As highlighted in previous chapters, education systems are now confronted with the urgent challenge of bridging the gap between the demand and supply of quality teachers, particularly as digitalisation, AI, and other technological innovations continue to transform educational environments. These changes, combined with demographic shifts and persistent inequities, require a more strategic and adaptive approach. As part of these efforts, modern technologies, such as AI-driven learning analytics and predictive tools, offer new opportunities for scenario planning and monitoring (Salas-Pilco, Xiao and Hu, 2022[1]). By also leveraging these innovations, education systems can enhance their capacity to track progress in real time, adapt policies proactively, and respond effectively to evolving needs.

Findings from the EPO Survey 2024 reveal that many education systems have yet to forecast workforce needs for teaching and leadership roles over the next few years. While two-thirds have developed projections for primary and secondary education, fewer than half have done so for early childhood and pre-primary levels (Figure 4.1). These gaps point to potential blind spots where targeted data and insights could better inform future workforce planning.

Building monitoring, evaluation and forecast capacity is becoming critical as shortages vary across education levels, disciplines and regions. The most acute teacher shortages appear to be in specialist areas, rural and remote locations and highly disadvantaged areas, which struggle to attract and retain teachers (OECD, 2024[3]). More granular and sophisticated data would enable education systems to quantify and address shortages – current and projected – and shape policy responses as part of a targeted and coherent long-term strategy. Yet, how the needed data are identified, collected, and processed, are important policy decisions that require thoughtful reflection about the specific context, local and systemic aims, and resources to achieve this.

For education systems, understanding the factors that support or hinder policy success in improving teaching quality remains critical. Such insights enable the refinement of strategies to address both current and future challenges. The EPO Survey 2024 indicates that, since January 2023, most education systems have introduced various initiatives to attract, retain, and foster high-quality teaching practices and professional learning. These initiatives vary widely in scale, focus, and maturity – from broad system-wide policies to targeted, localised efforts (OECD, 2024[2]). Many education systems also reported that additional practices are planned for the coming period, which will require resourcing and effort.

Survey responses also highlight notable barriers to policy monitoring and evaluation across different areas of teacher policy. A particularly high proportion of education systems report obstacles in evaluating policies aimed at strengthening professional learning compared to other areas. In many cases, policy evaluation efforts are constrained by limited human resources, particularly for initiatives to enhance teacher practices and professional development, as shown in Figure 4.2.

The evaluation of professional learning, in particular, may be especially challenging due to the need to account for varying, complex theories of change that underpin different initiatives. Furthermore, effective evaluation of professional learning should ideally track participants’ use of new knowledge and skills in their practice – often a deeply contextual process that defies standard measurement – and assess the subsequent impact on student outcomes, which can be difficult to attribute directly to professional learning (Guskey, 2000[4]). Moreover, the long timeframe needed to observe change in teaching practice and student outcomes after professional learning adds further complexity to these evaluations.

Additionally, some respondents noted that evaluating policies in non-school education settings presents particular challenges, often due to underdeveloped data infrastructure. For other education systems, the challenge lies not in infrastructure but in data availability; key data needed to understand teacher supply and demand are sometimes not collected or lack sensitivity to policy timelines, as noted by the Netherlands in the EPO Survey 2024. Another common issue reported through the survey is the limited capacity to monitor and evaluate policy outcomes effectively, which hinders systems' ability to adapt current policies or shape future decisions based on robust evidence.

These insights emphasise the critical need to strengthen foundational monitoring and evaluation systems. The following section presents approaches to help education systems address these gaps and build capacity for data-driven policy adaptation and planning.

A robust policy monitoring and evaluation framework, aligned to strategic visions of education systems, is essential for effective implementation, continuous policy development, and progress assessment. Such a framework can help identify challenges in achieving policy objectives and provide evidence to support policy adaptation, continuation, or expansion (OECD, 2023[5]).

Furthermore, establishing a continuous and longitudinal approach to monitoring and evaluation – beyond temporary or ad hoc measures – is crucial. This approach allows for a more thorough understanding of evolving challenges, supporting the development of adaptive implementation strategies, and providing insights into what works and where. These efforts can specifically address monitoring and evaluation challenges highlighted throughout this chapter, such as:

In England, the early years sector is a varied and disparate one and at times it can be challenging to define the source of problems like educators leaving the workforce and apply a blanket strategy across multitudes of issues and whilst there is a range of evidence indicating why people leave the workforce, there is a lack of policy evidence to suggest what would work in better retaining staff.

