short surveys or mining forum discussions can also highlight areas where the AI TA might be underperforming or producing confusing or inaccurate information. Supporting instructors in checking and refining the system will help to ensure that content remains accurate, relevant, and aligned with educational objectives, reduce instructor frustration, and increase the likelihood of long-term sustained use.