This chapter brings together the report’s main findings and shows how the three pillars of the lifelong learning (LLL) framework (demand, supply, and coordination) can guide future research, data collection, and policy development. It explains how learning needs, learning opportunities, and coordination mechanisms interact throughout the life course and how better information can support more effective lifelong learning systems. The chapter highlights the limitations of current data systems, which often overlook important forms of learning and provide only a partial picture of learning experiences and outcomes. It outlines practical actions for governments, researchers, and international organisations to collect and combine data to better understand learning needs, participation, and outcomes across different stages of life.
A Conceptual Foundation for the Development of a Lifelong Learning Measurement Framework
8. Conclusion and recommendations
Copy link to 8. Conclusion and recommendationsAbstract
This report presents a comprehensive conceptual foundation for the development of a measurement framework for LLL, structured around three interconnected pillars – coordination, demand, and supply – designed to systematically address the research aims of the report: scoping learning types across the life course, analysing national differences in LLL, and advancing data collection, reuse, and integration.
The framework builds on a multidimensional data space defined by lifelong and life-wide axes, with learning data points (concrete instances of learning occurrence) as the focal unit of analysis. These points arise from the interaction of demand and supply, mediated by coordination, and generate both proximal outcomes (e.g. skill acquisition) and distal outcomes (e.g. well-being, social mobility). The integration of these pillars in Section 5 underscores that stakeholder objectives, whether economic, social, or personal, shape not only what is valued but also how coordination is enacted.
Sections 6 and 7 shift to operationalisation, distinguishing the roles of data in research (uncovering patterns), policy design (evaluating interventions and informing strategic choices), and coordination (enabling alignment). They propose innovative strategies – enhanced cross-sectional and longitudinal surveys, data integration, and cumulative records – to overcome current limitations: fragmented coverage, static metrics, and underrepresentation of informal and hybrid learning.
The framework’s strength lies in its adaptive, open design, grounded in interdisciplinary theory (Annex A) and responsive to emerging trends: digitalisation, demographic shifts, and platform-mediated learning. It moves beyond labour-market-centric models to encompass health literacy, civic engagement, and intergenerational resilience, aligning with global priorities such as the UN Sustainable Development Goals (SDGs) and the European Pillar of Social Rights.
8.1. Summary of framework
Copy link to 8.1. Summary of framework8.1.1. Bridging conceptual foundations to data focus: methods, tools, and examples
This section translates the framework’s conceptual pillars into an operational, data-driven strategy. It outlines methods, tools, and pilot initiatives that can underpin a coherent and scalable OECD-led data ecosystem. Consistent with Section 7 the strategy emphasises integration of data sources and advanced analytics to address persistent blind spots.
Demand pillar. Updated cross-sectional survey modules (e.g. tailored extensions of the Programme for International Student Assessment (PISA), The Programme for the International Assessment of Adult Competencies (PIAAC) and the Adult Education Survey (AES)) and participatory logging can help identify latent learning needs. Enhanced and standardised scales on motivation and barriers could be embedded into international surveys, while machine-learning methods may generate composite indicators that combine survey data with digital traces (e.g. online search queries, labour platform activity). Pilot projects drawing on anonymised platform analytics (e.g. YouTube engagement) could help anticipate emerging civic learning demands.
Supply pillar. New approaches to mapping LLL are required. Web crawling and metadata analytics can substantially enhance coverage of provision, while updated classifications of learning types are needed to capture hybrid delivery types. National registries, such as Germany’s Information Web on Continuing Education (IWWB, 2025[1]), Finland’s registry of liberal adult education organisations (Manninen and Vuorikoski, 2024[2]), illustrate the potential of granular mapping and provide testbeds for advanced classification methods, including large language model techniques. Complementary innovations, such as mobile applications that track informal learning in real-time, could help fill major evidence gaps.
