#learningculture — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #learningculture, aggregated by home.social.
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Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis
I know and appreciate Joseph, a Kenyan health leader from Murang’a County, for years of diligent leadership and contributions as a Scholar of The Geneva Learning Foundation (TGLF). Recently, he began submitting AI-generated responses to Teach to Reach Questions that were meant to elicit narratives grounded in his personal experience.
Seemingly unrelated to this, OpenAI just announced plans for specialized AI agents—autonomous systems designed to perform complex cognitive tasks—with pricing ranging from $2,000 monthly for a “high-income knowledge worker” equivalent to $20,000 monthly for “PhD-level” research capabilities.
This is happening at a time when traditional funding structures in global health, development, and humanitarian response face unprecedented volatility.
These developments intersect around fundamental questions of knowledge economics, authenticity, and power in global health contexts.
I want to explore three questions:
- What happens when health professionals in resource-constrained settings experiment with AI technologies within accountability systems that often penalize innovation?
- How might systems claiming to replicate human knowledge work transform the economics and ethics of knowledge production?
- And how should we navigate the tensions between technological adoption and authentic knowledge creation?
Artificial intelligence within punitive accountability structures of global health
For years, Joseph had shared thoughtful, context-rich contributions based on his direct experiences. All of a sudden, he was submitting generic mush with all the trappings of bad generative AI content.
Should we interpret this as disengagement from peer learning?
Given his history of diligence and commitment, I could not dismiss his exploration of AI tools as diminished engagement. Instead, I understood it as an attempt to incorporate new capabilities into his professional repertoire. This was confirmed when I got to chat with him on a WhatsApp call.
Our current Teach to Reach Questions system has not yet incorporated the use of AI. Our “old” system did not provide any way for Joseph to communicate what he was exploring.
Hence, the quality limitations in AI-generated narratives highlight not ethical failings but a developmental process requiring support rather than judgment.
But what does this look like when situated within global health accountability structures?
Health workers frequently operate within highly punitive systems where performance evaluation directly impacts funding decisions. International donors maintain extensive surveillance of program implementation, creating environments where experimentation carries significant risk. When knowledge sharing becomes entangled with performance evaluation, the incentives for transparency about AI “co-working” (i.e., collaboration between human and AI in work) diminish dramatically.
Seen through this lens, the question becomes not whether to prohibit AI-generated contributions but how to create environments where practitioners can explore technological capabilities without fear that disclosure will lead to automatic devaluation of their knowledge, regardless of its substantive quality. This heavily depends on the learning culture, which remains largely ignored or dismissed in global health.
The transparency paradox: disclosure and devaluation of artificial intelligence in global health
This case illustrates what might be called the “transparency paradox”—when disclosure or recognition of AI contribution triggers automatic devaluation regardless of substantive quality. Current attitudes create a problematic binary: acknowledge AI assistance and have contributions dismissed regardless of quality, or withhold disclosure and risk accusations of misrepresentation or worse.
This paradox creates perverse incentives against transparency, particularly in contexts where knowledge production undergoes intensive evaluation linked to resource allocation. The global health sector’s evaluation systems often emphasize compliance over innovation, creating additional barriers to technological experimentation. When every submission potentially affects funding decisions, incentives for technological experimentation become entangled with accountability pressures.
This dynamic particularly affects practitioners in Global South contexts, who face more intense scrutiny while having less institutional protection for experimentation. The punitive nature of global health accountability systems deserves particular emphasis. Health workers operate within hierarchical structures where performance is consistently monitored by both national governments and international donors. Surveillance extends from quantitative indicators to qualitative assessments of knowledge and practice.
In environments where funding depends on demonstrating certain types of knowledge or outcomes, the incentive to leverage artificial intelligence in global health may conflict with values of authenticity and transparency. This surveillance culture creates uniquely challenging conditions for technological experimentation. When performance evaluation drives resource allocation decisions, health workers face considerable risk in acknowledging technological assistance—even as they face pressure to incorporate emerging technologies into their practice.
