While addressing teacher shortages is a crucial challenge (see Chapter 2), it is equally essential to ensure that current teachers are supported to deliver high-quality teaching and adapt effectively within rapidly changing technological landscapes.

Insights from the EPO Survey 2024 highlight policy efforts at the teacher, school, and system levels. These efforts span traditional support measures – such as managing teacher workloads and assessing development needs – to more forward-looking strategies, including enhancing pedagogical practices with AI and fostering cross-sector collaboration with industry and research to integrate AI in classrooms effectively.

In the EPO Survey 2024, education systems outlined policy priorities for strengthening teaching practices and professional learning from 2025 to 2030 within a rapidly evolving technological landscape. Survey responses reflect a widespread focus among education ministries on fostering a collaborative teaching profession, particularly at early educational stages. However, there is a noticeably greater emphasis on integrating digital technologies to support student learning than on leveraging these tools to enhance teachers’ own professional development (Figure 3.1).

This disparity represents a key missed opportunity, as AI technologies offer considerable potential for advancing professional learning. Applications range from more authentic-feeling simulations (Markel et al., 2023[2]) to virtual coaching and mentoring that provide tailored feedback (Neumann et al., 2021[3]), and enhanced data collection and analysis to gain better insights into teacher development (Salas-Pilco, Xiao and Hu, 2022[4]).

A notably low proportion of education systems also reported active initiatives to enhance teachers’ and leaders’ use of research, data, and evidence to drive pedagogical and institutional change. This is surprising given the recent policy focus on evidence-informed teaching practices (OECD, 2022[5]) and the growing recognition that while understanding “what works” in education has increased, translating this knowledge into effective practice remains a challenge (Gorard, See and Siddiqui, 2020[6]). Policymakers and teachers often underutilise the knowledge at their disposal, not from a lack of interest but due to gaps in structures, mechanisms, and a culture that supports evidence-based practice (Gorard, See and Siddiqui, 2020[6]). For evidence-informed innovation in teaching practices to flourish, policy efforts must address these foundational challenges.

The 2022 OECD Ministerial Declaration on Building Equitable Societies Through Education represents a collective commitment by OECD education ministers to reimagine education. This pledge includes ambitious goals: “to create and realise a bolder vision on leveraging spaces, time, technology, and human resources for more effective and inclusive learning” and to “acknowledge and address the evolving roles of education professionals, developing policies that empower them” (OECD, 2022[7]). Despite pressing challenges such as teacher shortages and workforce demands, OECD education systems are urged to maintain momentum on these essential commitments.

Findings from the EPO Survey 2024 indicate that governments place less emphasis on AI-enhanced professional learning for teachers than on AI applications for student learning – a pattern that aligns with broader trends. A review of policy guidance documents shows that while AI integration to support student learning is well-represented, few documents consider AI’s role in advancing teachers’ professional development (see Annex B). This suggests a pressing need for strategic planning on digital technology adoption that equally enhances outcomes for both students and teachers. Furthermore, in EdTech too, investment in technology that supports teachers, including their professional development, makes up only a fraction of the total market. For example, less than 5% of Europe’s 200 most promising EdTech startups in 2024 focused on teacher support (HolonIQ, 2024[8]). Similarly, in academia: a recent systematic review of research into AI adoption in higher education revealed that only 17% of the 138 studies considered had teaching staff as the intended users of AI for learning (Crompton and Burke, 2023[9]).

The integration of digital technologies in education stands at a critical juncture. Transactional approaches have often led to high resource investment with limited educational impact in student outcomes (see Chapter 1). Going forward, it is essential that decisions regarding digital technology adoption in classrooms balance educational value with broader cost-benefit considerations. Education systems must strategically embrace the complementarity between evidence-informed methods and technologically enhanced practices to drive meaningful transformation in teaching.

Achieving this balance, however, is challenging. Rapid global changes continually introduce new complexities into teaching and learning processes. Digital technologies exemplify this issue: swift innovation and gaps between research, industry, and classroom practice mean that educational policies must often operate under conditions of uncertain cost-effectiveness regarding new methods' educational value. By bringing teachers closer to evidence generation and empowering them as experimenters and digital innovators in their classrooms, education systems can more effectively support the translation of knowledge into improved practice. Policy plays a crucial role in fostering teaching quality that adapts to rapid technological advances, especially developments in AI. In 2023, the OECD and Education International issued guidelines on the effective and equitable use of AI in education, underscoring the importance of granting teachers the agency needed to critically engage with digital technologies (OECD, 2023[10]). Building on this foundation, the following analysis explores policy responses that aim to empower teachers to adopt an evaluative mindset, enabling them to experiment responsibly with AI to provide high-quality, evidence-informed, and technologically enhanced teaching.

How can policy strategically support the continuous enhancement of teaching quality in the years ahead? In a rapidly evolving technological landscape, education systems must equip teachers to better navigate the opportunities, challenges, and uncertainties of this context. This calls for policies that empower teachers to take a leading role in applying evidence-informed practices, particularly in the use of AI and other digital technologies. Figure 3.2 provides an overview of key areas of action for policymakers focused on nurturing teaching quality. Drawing on responses from the EPO Survey 2024 and additional desk-based research, it also summarises specific policy responses aimed at promoting evidence-informed and technologically enhanced teaching practices in today’s classrooms. Each of these policy responses is explored further in the following sections of this chapter.

In education systems that prioritise agency and professionalism, enhancing teaching quality begins with empowering teachers themselves. This entails giving them the time, space, tools, and mindset required to continuously enhance their everyday practice. Key areas of action include rethinking the structure of teachers’ workloads, integrating digital technologies to expand the pedagogical toolkit, and supporting evidence-informed practices to enrich teaching.

Teacher-level actions alone are insufficient to foster high-quality practices that respond effectively to rapid technological changes in every classroom. High-quality teaching is shaped by the broader school and system environments in which they work; policymakers must therefore consider complementary actions at these levels to create an integrated support structure for teaching quality.

At school level, teachers’ professional judgement is informed and strengthened by strong relationships with colleagues and other partners. Schools therefore have a critical role to play in enhancing teaching quality through nurturing a collaborative school climate and fostering partnerships beyond the school walls with other actors with relevant expertise. Beyond these areas, other key school-level actions that policymakers can consider when addressing teaching quality include supporting school leadership (see Pont, Nusche and Moorman (2008[12]) and enhancing physical and digital infrastructure or resources (see OECD (2018[13]; 2023[14]).

System-level policy actions are important for aligning the efforts of individual teachers and school teams into a cohesive approach. These actions can help education systems to drive progress at scale and towards clear strategic goals. Relevant areas of action in times of rapid change include ongoing professional development and formative teacher appraisal. In both cases, school-embedded approaches that bring system structures for teaching quality as close as possible to the everyday work of teachers are particularly impactful. School and system-level actions are thus mutually reinforcing. Other policy areas that support teaching quality at system level are professional standards and identity (see Guerriero (2017[15])) and initial teacher education (see OECD (2019[16])).