Coordination pillar. Advanced modelling can improve alignment between supply and demand. Agent-based and Bayesian simulations provide tools to predict mismatches, while big-data linkages (e.g. combining LinkedIn analytics with national administrative records) enable near-real-time monitoring of learner transitions. Internationally, linking PIAAC data with administrative registers could generate predictive models for mobility and skill mismatch.
Cross-cutting innovation. System dynamics modelling (e.g. Vensim) can simulate feedback loops across the LLL ecosystem, while the implementation of ethical AI frameworks (e.g. Fairness 360) ensures safeguards against algorithmic bias. Data linkages across OECD surveys (e.g. PIAAC-PISA) enable more robust tracking of learning outcomes.
Proposed OECD-led actions:
1. Enhance OECD survey programmes. Develop modular extensions based on the recommendations in Section 7 pilot in 5–10 member states and evaluate coherence through structured feedback loops before scaling globally.
2. Harmonise administrative data. Convene expert panels to refine definitions, pilot in volunteer countries, and integrate outputs into GDPR-compliant OECD data lakes.
3. Pilot data integration. Partner with national experts to test AI-driven linkages (e.g. BCS70, ELSA, NEPS, IWWB register, platform analytics), with results documented through comparative case studies.
4. Scale national innovations internationally. Share pilot outcomes within the OECD Working Party on Employment, develop cross-national observatories, and refine predictive analytics and indicators to strengthen systemic comparability.
8.1.2. Key insights from the framework
Our analysis produces four critical insights, derived from the three pillars, which together provide a nuanced understanding of LLL dynamics:
1. Multi-level demand requires flexible provision and pathways
The interplay of needs across multiple levels – from individual aspirations to employer and state priorities – requires adaptable forms of provision. Approaches such as micro-credentials, which integrate formal certification with informal online modules, can help to address mismatches and support transitional requirements (Sections 2.2, 3). Latent demand, often unexpressed among low-skilled groups, underscores the importance of life-wide learning opportunities that extend beyond traditional categories. Strengthening measures of learning needs and demand within large-scale surveys such as PISA, PIAAC and AES, and combining these datasets with big-data sources (e.g. YouTube, Coursera, LinkedIn, Facebook), offers the potential for more dynamic tracking of needs.
2. Institutional Clusters Explain National Variations
Institutional clusters – spanning formal education, employers, welfare policy, and civic systems – together with their coordination capacities, shape national differences in LLL provision. For instance, Norway’s approach integrates welfare and education to ensure equitable access (Bæck, 2025[3]). This contrasts with less comprehensive arrangements in Greece (Ioannidou and Zarifis, 2025[4]) and the Czech Republic (Kalenda, Karger and Vaculíková, 2025[5]), where fragmentation constrains participation (Section 4.3). The concept of “overlaps” between clusters, such as the labour–welfare linkages embedded in Germany’s dual system, highlights how integration can reduce mismatches and provide a basis for comparative analysis, underlining the need for governance designed to mitigate inequities (Eichhorst and Marx, 2011[6]). Sequence analysis, using integrated PIAAC and administrative data, can further model these variations and strengthen governance related insights.
3. Capabilities and Social Capital Underpin Wider Outcomes
Combining direct measures (such as skills assessments) with indirect proxies (such as motivations and outcomes) illustrates how LLL not only strengthens capabilities, including civic engagement and well-being, but also cultivates social capital through both bridging (across groups) and bonding (within groups) ties. These dynamics help to mitigate coordination failures, such as unpriced externalities and information asymmetries, thereby contributing to more resilient ecosystems (Section 2.3). Evidence suggests that capability gains can also enhance intergenerational mobility, as demonstrated in U.S. preschool studies, complementing economic returns with non-market benefits such as reduced social exclusion (Chetty et al., 2018[7]). Instruments such as Europass enable cumulative tracking of outcomes, while AI-enabled analytics reinforce data integration by embedding wider social benefits, including community cohesion (Section 7.2).
