SolveCC

Success, from an education and future generation perspective, means the Global Dialogue produces outcomes that the people most affected by long-term AI governance decisions can actually recognize as theirs. That means learners of all ages, young people across formal and informal contexts, and communities without current institutional standing. A few things would mark genuine success. First, future generations recognized as governance stakeholders, not only as beneficiaries. Learners and young people are consistently framed as recipients of AI's consequences: future workers, future citizens, future users. They are rarely engaged as participants in designing the governance frameworks that will shape those consequences. A successful Dialogue establishes structural mechanisms for intergenerational participation in AI governance architecture, not only youth-facing AI literacy programs. Second, education recognized as governance infrastructure. AI literacy, ethics education, and governance readiness programs are consistently underfunded relative to technical AI development. A successful Dialogue produces at least one concrete commitment that treats education as a governance investment across all learning contexts, formal and informal, from early childhood through adult learning. Third, practitioner evidence from the field taken seriously. Organizations working directly with learners and communities on AI governance challenges have accumulated practical knowledge about what readiness actually requires. A successful Dialogue creates channels for this knowledge to inform policy, not only for policy to cascade down to programs.

• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

These four priorities reflect what we observe directly in our work with learners and communities engaging real AI governance challenges across different educational contexts and life stages. AI capacity-building is the most immediate gap, but our experience is that capacity-building programs consistently underestimate what is required. Technical skills are necessary but insufficient. Learners of all ages need governance literacy: the ability to identify who is accountable for an AI decision, how to assess its impact on a community, and how to participate in the processes that shape those decisions. Most current capacity-building frameworks do not include this dimension. The social, economic, ethical, cultural, linguistic and technical implications of AI are inseparable in practice. When learners engage AI challenges affecting their communities, the technical and social dimensions are always entangled. Education that integrates these dimensions from the outset is more effective and more honest about how AI actually works in the world. Protection and promotion of human rights is selected because the communities we work with are frequently those most exposed to AI's consequential effects in hiring, public services, and content moderation, and least represented in the governance discussions that determine how those systems operate. Transparency and human oversight matters because meaningful oversight requires education. Citizens, judges, public servants, and future policymakers cannot exercise oversight of systems they do not understand. Oversight capacity is an educational outcome before it is a regulatory one, and it must be cultivated across the full learning lifecycle.

Two cross-cutting issues emerge consistently from our work that are not adequately captured by the current thematic structure. The first is the governance readiness of future generations as a distinct policy category. Current frameworks treat young people and learners primarily as future workers. The focus is on AI skills for employment. But the learners in programs like ours will spend the majority of their lives making decisions about AI: as engineers, policymakers, community leaders, parents, and citizens. Their readiness to govern AI, not only to use or build it, is a governance question that belongs in this Dialogue, not only in education ministries. No current thematic cluster addresses this directly. The second is the recognition gap for informal and experiential AI learning. A significant and growing portion of the global population is acquiring deep practical AI knowledge through direct engagement with AI systems, as users, as data contributors, as community members navigating AI-mediated services, and as workers in AI-adjacent roles. This learning is real, consequential, and almost entirely unrecognized. It creates no credentials, no participation rights in governance processes, and no pathways into the institutions that shape AI policy. Addressing this gap through recognition frameworks and participation mechanisms would simultaneously advance education equity, governance legitimacy, and inclusion across all four thematic clusters as currently framed.