This section outlines policy efforts across three levels – teacher, school, and system – each of which plays a vital role in supporting teaching quality. By addressing these interconnected levels, policymakers can build a robust framework that supports teachers’ ability to adapt, innovate, and excel in an evolving educational landscape.

Prescribing changes in teaching practice may help teachers do things better, but it rarely gets them doing better things. Such approaches also tend to overlook practices that they should stop doing. To foster a professional culture in which teachers proactively adapt practice in evolving educational contexts, policy must provide teachers with the time, space, and resources needed to experiment in real-world setting, taking full advantage of the tools and knowledge available. It also needs to support them to develop an evaluative mindset, through which teachers engage in deeper and sometimes more critical enquiry processes, are prepared to question the status quo, and view failure as a necessary part of the learning process (Golden, 2020[17]). The three sections that follow offer related policy responses.

Teachers across OECD countries and economies face significant workload pressures (OECD, 2020[18]), compounded by the need to make rapid, high-stakes decisions in dynamic educational settings (Creagh et al., 2023[19]). Such demands are linked to elevated stress levels, affecting teachers’ work-life balance and increasing their risk of burn out (Bakker et al., 2007[20]; Collie, Shapka and Perry, 2012[21]; Klassen and Chiu, 2010[22]). Over-burdened or highly stressed teachers are more likely to resort to habit dependency in the classroom and routine procedures at school level that push out innovation (Wotherspoon, 2008[23]). In addition, workload erodes job satisfaction, elevating the risk of attrition and reducing the attractiveness of the profession (OECD, 2020[18]).

Research consistently shows that increasing teacher workloads are largely driven by an expansion of “non-core” tasks within teachers’ work (Creagh et al., 2023[19]). Indeed, in many countries and economies, statutory teaching hours amount to less than half of total working time: in 2023, on average across the 23 OECD education systems with available data, 58% of upper secondary teachers’ working time was spent on non-teaching activities. Many of these activities – such as professional development and teamwork with colleagues – can enhance the quality of practice. Other tasks, such as communication with parents, acting as a class or form teacher and engaging in extra-curricular activities, help build stronger relationships with students, a key factor in quality education outcomes (OECD, 2024[24]).

Effectively managing teachers’ workloads requires a balanced approach that prioritises time for quality teaching while safeguarding teachers’ mental health and work-life balance. Some might think that one option is to reduce teaching hours. OECD data indicates that teaching hours declined between 2013 and 2023 in 18 out of 34 education systems with available data (OECD, 2024[25]; 2020[26]). However, reducing teaching hours can be difficult to implement in workforce shortage contexts and may be an overly simplistic solution to managing teaching workloads. Insights from the OECD's Teaching and Learning International Survey (TALIS 2018) indicate that time spent on teaching tasks is a less strong contributor to teacher stress compared to other factors. In contrast, administrative tasks and non-core responsibilities, such as communication with parents and paperwork, have a significantly larger impact. Therefore, targeted reductions in non-teaching tasks, rather than teaching hours, could be more efficient in reducing stress and improving teaching quality (OECD, 2020[18]) (Figure 3.3).

AI and digital technologies hold significant potential for alleviating non-teaching tasks, such as marking, administration, and communication with parents. By leveraging these technologies, education systems can reduce the administrative burden on teachers, as highlighted in Figure 3.3. Research shows that Large Language Models (LLM) can improve marking efficiency, reduce associated costs, and decrease grading bias (Li et al., 2024[27]). International data indicates that AI technologies could help teachers reallocate half of the time currently spent on administrative tasks like form filling, emailing, and resource planning (Bryant et al., 2020[28]). Additionally, AI can promote cooperative and student-centred learning, which has been linked to increased job satisfaction and self-efficacy (Anton and Van Ryzin, 2024[29]).

Policy efforts focusing specifically on leveraging AI technologies to reduce workload are nascent. Some early measures across OECD countries and economies and beyond include:

• In 2023, Singapore launched two Learning Feedback Assistants. The Language Feedback Assistant for English provides basic feedback on students’ spelling and grammar. Teachers are then encouraged to build on this by engaging in the more complex aspects of written language, such as creative expression and tone. The Short Answer Feedback Assistant provides suggested grades and auto-generated content-related feedback for close-ended, short answer questions for all subjects and grade levels. This allows teachers to more quickly check students’ knowledge and understanding, generating a first draft of grades and comments that they can then edit and build on (Ministry of Education, 2023[30]).

• England (United Kingdom) recently announced funding for a project to create a data pool of previously published government documents including curriculum guidance, lesson plans and anonymised pupil assessments. This will be made available to selected AI companies, specialising in developing tools that help teachers mark work, create teaching materials, and assist with routine school admin. These companies will be able to use the data to train their tools (Department for Education, 2024[31])

• Of 28 high-level policy guidance documents related to the use of AI in education analysed for this report (see Annex B), 17 reference the potential of AI to reduce teacher workload or make certain tasks more efficient. Common ideas include using AI tools for lesson preparation and resource creation, including differentiation by student needs; AI assistants to take on administrative tasks or provide straightforward support to students; AI technologies that can help with marking or grading, when appropriate; and AI-enhanced communication tools that facilitate interactions with parents or students. A small number of these documents also emphasise that AI-supported workload reduction can only have impact if systems avoid a situation in which AI development simultaneously adds to workload or responsibilities in other areas, and if time gained can be dedicated to enhanced practice (U.S. Department of Education, Office of Educational Technology, 2023[32]; European Commission, 2022[33]).

These efforts to leverage AI specifically build on broader approaches to workload reduction that have emerged across a wider group of OECD education systems in recent years. The OECD has previously identified the following approaches to helping teachers make the most of their working time: 1) balancing policy frameworks that regulate time use while supporting school-level flexibility to respond to changing needs; 2) building a common understanding of teachers’ core tasks and broader priorities that optimise their time; 3) fostering collaboration with other staff in schools who can relieve some of the burden of non-teaching tasks (Boeskens and Nusche, 2021[34]).

In relation to the first approach, a few countries and economies are including workload considerations in policy processes:

• Australia is in the process of developing a Teacher Workload Impact Assessment. All new initiatives related to the teacher workforce will be reviewed before they can be implemented.

• England (United Kingdom), through a commitment in the Education Staff Well-being Charter (2021), has pledged to embed teacher and leader workload reduction into all educational policy development and delivery.

Policy efforts in recent years indicate that some countries and economies have also been implementing efforts related to the second approach, in particular through reviewing teacher workloads and developing related guidance for teachers and schools. This includes:

• In 2023, Chile published a Guide to Well-being and Workplace Health for Educational Teams following growing concerns about teachers’ mental health following the COVID-19 pandemic. Workload is identified as a key risk factor. Related guidance includes better defining roles and responsibilities within schools and fostering collaborative professionalism (Ministry of Education, 2023[35]).

