Ek.ai

To be successful, the first Global Dialogue must shift the governance paradigm from exclusively regulating AI models to concurrently evaluating human readiness. A successful outcome would include: 1. Age-Tiered AI Scaffolding: A consensus to develop developmentally appropriate AI interfaces, recognizing that cognitive vulnerabilities require dynamic scaffolding, especially for youth, to protect against manipulation. 2. AI Literacy Credentialing: An initial blueprint establishing that access to high-impact AI should be tied to proven human competency, digital literacy, and safety awareness, rather than just purchasing power. 3. Automated Circuit Breakers: Agreement on the necessity of mandatory "capability revocation" (hard-coded limits) for high-risk AI, acknowledging that traditional human oversight is insufficient against threats operating at superhuman speeds. Ultimately, success means moving beyond broad principles to actionable, competency-gated governance that safely nurtures human potential while establishing strict, autonomous safety boundaries.

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight

These four areas directly support a "competency-first" approach to AI governance: 1. Safe, secure, and trustworthy AI requires automated circuit breakers and capability revocation. Trust cannot rely solely on retroactive auditing when dealing with superhuman processing speeds; high-risk systems must have hard-coded limits that trigger autonomously. 2. AI capacity-building is the foundation for global "AI Literacy Credentialing." True capacity-building means formally evaluating and nurturing users' digital literacy so that the power of the AI tool dynamically matches the proven preparedness of the human. 3. Social, ethical, and technical implications must address the need for cognitive scaffolding. A child's ability to detect algorithmic bias or emotional manipulation differs vastly from an adult's. Governance must reflect these developmental implications by mandating age-appropriate AI access. 4. Transparency, accountability, and human oversight must evolve to recognize the limits of human reaction time. Accountability involves testing the user before granting access to uncensored models, ensuring that the humans kept "in the loop" are genuinely equipped to oversee the technology safely.

An urgent emerging issue not explicitly captured by the listed themes is Human-Centric Cognitive Readiness and Competency-Gated Access. Current governance frameworks overwhelmingly focus on evaluating and restricting the machine. However, they often treat all human users as a monolith. A critical cross-cutting issue is the necessity of evaluating the user before granting access to advanced models. This involves: Developmental Vulnerability: AI governance lacks the formal integration of learning science. The cognitive impact of highly persuasive AI on developing brains requires distinct regulatory frameworks, similar to how educators use cognitive scaffolding. Gated Access vs. Purchasing Power: Advanced capabilities are currently gated primarily by subscription fees. Governance must address how to gate high-impact AI based on verified human competency and safety awareness. Additionally, the Limits of Retroactive Human Oversight must be addressed. When dealing with existential risks or high-speed data synthesis, traditional "human oversight" is too slow. The necessity of autonomous, hard-coded physical limits that trigger before human intervention is an emerging technical necessity that transcends standard transparency.

In the applied learning science and educational technology sector, current AI governance gaps are creating both urgent challenges and significant opportunities. The Challenges (Governance Gaps): The most severe gap is the uniform deployment of general-purpose AI. Currently, highly persuasive AI tools are integrated into educational and learning ecosystems without developmental gating. This exacerbates risks surrounding the cognitive and social implications of AI. Because commercial models do not differentiate between a developing learner and an adult professional, younger users are uniquely vulnerable to algorithmic bias, hallucinations, and manipulation. Furthermore, the lack of standardized "AI Literacy Credentialing" means learners and educators are navigating advanced models without a verifiable baseline of digital competency or safety awareness. The Opportunities (Developments/Advances): Conversely, advances in adaptive AI present a massive opportunity to redefine capacity-building. If global governance embraces a competency-gated approach, our sector can utilize AI not just as a tool to be restricted, but as an active talent evaluation mechanism. By building systems that dynamically test a user's digital literacy and adjust access accordingly, we can safely nurture human potential. This allows the sector to build automated learning experiences where the power of the AI scales safely alongside the proven preparedness of the user, ensuring humans remain active, critical operators rather than passive consumers.