The governance gaps we observe most directly are at the community and learning ecosystem level, where the distance between AI policy and AI reality is widest. At the community level, people of all ages are navigating AI systems in educational assessments, public services, and social platforms without the knowledge, vocabulary, or institutional standing to question, challenge, or shape how those systems affect them. The governance frameworks that nominally protect them are invisible in their daily experience. This is not a failure of intent. It is a failure of translation between policy and practice, between rights on paper and rights in operation. At the institutional level, learning environments from primary schools to adult education centers are themselves becoming sites of significant AI deployment, often without adequate governance frameworks for their own use of AI in assessment, administration, and instruction. The institutions responsible for developing governance-ready citizens are frequently ungoverned in their own AI practices. At the sector level, the workforce development dimensions of AI governance tend to be reactive, preparing workers for disruption rather than generative, preparing people to shape how AI transforms work and society. Reactive framing produces compliance. Generative framing produces agency. The difference is significant and it begins in how we design learning experiences across the full educational lifecycle. The opportunity is that learning environments, when properly resourced and connected to governance processes, are among the most effective sites for building the civic AI literacy that governance frameworks require to function.

The AI Dialogue can play a specific role that no existing education or governance process currently fills: establishing future generation readiness as a measurable, accountable dimension of international AI governance, not a peripheral concern but a core indicator of whether governance is actually working. Most existing international AI governance processes have no mechanism for assessing whether people across all life stages and learning contexts are being prepared to govern AI. The Dialogue can change this in three ways. First, by establishing a Future Generation and Learner Readiness indicator as part of how international AI governance progress is assessed. This would track not only AI literacy rates but governance literacy: whether learners understand AI's implications for rights, accountability, and democratic participation across formal and informal contexts. Second, by creating a formal education and future generation stakeholder track within the Dialogue's ongoing structure. Not a side forum, but an integrated track with genuine agenda-setting authority over how learning and readiness dimensions are addressed across all four thematic clusters. Third, by connecting the Dialogue's outputs to learning institutions and community educators directly. Governance commitments made in Geneva should have explicit pathways into curricula, community programs, teacher training, and institutional AI governance frameworks at every level of the education system. These contributions are distinct from what UNESCO, national education ministries, or workforce development bodies can deliver alone. The Dialogue's universality and political authority can create the mandate that makes these connections structural rather than voluntary.

Several existing initiatives provide foundations the Dialogue should build upon rather than duplicate. UNESCO's Recommendation on the Ethics of AI and its AI Competency Frameworks for students and teachers represent the most developed international reference points for AI education governance. The Dialogue should treat these as the baseline, assessing implementation gaps and identifying what mechanisms would accelerate adoption across diverse national and community contexts. SolveCC's challenge-based learning model engages learners with real AI governance problems in their communities across different educational contexts and life stages. This practitioner evidence of what governance-ready learning looks like in practice should inform the Dialogue's capacity-building recommendations directly. The OECD's work on AI skills and the future of work, and ITU's AI for Good educational programming, provide complementary evidence bases that the Dialogue should synthesize rather than parallel. The Future Ready Initiative's work on democratic participation in AI development, connecting community engagement with governance process design, offers a model for how learning programs can feed into governance structures rather than operating alongside them. The added value the Dialogue brings is the authority to convert these scattered initiatives into a coherent international framework with accountability mechanisms that hold across jurisdictions and learning contexts.

From an education and future generation perspective, the current stakeholder architecture of AI governance processes has a structural gap: learners and young people are present as subjects of discussion but absent as participants in its design. Learning communities of all kinds, from formal institutions to community education programs to informal learning networks, should be recognized as a distinct stakeholder category with standing in the Dialogue equivalent to that of civil society organizations. They are among the primary sites where governance capacity is built or not, and they are currently invisible in multilateral AI governance architecture. Learners and young people should be integrated into substantive sessions as contributors, not observers. This requires prior engagement: structured consultations with learning communities before Geneva that surface real governance questions from people navigating AI systems in daily life, and that channel those questions into the Dialogue's agenda. The Dialogue should pilot at least one session format that directly involves learners in governance problem-solving, not as panelists sharing perspectives, but as active participants in developing specific recommendations. Challenge-based formats, where mixed groups of learners, practitioners, and policymakers work on defined governance problems, produce more actionable outputs than traditional panel discussions. Formats that create genuine authorship, not just voice, are what distinguish meaningful intergenerational engagement from symbolic inclusion. The goal is not to consult the next generation about decisions already made. It is to include them in making those decisions.