• A national inquiry in Sweden has been tasked with proposing measures to reduce administrative tasks for teachers to make more time available for planning and teaching activities, with recommendations due in March 2025 (Ministry of Education, 2023[36])

• England (United Kingdom) established three Workload Review Groups in 2016 which published recommendations to increase efficiency in lesson planning, marking and data management. From this, the Workload Reduction Toolkit supports schools to identify and address workload issues and assess the impact of related strategies. Research into related efforts in 80 schools found that teachers’ weekly working time was reduced by round 1.5 hours (more when controlling for the impact of COVID-19) and the more aware staff were of the Toolkit, the greater the reduction. Efforts to reduce workload were associated with improved teacher well-being, which was in turn linked to higher student attainment. Key success factors included assigning responsibility to someone for implementing related efforts, using technology to automate data management processes and prioritising teacher-designed interventions (Churches and Fitzpatrick, 2023[37]; OECD, 2021[38])

Except for efforts in England (United Kingdom), there is so far little evaluative evidence regarding the impact of policy initiatives that seek to reduce teacher workload. However, a systematic review by Creagh et al. (2023[19]) suggests that policymakers should also consider teachers’ subjective experiences. Teachers’ perceptions of heavy workloads often result from a disconnection between the kind of teacher they want to be and the teacher they have time to be. In addition, the intensification of teachers’ work is frequently tied to broader policy shifts towards accountability and performativity.

Similarly, viewing AI as a silver bullet for reducing teachers’ administrative burden is overly simplistic. In fact, there is a real risk that such efforts could inadvertently increase perceived workloads. Research has shown that digital technologies can heighten symptoms of exhaustion, anxiety, and perceived stress among teachers (Fernández-Batanero et al., 2021[39]). Moreover, reducing time spent on administrative tasks will only support quality teaching if the time gained is sufficient for teachers to engage in activities that directly improve practice. Thus, workload reduction efforts alone are unlikely to lead to higher teaching quality.

Recent advancements in AI present new opportunities to enhance teaching quality, particularly through personalised teaching and learning. AI’s ability to interpret data patterns – such as student comprehension levels or common errors – enables it to suggest next steps and offer corrective guidance that can improve learning outcomes. Additionally, through continuous engagement in the learning and thinking process, AI evaluates the outcomes of prior strategies and generates new, refined approaches (Hwang, 2022[40]).

Intelligence tutoring and adaptive learning systems build on these capabilities to assess individual students’ mathematical understanding and learning preferences, providing personalised feedback and instruction at the student’s pace (Hwang, 2022[40]). Meta-analyses indicate that the tools have a small but statistically significant effect on mathematics performance among primary school students. For older students, they have been shown to lead to higher learning outcomes compared to teacher-led, large-group instruction, non-AI computer-based instruction, and traditional textbooks or workbooks. However, there was no significant difference in outcomes when compared to human-led individualised tutoring or small group instruction (Ma et al., 2014[41]). Generative AI also has a demonstrated impact on learning outcomes among older students through targeted tutoring, data analytics and learning pattern recognition (Sun and Zhou, 2024[42]).

However, the pedagogical benefits of digital technologies, including AI, cannot be realised without carefully designed, implemented, and evaluated digital education reforms. Initiatives that simply expand access to digital devices often fail to improve student performance. For technology to add pedagogical value, teachers must know how to implement it effectively. This need is increasingly relevant as new technologies such as AI and smart technologies are best understood as socio-technical systems that require human-technology collaboration (OECD, 2023[43]). Research indicates, for example, that the quality and quantity of the teacher’s instructional support are the most significant factors mediating AI’s capacity to enhance student learning outcomes (Alharbi, 2023[44]; Blake, 2016[45])

It is essential that teachers are not only equipped with digital devices and skills, but also develop a deep understanding of the added pedagogical value these technologies can bring. This understanding empowers them to implement technology in ways that enhance student learning and encourages critical reflection on the most appropriate ways to use technology – and, indeed, when it may be best not to use it (OECD, 2024[46]). Teachers benefit from having time, space, and resources to experiment with digital tools, as well as support in developing an evaluative mindset. Such a mindset enables them to explore the integration of digital technologies critically, informed by both practice-based evidence from their peers and broader research and data.

In integrating cutting edge technologies, many education systems recognise the benefit of promoting evidence-informed innovation. Analysis conducted for this report of 28 high-level policy guidance documents on AI in education reveals that 26 documents advocate for enhancing teachers’ use of research, evidence and data on AI and digital technologies, with 16 of them doing so to a large extent (Figure 3.4). Cited strategies include supporting teachers and leaders to pilot AI applications at classroom and institutional level; encouraging active collaboration between researchers, developers and practitioners; and ensuring careful monitoring and evaluation to weigh up the value and trade-offs of AI.

Several education systems have been working to foster teacher-led integration of digital technologies. One approach has been establishing dedicated structures that work directly with teachers to promote pedagogical innovation:

• The Centre for Innovation within Chile’s Ministry of Education aims to strengthen the innovation capacity of the education system. Reflecting this organisationally, a team for technological innovation works alongside a much larger team for pedagogical innovation. The Centre’s Innovation Network for Educational Transformation (2022) was established to strengthen professional collaboration and learning among educators and schools and help scale-up good practice in digital and pedagogical innovation. In 2022, the Network ran a national campaign to highlight and learn from innovative approaches developed during the period of the pandemic and post-pandemic recovery. The Network organised in-person and online workshops at both regional and national level through which education actors were invited to share and explore examples of innovation. On an ongoing basis, educators can also submit innovations to the Network which then organises and reports them via the website of the Center for Innovation (OECD, 2024[47]).

• In Denmark, the Knowledge Centres for IT in Teaching promotes the use of advanced digital technology in VET, offering professional development courses for teachers. The centre has also established a network of pedagogical staff and a network of leaders to facilitate the exchange of ideas, practical and technical knowledge and to address common challenges. In addition, two knowledge centres for automation and robot technology each work with more than a dozen VET schools to support teachers to operate virtual reality equipment and robots and incorporate them into their teaching practice (OECD, 2023[14]).

• Spain’s National Institute of Educational Technologies and Teacher Professional Development (INTEF) is responsible for the integration of digital technologies in education and related teacher professional development. It co-ordinates four main types of activities: 1) developing and providing open educational resources; 2) delivering professional development activities to promote teacher digital competences; 3) promoting digital education to different stakeholders (teachers, schools, students, and parents) through initiatives and guidelines; and 4) coordinating teacher collaboration and innovation. Among the Institute’s various projects, the Classrooms of the Future initiative seeks to foster both technological and pedagogical innovation, transforming traditional classrooms into dynamic, segmented areas designed for different types of active learning. These spaces also feature advanced tools such as digital boards, touchscreens, and Virtual Reality glasses. To achieve official recognition, participating schools must adhere to specific criteria which are both pedagogical and technological in nature. This includes using active learning techniques and digital technologies and ensuring the active participation of teachers and the broader school community (OECD, 2023[48]).