It can also offer multiple benefits. The first is aligning policy actions and resources with stated purposes, requirements and regulations. Secondly, it can facilitate learning about the ways in which policies are implemented at different levels of the system and the extent to which they have impact on outcomes. Thirdly, it can offer insight into how resources could be used more efficiently and effectively (Golden, 2020[6]). Several considerations emerge for education systems for monitoring and evaluation, as outlined below.

Monitoring and evaluating the impact of teacher attraction and retention policies is a complex undertaking but is critical for informing design and implementation. Education systems are making these approaches increasingly sophisticated to understand what works:

• England (United Kingdom) is making efforts to understand the early indicators of impact, which over time will lead to improved ability to understand the long-term impact of initiatives and the combined impact of multiple policies. It has built in early indicators of impact into process evaluations, such as pre- and post-surveys assessing perceptions. An example of this is an evaluation of the Early Career Framework induction process. It analyses post-intervention retention rates and will use comparisons with other early career cohorts to understand changes in relative retention patterns, as well as additional cohorts to build better counterfactual groups and determine its impact. Furthermore, programme-level measures have been introduced so that the combined impact of multiple policies can be monitored.

• Slovenia is building specific data requirements into its ongoing data collection processes to aid monitoring educational progress and systematic planning of measures/policies. A permanent working group has been established to coordinate and guide administrators and users of data within the ministry.

• Portugal has set up a “proximity model” at a national level to monitor and evaluate the implementation of its Student Profile for Compulsory Education. The Ministry of Education supports the work of schools through dedicated teams to monitor and evaluate the implementation of its Student Profile for Compulsory Education in schools. The model is assisted locally by regional teams, creating communities and networks for sharing practices between schools.

Some education systems are also developing mechanisms to better forecast and address teacher supply and demand issues:

• The Netherlands has developed a sophisticated labour market dashboard that provides information on the teacher workforce.

• The Flemish Community of Belgium publishes an annual education labour market report and future forecasts (Vlaanderen is onderwijs & vorming, n.d.[7])

• Australia is developing a national Australian Teacher Workforce Data initiative that administers an annual teacher workforce survey and draws together teacher supply and demand data to inform the future of the teaching profession. By connecting initial teacher education data and teacher workforce data from across Australia, the initiative aims to provide nationally consistent data (Australian Institute for Teaching and School Leadership, 2024[8]).

However, responses to the EPO Survey 2024 show that challenges persist across education systems to support adequate monitoring and evaluation processes related to addressing teacher shortage and teaching quality. Some aspects to keep in mind emerge below, based on the analysis conducted in previous chapters.

To evaluate policy effectiveness, it is important to define what success looks like within specific contexts. This starts with a vision that sets out clear objectives, supported by a monitoring and evaluation framework of measurable indicators.

Considering the factors introduced in Chapter 2 of this report shaping teacher demand (e.g. class size, duration of compulsory education, or required learning time) and supply (e.g. teacher preparation and certification, working conditions, or professional prestige), key questions related to efforts for addressing teacher shortage are:

• How many teachers are needed to meet demand?

• How long should teachers reasonably be expected to stay in the profession?

• Is it realistic to assume that teaching should be a lifelong profession?

For improving teaching quality, critical questions drawing from the analysis conducted in Chapter 3 include:

• What constitutes effective teaching practice at a system-level, and in the local context?

• What benchmarks should be used to measure improvement, what student outcomes can be expected arising from these practices, and how can measures be sensitive to local context?

These questions are challenging to answer. For instance, defining success in reducing teacher shortage is complicated by trends towards more fluid career pathways and declining expectations that individuals will stay in a single profession for life. Nonetheless, it is reasonable to expect that policy efforts should increase teacher retention, both for their economic benefits – by reducing costs associated with retraining and hiring replacements and ensuring a return on investment – and important educational outcomes brought about by improved teacher retention.

Setting clear and realistic metrics for what constitutes success is essential, not only for monitoring and evaluation purposes, but also for effective strategic planning and resourcing, which is an effort undertaken in other professions too (Box 4.1). This can be supported by a vision that sets out the system-level objectives, as well as a comprehensive monitoring and evaluation framework.