The economics of knowledge in global health contexts
OpenAI’s announced “agents” represent a substantial evolution beyond simple chatbots or language models. If they are able to deliver what they just announced, these specialized systems would autonomously perform complex tasks simulating the cognitive work of highly-skilled professionals. The most expensive tier, priced at $20,000 monthly, purportedly offers “PhD-level” research capabilities, working continuously without the limitations of human scheduling or attention.
These claims, while unproven, suggest a potential future where knowledge work economics fundamentally change. For global health organizations operating in Geneva, where even a basic intern position for a recent master’s degree graduate cost more than 200 times that of a ChatGPT subscription, the economic proposition of systems working 24/7 for potentially comparable costs merits careful examination.
However, the global health sector has historically operated with significant labor stratification, where personnel in Global North institutions command substantially higher compensation than those working in Global South contexts. Local health workers often provide critical knowledge at compensation rates far below those of international consultants or staff at Northern institutions. This creates a different economic equation than suggested by Geneva-based comparisons. Many organizations have long relied on substantially lower local labor costs, often justified through capacity-building narratives that mask underlying power asymmetries.
Given this history, the risk that artificial intelligence in global health would replace local knowledge workers might initially appear questionable. Furthermore, the sector has demonstrated considerable resistance to technological adoption, particularly when it might disrupt established operational patterns. However, this analysis overlooks how economic pressures interact with technological change during periods of significant disruption.
The recent decisions of many government to donors to suddenly and drastically cut funding and shut down programs illustrates how rapidly even established funding structures can collapse. In such environments, organizations face existential questions about maintaining operational capacity, potentially creating conditions where technological substitution becomes more attractive despite institutional resistance.
A new AI divide
ChatGPT and other generative AI tools were initially “geo-locked”, making them more difficult to access from outside Europe and North America.
Now, the stratified pricing structure of OpenAI’s announced agents raises profound equity concerns. With the most sophisticated capabilities reserved for those able to pay high costs for the most capable agents, we face the potential emergence of an “AI divide” that threatens to reinforce existing knowledge power imbalances.
This divide presents particular challenges for global health organizations working across diverse contexts. If advanced AI capabilities remain the exclusive province of Northern institutions while Southern partners operate with limited or no AI augmentation, how might this affect knowledge dynamics already characterized by significant inequities?
The AI divide extends beyond simple access to include quality differentials in available systems. Even as simple AI tools become widely available, sophisticated capabilities that genuinely enhance knowledge work may remain concentrated within well-resourced institutions. This could lead to a scenario where practitioners in resource-constrained settings use rudimentary AI tools that produce low-quality outputs, further reinforcing perceptions of capability gaps between North and South.
Confronting power dynamics in AI integration
Traditional knowledge systems in global health position expertise in academic and institutional centers, with information flowing outward to practitioners who implement standardized solutions. This existing structure reflects and reinforces global power imbalances.
The integration of AI within these systems could either exacerbate these inequities—by further concentrating knowledge production capabilities within well-resourced institutions—or potentially disrupt them by enabling more distributed knowledge creation processes.
Joseph’s journey demonstrates this tension. His adoption of AI tools might be viewed as an attempt to access capabilities otherwise reserved for those with greater institutional resources. The question becomes not whether to allow such adoption, but how to ensure it serves genuine knowledge democratization rather than simply producing more sophisticated simulations of participation.
These emerging dynamics require us to fundamentally rethink how knowledge is valued, created, and shared within global health networks. The transparency paradox, economic pressures, and emerging AI divide suggest that technological integration will not occur within neutral space but rather within contexts already characterized by significant power asymmetries.
Developing effective responses requires moving beyond simple prescriptions about AI adoption toward deeper analysis of how these technologies interact with existing power structures—and how they might be intentionally directed toward either reinforcing or transforming these structures.