Among the education systems leading the integration of AI technologies in education, one approach has been developing national strategies for AI in education on teacher-led innovation:

• Korea seeks to transform its already high-performing education system through the adoption of AI in education, with an emphasis on a pedagogical paradigm shift. To achieve this, Korea is developing AI-based textbooks but also aims to build a system culture of teacher-led innovation (see Annex A).

• Singapore's “Transforming Education through Technology” Masterplan 2030 focuses action on leveraging new technologies to better customise learning for every child and strengthening and scaling the culture of sharing and adapting technology-enabled lessons, resources, and good practices (Ministry of Education, 2023[49]). The implementation process has included a focus on teacher-led innovation, with the intention of empowering educators to take an active role in shaping how they integrate AI into their practice.

In addition to having the time, space, and tools to enhance their practice, teachers also need to develop a mindset that pushes them to become the change agents of their own professional practice. This includes policy approaches that can empower teachers to experiment in the classroom as reflective, enquiry-driven practitioners. Reflective practice is often included in professional standards and some initial teacher education curricula. In Türkiye, for example, The Century of Türkiye Education Model (2024), highlights teacher reflection as one of nine key facilitators of a holistic education approach (Ministry of National Education, 2024[50]).

While embedding a new mindset across the teaching profession will require considerable time and diverse policy efforts, education systems can also take more targeted steps. Policy that encourages teachers to critically engage with research evidence and interpret it within their specific contexts can support the effective use of evidence (Rickinson et al., 2023[51]). For instance, action research models can allow teachers to address challenges in specific contexts, by questioning their own practices, exploring relevant theory, trialling new approaches, and generating new knowledge (McKenney and Reeves, 2020[52]). Teacher inquiry processes also support teachers to systematically investigate and understand problems within their context, leading to deeper insights into their practice (Yalvac et al., 2023[53]; Cochran-Smith and Lytle, 2009[54]). Other empirically tested approaches include reflective dialogue, generative discourse and learning conversations (see Brown and Portman in OECD (2023[55])).

Some education systems have introduced policies that support educators to better engage with and apply research, specifically in their teaching of foundational skills. Perhaps due to the close collaboration with the research community, these approaches have been evaluated. In Brazil, New Zealand and Sweden, policy efforts led to evidence-informed changes to teaching practice, as well as increased teacher reflection and self-efficacy (Baptista and Melo, 2023[56]; Standard of Proof, 2023[57]; Österholm et al., 2016[58]). In Sweden, these efforts showed a long-term positive impact on school leaders’ efforts to strengthen the teaching and professional learning cultures of their schools (Österholm et al., 2016[58]). In New Zealand, research showed a positive impact on student outcomes (Standard of Proof, 2023[57]). Based on the evaluations, one success factor for these policies lies in challenging everyday pedagogical practice through high-quality evidence that is adaptable to different teachers’ needs:

• The academic rigour of stimulus material appears key. In Brazil, the capacity of the Reading and Writing in Early Childhood Education programme (2024) programme to develop teachers’ intellectual autonomy was identified as the main outcome by both evaluators and participants (Baptista and Melo, 2023[56]). Meanwhile, in Sweden, evaluators of the Mathematics Boost (2015) and Literacy Boost (2012) found that impact on practice was less evident for teachers of older students partly because they found the material less relevant or intellectually stimulating (Österholm et al., 2016[58]; Skolverket, 2020[59]).

• Implementation design for Germany’s Qua-math – Developing Teaching and Training Quality in Mathematics (2023) initiative seeks to ensure the material can meet the needs of different teachers. Teachers can select among one of the five pedagogical principles for mathematics that are embedded in an array of learning modules based on their needs. Research teams will continuously review and update the modules throughout the ten-year programme based on feedback and empirical developments (Prediger and Selter, 2024[60]).

• Beyond stimulus material, facilitators also help balance academic rigour and responsiveness to teachers’ needs. Brazil and Sweden appointed researchers as facilitators, to help participants have access to high-level expertise as they engage with and discuss material. In contrast, in the United States, a trial to enhance data-informed practice found no impact on teaching largely because facilitators were not experienced data specialists (Gleason et al., 2019[61]).

A second factor in the success of these policies is ensuring an openness to local adaptation, interpretation, and experimentation:

• In Germany, informed by professional learning and implementation science, facilitators are encouraged to be flexible and open to different ways of engaging with the programme. Rather than seeking implementation fidelity, the policy designers have identified six core aspects that facilitators must embody with the freedom to develop their own approaches beyond that (Prediger and Selter, 2024[60]).

• Similarly, in New Zealand, evaluators of the Better Start Literacy Approach (2021) recognised that participants are adapting their practice in different ways that clearly reflect local and institutional contexts (Standard of Proof, 2023[57]).

Scaling up these efforts is a challenge. In New Zealand and Sweden, programmes have been hard to implement in small or rural schools, partly because the model is not suitably flexible for contexts with limited human resources (Standard of Proof, 2023[57]; Ramböll Group, 2016[62]). Delivering these processes at scale is resource intensive and likely requires a long-term commitment. This suggests education systems may need to prioritise contexts where these processes can have the greatest impact on enhancing teaching quality. Current examples indicate that this prioritisation is already occurring, with a focus on teachers of foundational skills. Additional opportunities for targeted policy include supporting teachers in disadvantaged or low-performing schools and mid-career or experienced teachers, who may benefit most from re-evaluating established practices.

The evaluations raise concerns about the sustainability of positive effects resulting from implementing these processes. Follow-up studies in Sweden showed that improvements in teaching practices and mindset began diminishing 18 months post-intervention. In practice, fostering and sustaining the mindset required for quality evidence-informed teaching requires broader cultural change across the system. Policy action in Norway over the past eight years offers valuable insights into strategies for achieving this type of systemic change (see Annex A). Moreover, the following school- and system-level policy responses are designed to support the effective use of evidence, with relevant policy examples provided.

Looking ahead, AI holds significant potential to support teachers in experimenting with new practices. Augmented and virtual reality, for example, can provide educators with context-specific practice opportunities in low-stakes environments. This can give them highly personalised, structured learning experiences that have been shown to improve creative problem solving, self-efficacy, dialogic learning, and inclusive pedagogies (Huang et al., 2022[63]; Mena, Estrada-Molina and Pérez-Calvo, 2023[64]). Specialised LLMs, such as GPTeach, can also support teachers to practice certain instructional sequences or scenarios through interactive chat, with transcripts that facilitate professional reflection (Markel et al., 2023[2]). However, these approaches remain in the early stages of development and are not widely institutionalised.