A key challenge for education systems is developing the data infrastructure to collect, store, monitor and analyse the data required for policy insights. Without robust data systems, understanding the current state of teacher workforce (and teacher supply and demand), and tracking progress and impact of policy interventions, is difficult. Additionally, a lack of comprehensive longitudinal data can prevent education systems from assessing the impact of their policies and the career trajectories of teachers (OECD, 2021[12]; Golden, 2020[6]). In the EPO Survey 2024, some countries reported having insufficient data, as well as a lack of data linkage systems that would be necessary to support quality monitoring and evaluation processes. This can limit education systems’ responsiveness to the issue. As noted by Slovenia:

When implementing monitoring and evaluation in the field of the teaching profession, we are faced with insufficient data for quality monitoring of progress. Slovenia participates in most of the major international large-scale surveys in the field of education, so the data coverage in this area is good, but quality monitoring also requires specific data, which can only be obtained with our own permanent data sources.

Efforts to improve data infrastructure can be complex, time-consuming, and require coordination across different ministries, levels, and data custodians. However, there are several considerations for education systems to improve monitoring and evaluation processes, ensuring that the right data can be gathered. These include:

Improving data infrastructure, and leveraging these systems, also requires investment in the data analysis, reporting and dissemination of results – activities that can be resource intensive and costly. Technology-based tools can be of help, if integrated strategically in these processes, and managing potential risks that come with these technologies (See Chapters 1 and 3). AI-powered platforms can automate data collection efforts, reduce administrative burdens, and enable predictive analytics for more accurate workforce forecasting and policy adjustments. For instance, AI-driven insights can help identify trends in teacher attrition and inform targeted interventions (OECD, 2023[5]). Moreover, stakeholders need the capability and capacity to translate data into actionable insights that can inform implementation efforts and support accountability and transparency of policy decisions. This is a critical factor in effective policy implementation and should be planned for from the beginning of the implementation process (OECD, 2020[13]).

There are concerns about the quality of evaluative evidence on the impact of strategies to attract and retain teachers and professional learning currently, and the lack of available resources and capacity within ministries to enhance it going forward. Two ways in which education systems can tackle this include placing teachers at the centre of reform implementation and evidence generation, as outlined below. As part of these processes, leveraging AI-driven analytics can enhance the quality and granularity of this evidence.

By bringing teachers and leaders closer to evidence generation and empowering them as experimenters and digital innovators in their own classrooms, systems can better support them in translating knowledge into better practice – as well as supporting monitoring and evaluation efforts at various levels of the system.

This could include mobilising teachers and leaders as part of a whole-of-system effort to build a strong and rich evidence base, for example around the integration of digital technologies. As shown above, education systems indicated a lack of human resources available to generate evaluative information about which digital technologies aid learning in the classroom and which do not. But teachers and leaders who are empowered experimenters produce valuable evidence as part of their daily practice. During the COVID-19 pandemic, it was clear that, while national-level guidance was important, local and institutional responsiveness were key, and so institutional leaders were generally encouraged to adapt regulations, recommendations and guidelines to suit their own contexts (OECD, 2020[14]). Furthermore, empowering teachers and leaders as evidence contributors can be significantly enhanced through the use of digital tools and AI-based platforms. For example, AI-driven reflective tools can help teachers systematically document evidence from their practice, offering insights that external evaluations may overlook. Such technology-enabled approaches can support a decentralised evaluation model, fostering a deeper understanding of effective methods and translating this knowledge into improved classroom practice (Phillips, Saleh and Ozogul, 2022[15]).

Education systems can take the following steps to strategically mobilise this knowledge:

Teachers can also be placed at the centre of efforts to improve understanding of professional learning’s impact. This is because teachers are best equipped to establish the links between a professional learning activity, its outcomes for practitioners and resulting changes to practice and student outcomes (OECD, 2021[16]). Decentralising professional learning evaluations can mirror wider policy trends which see systems reducing reliance on standardised student assessments and external institutional evaluation in favour of practitioner-led approaches (OECD, 2023[17]). It can also align with broader efforts to embed a system culture of evaluative thinking. This could include engaging in open and collaborative discussion with the profession, professional development providers and researchers about what can realistically be expected from systematic practitioner-led evaluation of professional development and what support would be needed to achieve it (McChesney and Aldridge, 2018[18]).

