The theme of International Education Day 2025, “AI and education: Preserving human agency in a world of automation,” invites critical examination of how artificial intelligence might enhance rather than replace human capabilities in learning and leadership. Global health education offers a compelling context for exploring this question, as mounting challenges from climate change to persistent inequities demand new approaches to building collective capability.
The promise of connected communities
Recent experiences like the Teach to Reach initiative demonstrate the potential of structured peer learning networks. The platform has connected over 60,000 health workers, primarily government workers from districts and facilities across 82 countries, including those serving in conflict zones, remote rural areas, and urban settlements. For example, their exchanges about climate change impacts on community health point the way toward more distributed forms of knowledge creation in global health.
Analysis of these networks suggests possibilities for integrating artificial intelligence not merely as tools but as active partners in learning and action. However, realizing this potential requires careful attention to how AI capabilities might enhance rather than disrupt the human connections that drive current success.
Artificial Intelligence (AI) partnership could provide crucial support for tackling mounting challenges. More importantly, they could help pioneer new approaches to learning and action that genuinely serve community needs while advancing our understanding of how human and machine intelligence might work together in service of global health.
Understanding Artificial Intelligence (AI) as partner, not tool
The distinction between AI tools and AI partners merits careful examination. Early AI applications in global health primarily automate existing processes – analyzing data, delivering content, or providing recommendations. While valuable, this tool-based approach maintains clear separation between human and machine capabilities.
AI partnership suggests a different relationship, where artificial intelligence participates actively in learning networks alongside human practitioners. This could mean AI systems that:
- Engage in dialogue with health workers about local observations
- Help validate emerging insights through pattern analysis
- Support adaptation of solutions across contexts
- Facilitate connections between practitioners facing similar challenges
The key difference lies in moving from algorithmic recommendations to collaborative intelligence that combines human wisdom with machine capabilities.
A framework for AI partnership in global health
Analysis of current peer learning networks suggests several dimensions where AI partnership could enhance collective capabilities:
- Knowledge creation: Current peer learning networks enable health workers to share observations and experiences across borders. AI partners could enrich this process by engaging in dialogue about patterns and connections, while preserving the central role of human judgment in validating insights.
- Learning process: Teach to Reach demonstrates how structured peer learning accelerates knowledge sharing and adaptation. AI could participate in these networks by contributing additional perspectives, supporting rapid synthesis of experiences, and helping identify promising practices.
- Local leadership: Health workers develop and implement solutions based on deep understanding of community needs. AI partnership could enhance decision-making by exploring options, modeling potential outcomes, and validating approaches while maintaining human agency.
- Network formation: Digital platforms currently enable lateral connections between health workers across regions. AI could actively facilitate network development by identifying valuable connections and supporting knowledge flow across boundaries.
- Implementation support: Peer review and structured feedback drive current learning-to-action cycles. AI partners could engage in ongoing dialogue about implementation challenges while preserving the essential role of human judgment in local contexts.
- Evidence generation: Networks document experiences and outcomes through structured processes. AI collaboration could help develop and test hypotheses about effective practices while maintaining focus on locally-relevant evidence.
Applications across three global health challenges
This framework suggests new possibilities for addressing persistent challenges.
1. Immunization systems
Current global immunization goals face significant obstacles in reaching zero-dose children and strengthening routine services. AI partnership could enhance efforts by:
- Supporting microplanning by mediating dialogue about local barriers
- Facilitating rapid learning about successful engagement strategies
- Enabling coordinated action across health system levels
- Modeling potential impacts of different intervention approaches
2. Neglected Tropical Diseases (NTDs)
The fight against NTDs suffers from critical information gaps and weak coordination at local levels. Many communities, including health workers, lack basic knowledge about these diseases. AI partnership could help address these gaps through:
- Facilitating knowledge flow between affected communities
- Supporting coordination of control efforts
- Enabling rapid validation of successful approaches
- Strengthening surveillance and response networks
3. Climate change and health
Health workers’ observations of climate impacts on community health provide crucial early warning of emerging threats. AI partnership could enhance response capability by:
- Engaging in dialogue about changing disease patterns
- Supporting rapid sharing of adaptation strategies
- Facilitating coordinated action across regions
- Modeling potential impacts of interventions
Pandemic preparedness beyond early warning
The experience of digital health networks during recent disease outbreaks reveals both the power of distributed response capabilities and the potential for enhancement through AI partnership. When COVID-19 emerged, networks of health workers demonstrated remarkable ability to rapidly share insights and adapt practices. For example, the Geneva Learning Foundation’s COVID-19 Peer Hub connected over 6,000 frontline health professionals who collectively generated and implemented recovery strategies at rates seven times faster than isolated efforts.