Teacher-level policy action can provide teachers with the resources and mindset to enhance teaching practice in changing contexts. School-level policy actions build on this by creating a professional climate that enables empowered teachers to thrive, mainly by establishing a collaborative culture that fosters relational expertise. Collaborative school environments can also offer teachers the collegial support needed to experiment with integrating digital tools into the pedagogical toolkit, engage with research, and explore its practical application in their teaching.

Expertise within education systems is often siloed within individual teachers, leaders, or institutions. Appointing champion teachers or institutions to support peers in developing their practice can help translate isolated innovations into broader, system-wide change. This approach has been recognised in implementation and innovation science as an effective strategy to enhance practice (Brown, White and Kelly, 2021[65]; Drechsler et al., 2021[66]).

Appointing digital champions in schools has emerged as a clear policy trend aimed at enhancing the use of digital technologies in education. By 2019, nearly half of European countries and economies had implemented policies appointing digital champions in schools. These champions are typically responsible for a combination of pedagogical tasks, such as consulting teachers on digital technology use and facilitating professional learning, alongside logistical tasks (European Commission/EACEA/Eurydice, 2019[67]). This policy trend continues to evolve with new technologies: in Korea, for example, recent policies designate schools with a strong record of integrating digital technologies as “AI Schools”, tasking them with championing the integration of AI into pedagogies and to support experimentation with AI.

At the same time, despite evidence that champion roles can effectively promote evidence-informed teaching by influencing behaviours and attitudes towards research use (Gorard, See and Siddiqui, 2020[6]), their use remains limited in this area. In 2021, only 22% of education systems participating in the OECD’s Strengthening the Impact of Education Research policy survey reported appointing research champions (i.e. someone working in a school who has specific responsibility for facilitating research use) and 32% reported having embedded researchers (i.e. someone with a research background working in a school to facilitate research) (OECD, 2022[5]). Although no system-level policies appointing teacher champions for research use were identified for this report, examples emerge of some institutions in both school and higher education. Available evaluations suggest a positive impact on practice although the direct effect on student outcomes remains unclear.

Several success factors emerge from related policy evaluations and implementation reports on the appointment of champions in schools. Firstly, formalising the role of teachers and institutions acting as champions in the school’s structures can help ensure they have sufficient agency to affect change:

• Through competitive selection processes with clear criteria, Korea’s Teachers who Upgrade Classes with High Tech and AI pilot schools (see Annex A) and Norway’s Centres for Excellence in Education (2011) have been able to establish greater legitimacy for champions teachers and institutions.

• For Finland’s Digital Tutor Teachers (2016) and Portugal’s Digital Ambassadors, the allocation of protected time, either within or on top of their teaching load, has been key. This is made possible through financial support from the respective ministries. Between 2016 and 2020, education providers in Finland could access earmarked funding for both local and regional digital tutor activities. Those involved identified these additional resources as the highest contributing factor to success and around three-quarters of schools reported that an end to subsidies would trigger a reduction or termination of activities (Pennanen et al., 2021[68]). Nevertheless, by 2020, many providers had taken steps to ensure that the digital tutor teachers could continue following the end of allocated funding (Pennanen et al., 2021_[29]). Finland is currently undertaking research into how widely the model, and other digital supports, are in use across the system.

Secondly, there may be value in moving beyond individualistic approaches, ensuring champions are part of a collaborative effort that informs education systems’ knowledge needed to understand how to scale up:

• In Finland, regional networks of Digital Tutor Teachers have been identified as a contributing factor to success as they support tutors’ own professional learning and help mobilise knowledge of good practice (Pennanen et al., 2021[68]).

• Research in Norway highlights that collaboration may be the key to extending the reach of institutions appointed as champions to professionals who are more resistant to changing their practice. Evaluators suggest strategically collaborating with early adapters and adopters in other institutions as multipliers who can then influence their own networks (Kottmann, Westerheijden and van der Meulen, 2020[69]).

• In England (United Kingdom), among the Research Schools (2018) appointed to work with peer institutions in challenging contexts to promote the use of evidence in teaching, those that more successfully influence changes in practice have been found to draw on a complex network of local relationships, which typically predate their appointment as a champion institution, although their new roles enabled them to expand their reach further both locally and beyond (Gu et al., 2020[70]).

Finally, evaluations and implementation progress reports indicate that a promising next step – and a challenge – for education systems will be reimagining the roles of champions, envisioning them as leaders of learning as opposed to enactors of a specific policy:

• In Finland, evaluators suggest shifting the focus of the tutor role from the use of digital technologies specifically, to pedagogical innovation and expertise more broadly. This could alleviate pressure on school leaders while also building on one of the key positive impacts of the tutor network (Pennanen et al., 2021[68]). Similarly, in Portugal, evaluators suggest building on the capacity of Digital Ambassadors, in collaboration with local professional development and technology centres, to facilitate professional learning that is bottom-up and locally responsive. This can help overcome the one-size-fits-all approach typical of policy implementation (Wastiau, Looney and Laanpere, 2024[71]).

• Qualitative research indicates that the teachers appointed as Research Champions within the Cardiff Partnership for Initial Teacher Education in Wales (United Kingdom) support student teachers’ research engagement at the same time as learning from them. They also help establish a culture of research engagement among the wider teaching profession, particularly as part of a broader research community made up of student teachers, teaching staff, initial teacher education providers and wider research groups (Beauchamp et al., 2020[72]).

• In the Netherlands, institutions aspiring to be appointed as Expertise Schools within the Development Power Programme (2023) undergo an intensive, tailor-made programme supporting teachers and leaders to guide other schools in research-based methods. Therefore, expertise schools are not simply appointed because they demonstrate best practice, but because they have developed the skills to nurture best practices in others (Development Power, 2024[73]).

These experiences indicate that policymakers can do more to capitalise on the opportunities offered by champion teachers or institutions to drive innovation in teaching practice. This involves expanding their roles in areas of practice in which the application of such models has so far been limited, namely the use of research, evidence, and data in teaching. Additionally, a clear vision is needed for how champions can promote innovation at scale: education systems need to broaden their view of how individual champions fit into a system-wide, collaborative innovation eco-system. Finally, as part of a broader effort to rethink the teaching profession, systems can consider how these specialist roles can strengthen and diversify the teaching workforce, improve practice, and increase attraction and retention (see Chapter 2).