Furthermore, an area for further consideration includes facilitating practitioner-led evaluation methods that go beyond satisfaction measures, towards understanding practitioners’ learning, use of new knowledge and skills, and student learning outcomes. The use of AI-based tools can enhance the effectiveness of practitioner-led assessments of professional learning, and how it translates into the classrooms. AI-driven platforms can support teachers in designing and conducting their own evaluations, offering real-time data visualisations and automated analysis. This approach can not only reduce the reliance on standardised assessments but also empower teachers to generate actionable insights tailored to their unique classroom contexts (Celik et al., 2022[19]). At the same time, while a framework and tools for evaluation can make routine evaluation more manageable and robust but teachers and leaders need to be able to easily access them (McChesney and Aldridge, 2018[18]). Advanced technologies can support this as well, as can requirements on professional development providers to supply, for example, theories of change for their intervention. This includes supporting teachers and leaders with structures, resources and guidance they require to translate this knowledge into evaluative findings and into practice.

How can practitioners and policymakers monitor, evaluate and adapt teaching practice in as close to real time as possible? This is a common challenge across education systems, as shown by responses to the EPO Survey 2024. Ministries participating in this survey reported a lack of a responsive implementation process which collects, learns from, and responds to monitoring data, as well as follows a realistic, flexible timeframe – all of which can inhibit implementation.

For any evaluation to be effective, and its cost justifiable, it must promote learning that informs future efforts or modifies existing approaches (Golden, 2020[6]). While policy evaluations determining the impact of national reforms are often resource intensive and take considerable time, there is scope to consider ways in which smaller scale or bottom-up evaluation can support incremental progress.

In this regard, education policymakers could take inspiration from trends in rapid approaches to evaluation that are developing across public administration and health and social care. In the public sector, as part of work to support the implementation of the OECD Declaration on Public Sector Innovation, the OECD has proposed a range of rapid evaluative approaches. This includes establishing dedicated simulation or testing environments and encouraging piloting and prototyping. Another proposal is to adopt mission-oriented approaches. These focus less on whether specific policy efforts solve an identified challenge and more on whether they help move the system towards the desired transformation. Anticipatory approaches, such as visioning, scenario building and stress testing, can also play a role. These efforts encourage public sector workers to pay attention to the “little stories” of innovation, not just the “star innovators” or known heroes. This can help provide concrete and relatable examples to other public servants while simultaneously acknowledging and incentivising everyday efforts (OECD, 2022[20]).

In health care, evaluators are also reflecting on ways to speed up evaluations to meet emerging needs more quickly. A scoping review of evidence from high-income countries (see Norman et al. (2022[21])) indicates that rapid evaluation approaches typically employ qualitative or mixed methods. They mainly assess aspects of user experience and acceptability, or implementation barriers and facilitators. Other approaches include focusing on a certain moment in a standard process or narrowing the scope or depth of existing methodologies. Inevitably, rapid evaluation that remains resource efficient involves trade-offs in rigour or scope; that said, it is useful to establish when such trade-offs are legitimate or determine certain non-negotiables.

Technology also facilitates rapid evaluation through communication tools, and automation or simplification of data collection, collation and analysis. Rapid evaluation models can be further strengthened through AI and automation. AI-enabled data analytics can process real-time feedback from pilot initiatives, enabling faster adjustments and more nuanced insights. For example, automated analysis of classroom data can help identify effective teaching strategies more quickly, reducing the time required for evaluation cycles (Salas-Pilco, Xiao and Hu, 2022[1]).

“Sludge audits” are also a behavioural approach gaining traction in some OECD countries to help address similar issues. They seek to identify, quantify and reduce the excessive frictions experienced by policy audiences to increase uptake and impact. They complement existing service delivery improvements, such as reducing administrative burdens and enhancing user experience design (OECD, 2024[22]). Education systems can seek to optimise administrative tasks across the system in order to better support teachers and increase their efficiency through Artificial Intelligence (AI), as is being done for professionals in other public sectors (Box 4.2).