Three communities are consistently underrepresented, and their absence reflects structural choices rather than logistical limitations. Learners and young people outside elite institutions and high-income countries represent the most significant gap. The voices that do participate in AI governance forums tend to come from well-resourced settings and are already fluent in the language of policy. The learners most affected by AI's consequential effects, those navigating AI in public services, in informal labor markets, in under-resourced educational systems, across all age groups, are almost never present. Inclusion requires investment in participation pathways: supported engagement, translated materials, and formats that do not require prior familiarity with UN process. Educators and community learning practitioners working at the grassroots level have direct knowledge of what AI governance looks like in practice and what it does not look like. They are almost never present in multilateral governance discussions. Their inclusion would ground the Dialogue in lived experience and strengthen the connection between Geneva commitments and community reality. Learners from non-anglophone and non-Western educational traditions bring governance frameworks, epistemologies, and community values that are largely absent from current AI ethics discourse. The homogeneity of the conceptual frameworks being used to govern AI is itself a governance problem. Addressing it requires active recruitment of diverse learning traditions into the dialogue, not only translation of existing frameworks into other languages. A practical starting point is the establishment of regional learning community consultations as a formal part of the Dialogue's preparatory process, with supported participation and real agenda influence.

The formats that work best, based on direct experience with learners and communities engaging AI governance questions, are those that create genuine stakes where participants are solving real problems rather than discussing hypothetical ones. Challenge-based governance sprints work across learner contexts and age groups. Presenting mixed groups of learners, practitioners, community members, and policymakers with a specific, bounded AI governance problem and asking them to develop a concrete recommendation within a defined time produces outcomes that panel discussions do not. Participants develop ownership of the problem. Recommendations are grounded in specifics. The process itself builds the collaborative governance capacity that international AI governance requires. Intergenerational working formats, pairing senior policymakers or governance experts with learners or young practitioners on specific issues, create mutual learning that neither participant achieves alone. The senior participant gains ground-level perspective. The younger participant gains governance process knowledge. The outputs are stronger for both. Pre-dialogue community consultations, conducted with learners and community members before the Geneva sessions, can surface governance questions that never appear in traditional preparatory processes because they come from people navigating AI systems in daily life rather than people who study AI governance professionally. These questions, brought into the Dialogue as formal agenda items, would change the substance of the discussion. Structured follow-up mechanisms, where learning community representatives track implementation of Dialogue commitments over the following year and report back at the next session, would create continuity and accountability that single-event formats cannot achieve.

Several approaches offer concrete and replicable lessons for the Dialogue's education and future generation agenda. SolveCC's challenge-based learning model engages learners with real AI governance problems affecting their communities across different educational contexts and life stages. Participants develop governance literacy not through content delivery but through structured problem-solving with real stakes and genuine community connection. The model is adaptable across institutional and non-institutional learning contexts, across age groups, and across cultural settings. It produces both learner development and community-relevant governance recommendations that reflect the lived experience of people navigating AI systems in practice. UNESCO's AI Competency Frameworks provide a baseline for what AI governance-ready education looks like at curriculum level. Their limitation is that they remain aspirational in most national systems, adopted in principle but not yet reflected in teacher training, institutional practice, or assessment across the full learning lifecycle. The gap between framework adoption and implementation is where the Dialogue can add specific value through accountability mechanisms. Finland's national AI literacy program, Elements of AI, offered freely and translated into multiple languages, demonstrates that broad-based AI education can be delivered at scale across diverse learner populations. Extending this model to include AI rights, accountability, and civic participation dimensions would produce a more complete governance-readiness approach. The Future Ready Initiative's work on democratic participation in AI development, connecting community engagement with governance process design, offers a model for how learning programs can feed directly into governance structures. It demonstrates that education and governance are not separate tracks but mutually reinforcing investments when designed together from the outset.