Empowered teachers should be supported to become co-designers, co-researchers, co-developers and co-evaluators of new tools, knowledge, and practice. Many digital tools for education are designed in a way that devalues the pedagogical expertise of teachers and the social and emotional nature of teaching, its sensitivity to local and individual contexts and inherent complexity (Holmes, 2023[74]). To overcome this, partnerships between teachers, EdTech and/or researchers help teachers engage more actively in iterative, reciprocal processes and move away from the traditional linear models of production and uptake (Schlicht-Schmälzle et al., 2024[75]; Molenaar and Sleegers, 2023[76]). As teachers participate in this type of user-driven innovation, it can therefore become easier for them to better align their needs with the end products. This type of partnership can also increase trust and understanding between different professional communities (OECD, 2023[14]).

Such approaches are becoming increasingly relevant as countries and economies work to implement human-centred AI in education. Guidance from the OECD and Education International identifies the co-creation of AI-enabled digital learning tools as one of nine key guidelines to harness the opportunities offered by AI and mitigate its risks to equity, quality and efficiency (OECD, 2023[10]). Nevertheless, only 7 of the 28 high-level policy documents on the integration of AI in education analysed for this report reference the need for collaboration between teachers and developers, 4 of which extended this to include researchers too (see Annex B). Nearly all of these focused specifically on the need for co-creation, noting that this should go beyond simple representation or consultation in order to involve teachers as designers from the earliest stage in the design process (U.S. Department of Education, Office of Educational Technology, 2023[32]; Kenniscentrum Digisprong, 2023[77]). Some also emphasised the importance of teachers’ role as reviewers or evaluators of products and that this can be most impactful when done in rapid and continuous feedback loops (INTEF, 2024[78]; Conference of Ministers of Education, 2024[79]).

Despite the recognised importance of partnerships, practice, research, and development typically remain siloed in education. For example, in digital technology, scientific researchers mainly focus on fundamental research with a limited exploration of classroom applications, while entrepreneurs often lack practical educational and pedagogical expertise (OECD, 2023[48]). Although various partnership models exist for integrating research, evidence and data in teaching practice, those that connect teachers and researchers are typically small-scale and often bottom-up (Schlicht-Schmälzle et al., 2024[75]). This fragmented landscape inhibits both the quality and pace of progress in innovative teaching practices.

System-level efforts identified for this analysis suggest that expanding the scope and cohesion of partnerships requires long-term efforts, reaching up to ten years in the Netherlands, for example. Moreover, these efforts appear to emerge in systems with established foundational cultures in either digital education, such as Korea, or evidence-informed practice, as in the Netherlands and Sweden.

Evaluative evidence and implementation progress indicates some emerging success factors for these policies, corroborated by academic literature, which can help guide education systems undertaking similar efforts. Firstly, bringing together professionals with distinct cultures and practices can impose a variety of bureaucratic and hierarchical barriers to collaboration (Schlicht-Schmälzle et al., 2024[75]). Therefore, horizontal structures can help bring distinct professional communities closer together:

• In the Netherlands, the inclusion of school boards within the formal governance arrangements for the National AI Education Lab is noted as a strength, even if this practice is not always associated with partnership approaches (Molenaar and Sleegers, 2023[76]).

• In Korea (see Annex A), the regional governance structure of the EdTech Soft Labs, which aligns these communities with subnational offices of education, is seen as facilitating their collaboration to respond more directly to regional specialisation strategies and to localised education challenges (Lee, 2023[80]).

• In Sweden, it is now more common for partners in the Education, Learning, Research programme (2017) to establish formal agreements at the start of each relationship defining goals, roles, and responsibilities. Best-practice examples show that business and institutional planning include these agreements to increase accountability and sustainability of the initiatives (National Coordination Group ULF, 2022[81]). Furthermore, agreements are established at each governance layer: strategic objectives are established in a national agreement and then reflected in regional and individual partnership agreements.

Secondly, establishing a “partnership infrastructure” of dedicated spaces and processes facilitates boundary crossing between professions and institutions:

• “Third spaces” are essential. In Korea and Sweden, the emphasis has been on physical spaces. Korea’s EdTech Soft Lab is designed as an active participatory space (Ji-hye, 2022[82]) while in Sweden, multiple spaces at different levels within partner organisations provide a venue for in-depth discussions that increase mutual understanding (National Coordination Group ULF, 2022[81]). Meanwhile, Ireland’s Teachers’ Research Exchange (T-REX) (2017) and Lithuania’s EdTech Center testing platform (2022) illustrate how digital technologies can be used to create dedicated spaces for collaboration in contexts where the system-level culture is less developed and partners are more geographically or professionally isolated (McGann et al., 2020[83]).

• Systematised processes for collaboration are also important. In Ireland, communities can collaborate through T-REX, which offers three model processes for shared activities and supports collaboration without enforcing a single partnership model (McGann et al., 2020[83]). In the Netherlands, an overarching reference framework guides collaboration, updated annually based on the experiences of various partnerships and evolving empirical evidence (NOLAI, 2023[84]). Korea is developing common and optional functions for EdTech Soft Labs to create exemplar operating models (Lee, 2023[80]).

Calls to increase collaboration between educational professionals, digital developers and researchers are not new. However, in the coming years they will become increasingly urgent: education systems cannot hope to capitalise on the opportunities offered by new technologies to address old and emerging challenges in an ethical, human-centred way without committing to concrete steps that answer those calls at scale. Moreover, such partnerships can help solidify teaching as a more attractive, forward-looking profession.

School-level actions can help establish an environment where strong professional relationships enable teachers to continuously enhance their practice. System-level structures and processes, in turn, ensure that empowered teachers who actively seek out opportunities to improve teaching quality in dynamic contexts can align their efforts with broader strategic priorities. The following policy responses present possible structures and processes to ensure high-quality professional learning for in-service teachers.

OECD education systems invest considerable resources in teachers’ and leaders’ professional development, covering costs such as professional services, travel, subsistence, and staff cover (TNTP, 2015[85]). In England (United Kingdom), schools annually spend nearly USD 4 000 per teacher on professional development – just under 3% of total spending, although this likely underestimates the full cost (Brande and Zuccollo, 2021[86]). In the United States, research estimates annual district-level spending on staff development at just under USD 18 000 per teacher (TNTP, 2015[85]).

Ensuring that investments in professional learning translate into better practice for improved student outcomes is essential. Professional learning models that foster deep collegial relationships between colleagues have been identified as powerful drivers of teacher development, bringing multiple benefits that help justify resource investment, including competence development and engagement, job satisfaction and enhanced self-efficacy, creative thinking and experimentation (OECD, 2020_[1]; Viac and Fraser, 2020_[2]; Vangrieken et al., 2017_[3]). When implemented at the institutional level, collaboration can enable teachers to co-develop tailored solutions to shared problems in ways that are relevant and applicable to their mutual context. This helps narrow the knowledge transfer gap common in teacher professional learning (Hemsley-Brown and Sharp, 2003[87]).