Beyond monitoring policy progress through core measurement indicators, education systems must also consider contextual factors that may impact the success of policy implementation. Integrating these factors into monitoring and evaluation frameworks can help identify potential obstacles early and guide more effective policy adjustments. According to the EPO Survey 2024, 22 participating education systems (69% of the 32 systems implementing new policies since January 2023) reported at least one barrier to implementation across different educational levels (Figure 4.3).

Insights from the survey reveal an interesting distinction between policy implementation and evaluation challenges. While strengthening professional learning is most often cited as having at least one barrier to effective monitoring and evaluation, policies to enhance teaching practice pose greater challenges for implementation, with 57% of education systems facing barriers in this area. Approximately half of the systems also reported implementation barriers in policies aimed at attracting (50%) and retaining (54%) teachers. Challenges in implementing professional learning initiatives were noted by a slightly smaller share (39%) of education systems. As one respondent noted in the EPO Survey 2024:

The biggest obstacle is the systemic aspect and the scale of the reform undertaken. The sustainability of the reforms has proven to be precarious, and some projects have had to be put on hold. The concurrent nature of the ongoing reforms and the health crisis has delayed the implementation of certain changes.

Education systems frequently encounter implementation obstacles across multiple policy areas, underscoring the challenges inherent in educational reform. These overlapping barriers risk hindering progress on both the urgent and important priorities, which are already challenging to balance and implement simultaneously (Table 4.2).

Insufficient resources and stakeholder capacity emerged as the most frequently cited obstacle, with approximately one-third of education systems reporting this challenge across all policy areas (Figure 4.4.). This resource gap highlights the need for careful planning and resourcing to ensure implementation success across diverse policy domains.

Beyond resource limitations, there is notable variability in implementation barriers depending on the policy area. Insufficient understanding of the proposed policy solution was reported by over a quarter of the systems attempting to strengthen teaching practices (26.1%, or 6 out of 23 systems) and attract teachers (26.7%, or 8 out of 30 systems). For strengthening teaching practices specifically, 30.4% of respondents (7 out of 23 systems) noted that institutional arrangements and policy alignment within the education system are not conducive to successful implementation, a barrier less commonly reported in other areas. These findings suggest that effective reform requires tailored approaches that reflect the unique demands of each policy area and educational level and must be accounted for in the design of monitoring and evaluation frameworks.

In addition to financial resourcing challenges, there are sometimes limitations on how resources are allocated, often influenced by institutional guidelines. For example, the French Community of Belgium noted:

Financial resources have been released to enable said organising authorities to deploy support policies for teachers, but institutional guidelines, particularly in terms of allocation of these resources within schools, constitute limits to the implementation of appropriate strategies.

Recognising these barriers, Japan has collected and disseminated good practices contributing to resolve teacher shortages, and suggested some measures that can be used as reference for each local government to promote their own initiatives. These are intended to overcome the barriers associated with not having a clear and detailed implementation strategy. Furthermore, this type of system-level guidance can support consistency and coherence in implementation across local government areas, while allowing for local adaptation.

Based on the findings in this chapter, policymakers may consider the following steps to address the immediate priority of teacher shortages and the longer-term goal of strengthening teaching quality:

[8] Australian Institute for Teaching and School Leadership (2024), Australian Teacher Workforce Data, https://www.aitsl.edu.au/research/australian-teacher-workforce-data (accessed on 3 September 2024).

[19] Celik, I. et al. (2022), “The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research”, TechTrends, Vol. 66/4, pp. 616-630, https://doi.org/10.1007/s11528-022-00715-y.

[6] Golden, G. (2020), “Education policy evaluation: Surveying the OECD landscape”, OECD Education Working Papers, No. 236, OECD Publishing, Paris, https://doi.org/10.1787/9f127490-en.

[4] Guskey, T. (2000), Evaluating professional development., Corwin Press.

[10] Huerta Melchor, O. (2013), “The Government Workforce of the Future: Innovation in Strategic Workforce Planning in OECD Countries”, OECD Working Papers on Public Governance, No. 21, OECD Publishing, Paris, https://doi.org/10.1787/5k487727gwvb-en.

[23] Johnson, B., J. Coggburn and J. Llorens (2022), “Artificial Intelligence and Public Human Resource Management: Questions for Research and Practice”, Public Personnel Management, Vol. 51/4, pp. 538-562, https://doi.org/10.1177/00910260221126498.