4. Data Innovations Address Fragmentation
Current weaknesses in data systems – such as the limited tracking of increasingly hybridised learning forms or context-specific transitions – could be alleviated through sustained research and development aimed at advancing data integration tools, methods, and LLL observatories. Promising directions include AI-adaptive surveys, synthetic cohorts, real-time data from mobile apps, big-data platforms, and blockchain technologies. These approaches have the potential to support predictive analytics and real-time forecasting of emerging trends, including risks such as skill obsolescence.
Blockchain technologies, for instance, offer distinctive applications: encryption and off-chain storage, smart contracts for consent and access control, zero-knowledge proofs for privacy-preserving linkages, tokenisation and anonymisation, and immutable audit trails for compliance (Fatoum et al., 2021[8]). Applied in cross-national observatories, such innovations could substantially enhance data development and integration. Alongside existing standardised datasets and indicators, maintained by international working groups, these advances would help to address current fragmentation and open new possibilities for more sophisticated analysis.
8.2. Suggestions for implementation
Copy link to 8.2. Suggestions for implementationTo operationalise the framework and advance its research aims, the following recommendations offer practical pathways for policymakers, researchers, and educators to strengthen LLL ecosystems. Structured around the framework’s three pillars with an overarching cross-cutting focus, these recommendations refine the proposed action points by grounding them in evidence and aligning them with the data strategies in Section 7 by leveraging multi-source data collection, advanced analytics, and iterative feedback loops, they aim to ensure adaptability and equity across diverse national and cultural contexts.
8.2.1. Demand: enhancing needs assessment
Strengthening the identification of learning needs across micro, meso, and macro levels requires investment in data tools that capture both expressed and latent demands, with a particular focus on underserved groups.
Pilot blended datasets for inclusive integration by developing datasets that combine survey data (e.g. PIAAC, AES) with administrative and big-data sources (e.g. anonymised YouTube, LinkedIn logs), secured through blockchain-enabled linkages, or expand these surveys in longitudinal data-collection projects like PIAAC-L (Rammstedt et al., 2017[9]). Co-designing these datasets with NGOs and community organisations ensures cultural sensitivity, while AI-driven sentiment analysis can surface needs often overlooked, such as civic or health literacy.
Implement integrated data systems by developing interoperable, GDPR-compliant data infrastructures (e.g. data lakes) to integrate survey, administrative, and big-data sources. International bodies such as the OECD should work towards standardising key concepts (e.g. “informal learning engagement”) to enable comparability across contexts. Integrating qualitative narratives (e.g. life-story interviews) with quantitative metrics, such as the Latent Needs Index (Annex C), could provide a more comprehensive picture of aspirations.
8.2.2. Supply: expanding provision mapping
To comprehensively map LLL provisions, stakeholders must update classification frameworks to reflect the ongoing digitisation, flexibilisation, and hybridisation of learning opportunities.
Foster open-source toolkits for collaborative analytics by establishing repositories that host scripts (e.g. Python for capability tracking, R for mismatch modelling) alongside free API access (e.g. Coursera). Such toolkits would quantify informal contributions, which are particularly significant in regions with less developed formal provision of LLL. Revising taxonomies through the Hybridisation Ratio, as outlined in Annex C, could capture emerging modalities, such as micro-credentials.
Scale cumulative records by expanding digital profiles, such as Europass and SkillsFuture, to consolidate individual data on various types of learning. Blockchain technologies can provide secure validation, while AI-driven agents could support personalised transition planning. This strengthens not only agency but also advances longitudinal tracking.
In parallel, AI-enabled metadata analytics, using privacy-preserving synthetic data, enhance the visibility of community-driven learning. This can support comparative analysis of institutional cluster variations and advance data integration for policy alignment, as detailed in Section 7.
8.2.3. Coordination: strengthening systemic alignment
To improve coordination between needs and provisions, stakeholders should align across levels through integrated data systems.
Establish international LLL observatories for cross-national insights by forming consortia that repurpose administrative data into synthetic cohorts. These observatories should also employ new data-collection techniques to capture self-reported informal learning (e.g. mobile application), while also strengthening equity data in underrepresented regions such as the Global South.