For novice teachers and leaders, mentoring can facilitate transitions to the workforce, increasing commitment to and understanding of the profession (Zhao and Zhang, 2017[88]; Rodrigues and Mogarro, 2019[89]). In TALIS 2018, teachers who took part in some kind of induction activity tended to feel more confident in their teaching abilities and more satisfied with their job (OECD, 2019[90]). Moreover, mentoring also enables experienced teachers and leaders to continue their professional growth, share expertise and grow in their specialisation.

Mentoring structures are widespread across OECD education systems. In the Programme for International Student Assessment (PISA) 2022, 82% of students attended schools where teacher mentoring is available, although only a minority (19%) are in schools for whom it is mandatory (OECD, 2023[91]). In recent years, several education systems have been expanding the scope of mentoring programmes:

• Victoria (Australia) is expanding a promising pilot of the Career Start programme, which will provide mentoring support to all government schools across the state. This includes reduced time for face-to-face teaching to engage with induction supports, a dedicated mentor to accelerate the development of teachers’ teaching practice, networking opportunities within local learning alliances, and professional learning to develop professional practice, professional identity, and support well-being.

• Austria introduced a mandatory one-year induction period for new VET teachers in 2019. This includes practical professional development in "practice schools" under the supervision of experienced professional mentors (OECD, 2021[92]).

• In 2018, Ontario (Canada) extended its New Teacher Induction programme to include any teacher in the first five years of their practice. A key element of the programme is continuous mentoring by an experienced colleague. Longitudinal research shows that participating novice teachers have made meaningful and sustained improvements in confidence, efficacy and instructional practice and show commitment to ongoing professional learning. Formal and informal mentorship or support from colleagues were seen as particularly helpful, with relationships between mentors and mentees and between the mentors themselves identified as key to successful implementation (Frank et al., 2021[93]).

• Under the Early Career Framework England (United Kingdom) has introduced an entitlement for all early career teachers to a funded two-year package of structured professional development and support. This includes additional time off from their school timetable for training and mentoring during the first two years, tailored training materials for them and their mentors, and targeted support from approved providers. In 2024, survey data revealed that for 84% of early career teachers, mentors are the key source of advice and support for any concerns or queries. They are viewed as particularly important among mentees with concerns about their progress in reviews and assessments or about the quality of their induction (Department for Education, 2024[94]).

Beyond mentoring for novice teachers, several countries and economies are implementing institution-based peer learning models, where teachers and leaders work together in small professional learning communities (PLC) or engage in instructional coaching. Empirical evidence strongly supports the effectiveness of PLCs in enhancing teacher practice and student outcomes, although context is crucial (Brodie, 2019[95]; Sébastien, Branka and Vincent, 2020[96]). Similarly, meta-analyses of instructional coaching show large positive effects on practice and a smaller positive effect on student achievement (Kraft, Blazar and Hogan, 2018[97]).

Among the policies identified for this report, Quality Teaching Rounds in Australia and MyTeachingPartner in the United States have shown a positive impact on reading and mathematics performance for learners of different ages through randomised controlled trials, (Gore et al., 2023[98]; Foster, 2021[99]). Across policies, there is also evidence of increases in teachers’ enthusiasm, collegiality, and reflective capacity.

While professional learning communities and instructional coaching are not new practices in OECD education systems, there is now a growing body of empirical and policy evidence that policymakers can use to level-up existing efforts.

Success factors include favouring ongoing collaborative inquiry as opposed to time-bound, project-specific approaches:

• In Iceland’s Education Complex initiative (2020) and the Coaching via Skype (2017) programme in Ceará (Brazil), the strong interconnection between the programmes and the daily work of teachers is seen as contributing to their success (Sturludóttir et al., 2021[100]; World Bank, 2018[101]). Similarly, in the United States, evaluators of the My Teaching Partner initiative (2021) found that teachers do not perceive the coaching to be additional work but rather part of their teaching lesson planning (Foster, 2021[99]). In Victoria (Australia), PLCs, which have been strongly supported since 2016, are part of a wider effort that frames professional learning as a routine practice engaged in with colleagues and tightly focused on the core work of teachers: improving student outcomes (VAGO, 2019[102]). In Australia, evaluators concluded that the success of the Quality Teaching Rounds (2014) lies in the fact that, unlike most professional development, it is pedagogy focused as opposed to content focused (Harris et al., 2022[103]).

• The emphasis on process over content is extended further when, in some cases, the PLCs or coaching interventions are integrated into school management processes. For example, in Victoria (Australia) the PLCs must be embedded in school improvement planning to ensure they respond to shared institutional needs (VAGO, 2019[102]). In Chile, video coaching for school leadership teams (2020) was integrated into mandated approaches for schools to reopen following the COVID-19 pandemic (OECD, 2020[104]).

Secondly, some policy experiences show the value of making teaching and learning a more tangible object of analysis through using specific tools:

• In Australia, the Quality Teaching Model provides shared concepts and language with which participants analyse teaching practice (Harris et al., 2022[103]). Analysis of longer-standing programmes in the United States in ECEC also emphasise the importance of using quality standards that are well-evidenced to guide coaching interventions (Schachner et al., 2024[105]).

• Digital tools have also been useful: in particular, the use of videos of classroom practice in Australia and the United States has been reported as helping to make visible instructional processes that had previously been hidden to teachers, putting them at the forefront of discussions (Foster, 2021[99]; Harris et al., 2022[103]).

Finally, education systems are facilitating teachers’ participation in structured collaborative professional learning through digital technologies:

• Singapore’s One Portal All Learners platform allows teachers to set up online collaboration groups, facilitating asynchronous knowledge construction that complements face-to-face interactions. Users appreciate the platform’s compatibility with mobile technologies and its unified log-in with existing email systems (Lee et al., 2020[106]). By facilitating asynchronous collaboration, digital technologies also help overcome the challenge of bringing together participants with different roles and responsibilities and incompatible work schedules (OECD, 2024[47]).

• In Chile, teachers with advanced certification are obliged to support colleagues in their own school either through structured development activities or mentoring. Each advanced teachers receives a personalised portal which acts as a dedicated virtual space through which they can produce, create, and distribute resources with the colleagues they work with. This facilitates interactions between colleagues within the same school but also systematises knowledge sharing between advanced teachers, who together make up the Teachers for Teachers network (MINEDUC, n.d.[107])

A key challenge of these models is resourcing. For example, both Austria and England (United Kingdom) have experienced challenges in recruiting mentors for novice teachers and in minimising their turnover. In England (United Kingdom), suggested approaches to addressing this challenge include involving more junior staff as mentors, providing mentors with buddies, increasing networking opportunities, protecting allocated time for mentors, and accrediting the mentor role in some way.

However, there is also scope for AI to play a role in addressing this resourcing challenge. AI-powered mentors, coaches and assistants that provide automated feedback on recorded lessons, have been shown to improve instructional practice although results vary by tool (Demszky et al., 2023[108]). Combined with learning analytics, AI can enhance teacher noticing and decision-making, supporting teachers to better align practices with competency frameworks. Specifically trained LLMs can provide feedback or prompt reflective discussion on lesson plans and assessment of students’ work. They can support teachers’ self-directed learning too, diagnosing needs, setting learning goals, designing personalised plans, and supporting reflection (Neumann et al., 2021[3]).