[9] Kroezen, M., M. Van Hoegaerden and R. Batenburg (2018), “The joint action on health workforce planning and forecasting: results of a European programme to improve health workforce policies”, Health Policy (New York) 122, pp. 87-93, https://doi.org/10.1016/j.healthpol.2017.12.002.

[18] McChesney, K. and J. Aldridge (2018), “A review of practitioner-led evaluation of teacher professional development”, Professional Development in Education, Vol. 45/2, pp. 307-324, https://doi.org/10.1080/19415257.2018.1452782.

[24] Ministère de la Transformation et de la Fonction Publiques (2024), Strategy for the use of artificial intelligence in human resources management in the State civil service, https://www.fonction-publique.gouv.fr/files/files/Publications/Publications%20DGAFP/2024/guide-strategie-usage-intelligence-artificielle-EN.pdf (accessed on 11 August 2024).

[21] Norman, G. et al. (2022), “Approaches to enabling rapid evaluation of innovations in health and social care: a scoping review of evidence from high-income countries”, BMJ Open, Vol. 12/12, p. e064345, https://doi.org/10.1136/bmjopen-2022-064345.

[3] OECD (2024), Education at a Glance 2024: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/c00cad36-en.

[2] OECD (2024), Education Policy Outlook National Survey for Comparative Policy Analysis.

[22] OECD (2024), “Fixing frictions: ‘sludge audits’ around the world: How governments are using behavioural science to reduce psychological burdens in public services”, OECD Public Governance Policy Papers, No. 48, OECD Publishing, Paris, https://doi.org/10.1787/5e9bb35c-en.

[17] OECD (2023), PISA 2022 Results (Volume II): Learning During – and From – Disruption, PISA, OECD Publishing, Paris, https://doi.org/10.1787/a97db61c-en.

[5] OECD (2023), Shaping Digital Education: Enabling Factors for Quality, Equity and Efficiency, OECD Publishing, Paris, https://doi.org/10.1787/bac4dc9f-en.

[20] OECD (2022), Tackling Policy Challenges Through Public Sector Innovation: A Strategic Portfolio Approach, OECD Public Governance Reviews, OECD Publishing, Paris, https://doi.org/10.1787/052b06b7-en.

[12] OECD (2021), Education Policy Outlook 2021: Shaping Responsive and Resilient Education in a Changing World, OECD Publishing, Paris, https://doi.org/10.1787/75e40a16-en.

[16] OECD (2021), “Teachers’ professional learning study: Diagnostic report for Wales”, OECD Education Policy Perspectives, No. 33, OECD Publishing, Paris, https://doi.org/10.1787/caf912c7-en.

[13] OECD (2020), “An implementation framework for effective change in schools”, OECD Education Policy Perspectives, No. 9, OECD Publishing, Paris, https://doi.org/10.1787/4fd4113f-en.

[14] OECD (2020), Lessons for Education from COVID-19: A Policy Maker’s Handbook for More Resilient Systems, OECD Publishing, Paris, https://doi.org/10.1787/0a530888-en.

[15] Phillips, T., A. Saleh and G. Ozogul (2022), “An AI toolkit to support teacher reflection”, International Journal of Artificial Intelligence in Education, Vol. 33/3, pp. 635-658, https://doi.org/10.1007/s40593-022-00295-1.

[11] Queensland Health (2020), Strategic health workforce planning framework, https://www.health.qld.gov.au/__data/assets/pdf_file/0034/986614/shwpf-guide-20200623.pdf (accessed on 27 August 2024).

[1] Salas-Pilco, S., K. Xiao and X. Hu (2022), “Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review”, Education Sciences, Vol. 12/8, p. 569, https://doi.org/10.3390/educsci12080569.

[25] U.S. Office of Personnel Management (2022), 2022 Federal Workforce Priorities Report, https://www.opm.gov/policy-data-oversight/human-capital-management/federal-workforce-priorities-report/2022-federal-workforce-priorities-report.pdf (accessed on 11 August 2024).

[7] Vlaanderen is onderwijs & vorming (n.d.), Onderwijsarbeidsmarkt, https://onderwijs.vlaanderen.be/nl/beleid/onderwijsarbeidsmarkt (accessed on 15 October 2024).