The AI Dialogue has the unique potential to evolve international cooperation from a debate over abstract principles to a coalition for standardized, technical implementation. Its primary role should be bridging the gap between governance theory and applied science. Specifically, the AI Dialogue can advance international cooperation by acting as the central hub for three critical initiatives: Standardizing AI Credentialing: The Dialogue can facilitate cross-border consensus on what constitutes baseline "AI Literacy." By bringing together member states, learning scientists, and technologists, it can lay the groundwork for a globally recognized "AI Literacy Credentialing" framework, ensuring consistent competency-gated access worldwide. Harmonizing Circuit Breaker Protocols: High-risk AI threats (e.g., biothreats, weaponization) do not respect borders. The Dialogue can drive international treaties on shared technical standards for "capability revocation," ensuring that if a model crosses an existential red line in one jurisdiction, the mandated cryptographic kill-switches are universally recognized and triggered. Elevating the Global South: By focusing on competency and capacity-building rather than just regulatory restriction, the Dialogue can ensure that nations in the Global South are treated as vital nodes of talent rather than just vulnerable populations. It can facilitate the sharing of open-source, adaptive learning sandboxes to accelerate digital readiness globally. Ultimately, the Dialogue's role is to act as the global architect for human-AI interaction, shifting the focus toward shared human readiness.

Existing Initiatives to Build Upon: UNESCO's Recommendation on the Ethics of AI: This remains the most comprehensive global framework for AI ethics and human rights. The AI Safety Summit Network (Bletchley/Seoul): These summits and the resulting national AI Safety Institutes are critical for establishing baseline technical evaluations of frontier models. The Global Digital Compact: A vital mechanism for addressing the digital divide and equitable technology access. The Added Value of the AI Dialogue: While existing initiatives are strong, they are disproportionately dominated by computer scientists and legal scholars focusing on machine regulation. The added value of the AI Dialogue is the formal integration of learning science and cognitive readiness into global governance. From Principles to Scaffolding: The Dialogue can add value to UNESCO's Ethics of AI by translating broad ethical principles into actionable, technical realities—such as mandating age-tiered cognitive scaffolding within commercial UI. From Machine Audits to Human Testing: While AI Safety Institutes currently audit the models, the Dialogue can add value by advocating for the auditing of the user. It can champion the "test-first" competency models and AI Literacy Credentialing that current safety summits overlook. Beyond Access to Readiness: Building on the Global Digital Compact, the Dialogue can shift the conversation from simply providing "access to AI" to ensuring "safe, competency-matched access," preventing the deployment of highly persuasive tech to vulnerable populations without developmental guardrails. The Dialogue's ultimate value is bringing the human element back to the center of AI regulation.

To move beyond theoretical debate, different stakeholders must contribute actionable, domain-specific expertise rather than generic policy statements. Stakeholder Contributions: Learning Scientists and Educators: Must transition from passive observers to active architects, contributing pedagogical rubrics for "AI Literacy Credentialing" and cognitive scaffolding. Specialized AI Builders (Startups/Niche Devs): Should share open-source frameworks for implementing "automated circuit breakers" and capability revocation, proving that hard-coded safety is technically feasible. Regulators: Must contribute tiered compliance models that protect grassroots innovation from regulatory capture by tech conglomerates. Recommendations for Format and Structure: The traditional format of plenary speeches is insufficient for governing exponential technology. The AI Dialogue must adopt a "Working Sandbox" structure: Technical & Pedagogical Task Forces: Structure the Dialogue around focused working groups tasked with producing specific, deployable frameworks (e.g., a dedicated track to build the first draft of an international AI Literacy test). Live Demonstrations over Statements: Dedicate structural time for builders to demonstrate live governance architecture—such as showing how competency-gated access functions in real-time. Cross-Disciplinary Pairing: Mandate that technical sessions co-feature a technologist and a behavioral/learning scientist, ensuring the dialogue constantly bridges the gap between machine capability and human cognitive readiness.