Despite this potential, AI’s capacity to enhance teachers’ professional learning does not appear to be a prominent focus in emerging AI integration policies in education. Among the 28 high-level guidance documents published by OECD education systems and analysed for this report (see Annex B), there seems to be a clear understanding of the need to support teachers to develop AI literacy and new pedagogies for AI-enhanced practice. However, there is little reference to how AI itself can support this. While 26 documents acknowledge the need for teachers to develop competencies in the use of AI technologies for teaching and learning, only 2 explore in more detail how AI can enhance teachers’ professional learning more broadly.

Of the high-level documents that do recognise AI’s potential to enhance professional learning, cited capabilities include: using AI to analyse teaching through algorithms that suggest moments in classroom discussions worth reviewing with a coach; tools that automate the monitoring and analysis of teaching and learning, helping teachers to turn even micro-moments in the classroom into opportunities for professional learning; and simulators that can change the faces and voices of students, allowing teaching situations to be shared and discussed anonymously among colleagues (U.S. Department of Education, Office of Educational Technology, 2023[32]; Kralj et al., 2024[109]). Another example is the potential use of AI as a professional coach to support both lesson planning and instruction (Kralj et al., 2024[109]).

The standards-based approach to professional learning in OECD education systems defines professional competences and ties their acquisition to career and salary trajectories, appraisal, and development. While this approach has been crucial for professionalising teaching (Guerriero, 2017[15]), it presents two major challenges: 1) it may overlook the deeply personal and non-linear nature of teacher professional identity (Suarez and McGrath, 2022[110]); and 2) it can prioritise generic, externally delivered professional development aligned with reform agendas over initiatives driven by the needs and interests of teachers and leaders (Dimmock et al., 2021[111]).

Ongoing professional development that aligns with teachers’ aspirations and interests is crucial for teaching quality and job satisfaction. It can also help make teaching a more intellectually attractive profession: evidence indicates that teachers with targeted career support and development opportunities are more likely to remain in the profession across all education levels. Furthermore, when teachers experience professional growth and career progression, their efficacy and ability to improve student outcomes increase (OECD, 2020[18]).

Strengthening the diagnostic base to improve understanding of teachers’ developmental needs is therefore crucial. This approach can both enhance the strategic impact of professional learning while increasing teacher engagement. First, regular formative teacher appraisal can support teachers, schools, and the education to better understand developmental needs. This appraisal usually relies on classroom observations, teachers’ self-evaluation, and teaching portfolios to provide specific feedback to guide continued professional growth (OECD, 2013[112]). Typically conducted by school leadership, more impactful appraisal models involve collaboration between school management and teachers to create individualised development plans, defining activities aimed at improving specific aspects of teaching practice (Maghnouj et al., 2020[113]).

Some education systems have been strengthening teacher self-assessment in regular formative appraisal processes, including through digital tools:

• In Alberta (Canada), the Teacher Growth, Supervision and Evaluation Policy (2015) established a mandated annual dialogue between teachers and school leaders regarding the professional development plans for each teacher based on their strengths and weaknesses. These are identified by the teacher and reviewed or approved by the school leader or a group of teachers appointed by school management. To facilitate self-assessment and ensure that it leads to pertinent insights, Alberta introduced the Reflection on My Professional Practice tool. Based on teachers’ self-reflections, this digital, interactive platform produces a profile of strengths and considerations for professional development and includes suggested resources to support implementation (Alberta Teachers' Association, 2024[114]).

• Japan’s National Teacher Professional Development Platform (2023) will, among other features, enable teachers to evaluate the quality of their professional learning and its impact on their practice (see Annex A).

• In Wales (United Kingdom), teachers, leaders and teaching assistants can use the digital Professional Learning Passport (2020) to self-assess their learning needs, track their development activities, and self-evaluate effectiveness. At system-level, this data can help build a picture of the extent to which professional learning is meeting needs, both in the offer and in practice, at institutional, regional, and national level. However, there is more to do to ensure that teachers across the career spectrum engage with the tool (Thomas et al., 2023[115]).

Beyond better understanding teachers’ immediate professional development needs, education systems can also work to improve capacity to predict possible emerging needs through foresight activities. Some education systems are implementing collaborative foresight processes, including through scenario building and teacher personas:

• Austria, the Flemish Community of Belgium and Wales (United Kingdom), have carried out foresight work with support from the OECD’s New Professionalism and the Future of Teaching project. Those participating include representatives of the ministries of education, teacher unions, education research institutes and initial teacher education providers. In these collaborations, system-level actors work together with practitioners to co-create future scenarios for the profession and guide the direction of the professional learning offer, accordingly. They also use teacher personas to strengthen their future scenarios and support backcasting discussions (OECD, 2024[116]).

• In Estonia, the Development Monitoring Centre has produced 12 future scenarios for the teaching profession, set in 2040. This foresight work recognises the varied experiences of teachers working in different contexts with four alternative scenarios each for professionals working in schools in rural areas, in towns and in cities (Foresight Centre, 2023[117]).

• Finland’s Forum for Development of Early Childhood Education Professional Development (2019-2022) convened training providers and educators for ongoing dialogue on the short- and longer-term developmental needs of the profession. These efforts involved a broad coalition of stakeholders through direct inclusion in governance structures, such as steering committees and working groups (Ministry of Education and Culture, 2024[118]).

• The current term of Finland’s Teacher Education Forum, for teachers in primary and secondary education, aims to create future scenarios through visioning teacher education of the future.

• In Singapore, efforts to update the Teacher Education 21 model included the development of Five Roles of Graduands as Future-ready Teachers (e.g. Shapers of Character, Agents of Educational Change), to help student teachers see the relevance of their learning to the broader national ecosystem (National Institute of Education, n.d.[119]).

In navigating the complexities of teacher development, education systems must balance structured standards-based approaches with flexible, personalised professional learning opportunities that resonate with teachers’ individual aspirations and evolving roles. Furthermore, strengthening diagnostic practices, including formative appraisals, self-assessment tools, and collaborative foresight activities, can help to make professional learning both more strategically aligned and responsive to teachers’ immediate and future needs. These approaches can foster a culture where professional growth is valued, creating a supportive environment for teachers to continually refine their practice and adapt to emerging educational challenges. As OECD countries and economies look toward 2025-2030, embedding these diagnostic strategies within a coherent framework for teacher development will be essential in sustaining teaching quality, improving job satisfaction, and advancing student outcomes.

Drawing from the analysis in this chapter, education policymakers may consider the following steps as they work to enhance teaching quality in the current context of technological change:

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