This networked response capability suggests new possibilities for pandemic preparedness that combines human and machine intelligence. Heightened preparedness could emerge from the interaction between health workers, communities, and AI partners engaged in continuous learning and adaptation.
Current pandemic preparedness emphasizes early detection through formal surveillance. However, health workers in local communities often observe concerning patterns before these register in official systems.
AI partnership could enhance this distributed sensing capability while maintaining its grounding in local realities. Rather than simply analyzing reports, AI systems could engage in ongoing dialogue with health workers about their observations, helping to:
- Explore possible patterns and connections
- Test hypotheses about emerging threats
- Model potential trajectories
- Identify similar experiences across regions
The key lies in combining human judgment about local significance with AI capabilities for pattern recognition across larger scales.
The focus remains on accelerating locally-led learning rather than imposing standardized solutions.
Perhaps most importantly, AI partnership could enhance the collective intelligence that emerges when practitioners work together to implement solutions. Current networks enable health workers to share implementation experiences and adapt strategies to local contexts. Adding AI capabilities could support this through:
- Ongoing dialogue about implementation challenges
- Analysis of patterns in successful adaptation
- Support for rapid testing of modifications
- Facilitation of cross-context learning
Success requires maintaining human agency in implementation while leveraging machine capabilities to strengthen collective problem-solving.
This networked vision of pandemic preparedness, enhanced through AI partnership, represents a fundamental shift from current approaches. Rather than attempting to predict and control outbreaks through centralized systems, it suggests building distributed capabilities for continuous learning and adaptation. The experience of existing health worker networks provides a foundation for this transformation, while artificial intelligence offers new possibilities for strengthening collective response capabilities.
Investment for innovation
Realizing this vision requires strategic investment in:
- Network development: Supporting growth of peer learning platforms that accelerate local action while maintaining focus on human connection.
- AI partnership innovation: Developing systems designed to participate in learning networks while preserving human agency.
- Implementation research: Studying how AI partnership affects collective capabilities and health outcomes.
- Capacity strengthening: Building health worker capabilities to effectively collaborate with AI while maintaining critical judgment.
Looking forward
The transformation of global health learning requires moving beyond both conventional practices of technical assistance and simple automation. Experience with peer learning networks demonstrates what becomes possible when health workers connect to share knowledge and drive change.
Adding artificial intelligence as partners in these networks – rather than replacements for human connection – could enhance collective capabilities to protect community health. However, success requires careful attention to maintaining human agency while leveraging technology to strengthen rather than supplant local leadership.
7 key principles for AI partnership
- Maintain human agency in decision-making
- Support rather than replace local leadership
- Enhance collective intelligence
- Enable rapid learning and adaptation
- Preserve context sensitivity
- Facilitate knowledge flow across boundaries
- Build sustainable learning systems
Listen to an AI-generated podcast about this article
🤖 This podcast was generated by AI, discussing Reda Sadki’s 24 January 2025 article “A global health framework for Artificial Intelligence as co-worker to support networked learning and local action”. While the conversation is AI-generated, the framework and examples discussed are based on the published article.
Framework: AI partnership for learning and local action in global health
| Dimension | Current State | AI as Tools | AI as Partners | Potential Impact |
| Knowledge creation | Health workers share observations and experiences through peer networks | AI analyzes patterns in shared data | AI engages in dialogue with health workers, asking questions, suggesting connections, validating insights | New forms of collective intelligence combining human and machine capabilities |
| Learning process | Structured peer learning through digital platforms and networks | AI delivers content and analyzes performance | AI participates in peer learning networks, contributes insights, supports adaptation | Accelerated learning through human-AI collaboration |
| Local leadership | Health workers develop and implement solutions for community challenges | AI provides recommendations based on data analysis | AI works alongside local leaders to explore options, model scenarios, validate approaches | Enhanced decision-making combining local wisdom with AI capabilities |
| Network formation | Lateral connections between health workers across regions | AI matches similar profiles or challenges | AI actively facilitates network development, identifies valuable connections | More effective knowledge networks leveraging both human and machine intelligence |
| Implementation support | Peer review and structured feedback on action plans | AI checks plans against best practices | AI engages in iterative dialogue about implementation challenges and solutions | Improved implementation through combined human-AI problem-solving |
| Evidence generation | Documentation of experiences and outcomes through structured processes | AI analyzes implementation data | AI collaborates with health workers to develop and test hypotheses about what works | New approaches to generating practice-based evidence |
Image: The Geneva Learning Foundation Collection © 2024