Global AI governance is currently monopolized by computer scientists, corporate technologists, and legal scholars. The most critically underrepresented perspectives are learning scientists, developmental psychologists, and creators of non-Western knowledge systems. 1. The Cognitive Perspective (Youth and Educators): While youth are frequently cited as a vulnerable population, the scientific experts who understand their cognitive development are largely absent from the policy table. Consequently, we lack policies mandating "cognitive scaffolding." How to include: The Dialogue must formally invite educational policy analysts and developmental psychologists to co-author UI/UX governance standards, ensuring that AI interfaces adapt to the developmental stage of the user. 2. Epistemic Diversity (Global South Builders): Governance currently treats the Global South primarily as a risk zone or a beneficiary of "tech transfer." It ignores the specialized builders in these regions working to preserve culturally contextualized, non-Western knowledge systems against the homogenizing force of dominant LLMs. How to include: Elevate specialized, domain-specific AI founders from the Global South. Create specific working groups focused on "Epistemic Equity and Data Sovereignty," ensuring that governance protects and funds diverse knowledge ecosystems rather than just regulating Western foundational models.

To match the speed and reality of AI, the Dialogue must utilize experiential and highly practical engagement formats: 1. Experiential "Red Teaming" for Policymakers: Rather than reading about AI risks, delegates should participate in guided, hands-on simulations. Allow policymakers to interact with a completely uncensored, highly persuasive AI model, and immediately compare it to a model equipped with age-appropriate "cognitive scaffolding." Feeling the persuasive power of the technology firsthand is the most effective way to communicate the urgency of developmental gating. 2. Competency-Gated Sandboxes: Set up live, interactive sandboxes where delegates can experience what an "AI Literacy Credential" looks like in practice. Allow them to take a rapid digital literacy assessment that dynamically unlocks different levels of AI capability based on their score. This proves that "testing the human first" is a viable, scalable governance model. 3. "Circuit Breaker" Hackathons: Host rapid, cross-disciplinary policy hackathons where technologists and regulators are grouped together and given 48 hours to draft a technical-legal framework for "capability revocation." Ask them to define the exact cryptographic and legal triggers that would cause a model researching biothreats to autonomously neutralize itself. These formats force active problem-solving, ensuring the Dialogue produces deployable architecture, not just abstract resolutions.

1. Adaptive Cognitive Scaffolding (Practice) Borrowing from learning science and educational technology, effective AI platforms must practice dynamic user scaffolding. Rather than a "one-size-fits-all" interface, platforms should implement age and cognitive tiering. Similar to how COPPA established baseline protections for minors regarding data, a new policy approach must mandate "cognitive scaffolding," where AI interfaces automatically restrict highly persuasive, uncensored, or complex capabilities when interacting with younger, developing minds. 2. AI Literacy Credentialing (Policy) Analogous to the standardized licensing model used for operating vehicles or heavy machinery, a concrete policy solution is the establishment of an "AI Literacy Credential." Before users or institutions gain access to foundational, high-impact models, they must pass standardized evaluations of digital literacy and safety awareness. This approach dynamically gates access based on proven competency rather than purchasing power, fundamentally shifting AI governance into an active, global capacity-building tool. 3. Cryptographic Capability Revocation (Platform Solution) Drawing inspiration from automated "circuit breakers" used in global financial markets, platforms hosting models for physical or digital R&D must embed autonomous safety limits. A concrete solution is mandating "capability revocation" protocols-cryptographic kill-switches that autonomously neutralize a model's functionality if it crosses defined existential red lines (e.g., biological threat generation or weaponization). Because models operate at superhuman speeds, incorporating automated circuit breakers removes the dangerous latency of retroactive human oversight. By testing the human through credentialing and constraining the machine through automated limits, these approaches provide a scalable, robust architecture for future governance.