Innovate policy labs for dynamic coordination by trialling interventions such as AI-guided micro-credentials through randomised controlled trials. Incorporating simulations to model scenarios such as the impacts of automation and subsidising access for low-skilled or migrant learners can enhance both engagement and outcomes.
Advance Active Labour Market Policies (ALMPs) data development by promoting international negotiations, for example via the OECD’s Working Party on Employment, to define “qualification-linked ALMPs” as ISCED-aligned programmes. Developing indicators such as “ALMP qualification completion rates” would address current shortcomings in data aggregation.
Foster governance coordination by utilising LLL observatories (similar to the OECD Skills Strategy implementations) to facilitate negotiated settlements among stakeholders. Leveraging institutional social capital, as seen in Nordic models, can reduce fragmentation and strengthen alignment across systems.
8.2.4. Cross-cutting: building resilient LLL ecosystems
To build resilient LLL ecosystems, stakeholders must strengthen integrated governance and harness predictive tools.
Advance ethical AI for predictive insights by developing algorithms subject to bias audits (e.g. IBM Fairness 360) to anticipate learning demands from fused datasets (Bellamy et al., 2019[10]). These tools should model cohort outcomes under different policy scenarios, such as subsidies, to mitigate externalities in automation-driven labour markets (Acemoglu and Restrepo, 2019[11]).
Integrating ecosystem indices from Annex C with machine-learning techniques (e.g. Bayesian models, agent-based simulations) and combined datasets ensures that: new taxonomies capture diverse learning forms; comparative insights address institutional variations; and data integration supports adaptive systems.
Finally, resilience can be further enhanced by scaling cumulative records and standardising ALMP data through international negotiations. Open-source dashboards, informed by these datasets, can also capture wider social outcomes such as community cohesion.
8.3. Next steps
Copy link to 8.3. Next stepsTo operationalise these options, we propose three experimental pathways, each engaging stakeholders in an iterative development process.
Large-scale measurement and survey development. Building on international survey programmes such as PIAAC, AES, PISA, and CVTS, this strand aims to enhance existing instruments to create a more coherent global data ecosystem. The next steps are: (1) establishing OECD working groups to test new measurement concepts; (2) piloting approaches in 5–10 countries; (3) collecting structured feedback to assess coherence across contexts; and (4) scaling internationally to strengthen comparability and system optimisation.
Administrative data: definitions and harmonisation. Drawing on initiatives such as the Indicators of Education Systems (INES) including the Labour Market and Social Outcomes (LSO) network and Network for the Collection and Adjudication of System-level Descriptive Information on Educational Structures (NESLI), as well as groups involved with ALMPs, this effort focuses on standardising definitions and enhancing the comparability of administrative data. The main steps include: (1) convening OECD expert panels to refine definitions; (2) running pilot projects with volunteer countries; (3) developing a core set of shared indicators; and (4) integrating outputs into secure, GDPR-compliant “data lakes” that enable cross-country comparison.
Data integration and exploratory collaborations. A new initiative to connect data scientists with education policy and indicators experts to design strategies and indicators for integrated data systems. Work would begin with small-scale pilots that leverage national strengths, such as longitudinal surveys (e.g. the BCS70, ELSA, NEPS) or comprehensive provider registries (e.g. liberal education providers in Finland, IWWB in Germany). These national testbeds can inform international scaling. The planned steps are: (1) leveraging specialised national datasets; (2) piloting AI-driven linkages with large-scale data sources; (3) documenting lessons through comparative case studies; and (4) sharing insights via OECD forums, contributing directly to ecosystem strategies examining options to collect, reuse, or integrate data across the life-course.
Looking ahead, piloting should be prioritised in regions characterised by high institutional diversity. These efforts should be accompanied by continuous evaluation, ensuring that new measurement and integration approaches not only enhance data coherence but also generate actionable insights to improve participation and outcomes across the life course.
References
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