My framework for Artificial Intelligence as co-worker to support networked learning and local action is intended to contribute to such efforts.
Illustration: The Geneva Learning Foundation Collection © 2025
References
Frehywot, S., Vovides, Y., 2024. Contextualizing algorithmic literacy framework for global health workforce education. AIH 0, 4903. https://doi.org/10.36922/aih.4903
Hazarika, I., 2020. Artificial intelligence: opportunities and implications for the health workforce. International Health 12, 241–245. https://doi.org/10.1093/inthealth/ihaa007
John, A., Newton-Lewis, T., Srinivasan, S., 2019. Means, Motives and Opportunity: determinants of community health worker performance. BMJ Glob Health 4, e001790. https://doi.org/10.1136/bmjgh-2019-001790
Newton-Lewis, T., Munar, W., Chanturidze, T., 2021. Performance management in complex adaptive systems: a conceptual framework for health systems. BMJ Glob Health 6, e005582. https://doi.org/10.1136/bmjgh-2021-005582
Newton-Lewis, T., Nanda, P., 2021. Problematic problem diagnostics: why digital health interventions for community health workers do not always achieve their desired impact. BMJ Glob Health 6, e005942. https://doi.org/10.1136/bmjgh-2021-005942
Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health (OECD Artificial Intelligence Papers No. 28), 2024. , OECD Artificial Intelligence Papers. https://doi.org/10.1787/9a31d8af-en
Sadki, R. (2025). A global health framework for Artificial Intelligence as co-worker to support networked learning and local action. Reda Sadki. https://doi.org/10.59350/gr56c-cdd51
#accountability #accountabilityOverloads #ArtificialIntelligence #compliance #conservatism #globalHealth #healthWorkers #HRH #incentives #innovation #learningCulture #performanceMonitoring #TeachToReach
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Investing in learning culture fuels personal growth and gives companies strategic agility. Are we ready to adapt in fast-changing markets?
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Investing in learning culture fuels personal growth and gives companies strategic agility. Are we ready to adapt in fast-changing markets?
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Learning culture: the missing link in global health between learning and performance
Learning culture is a critical concept missing from health systems research.
It provides a practical and actionable framework to operationalize the notion of ‘learning health systems’ and drive transformative change.
Read this first: What is double-loop learning in global health?
Watkins and Marsick describe learning culture as the capacity for change. They identify seven key action imperatives or “essential building blocks” that strengthen it: continuous learning opportunities, inquiry and dialogue, collaboration and team learning, systems to capture and share learning, people empowerment, connection to the environment, and strategic leadership for learning (Watkins & O’Neil, 2013).
Crucially, the instrument developed by Watkins and Marsick assesses learning culture by examining perceptions of norms and practices, not just individual behaviors (Watkins & O’Neil, 2013).
This aligns with Seye Abimbola’s assertion that learning in health systems should be “people-centred” and occurs at multiple interconnected levels.
Furthermore, this research demonstrates that certain dimensions of learning culture, like strategic leadership and systems to capture and share knowledge, are key mediators and drivers of performance outcomes (Yang et al., 2004).
This provides compelling evidence that investments in learning can yield tangible improvements in health delivery and population health.
Learn more: Jones, I., Watkins, K. E., Sadki, R., Brooks, A., Gasse, F., Yagnik, A., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., & Kovanovic, V. (2022). IA2030 Case Study 7. Motivation, learning culture and programme performance (1.0). The Geneva Learning Foundation. https://doi.org/10.5281/zenodo.7004304
As Watkins and Marsick (1996) argue, to develop a strong learning culture, we need to “embed a learning infrastructure”, “cultivate a learning habit in people and the culture”, and “regularly audit the knowledge capital” in our organization or across a network of partners.
While investments in learning can be a challenging sell in resource-constrained global health settings, this evidence establishes that learning culture is in fact an indispensable driver of health system effectiveness, not just a “nice to have” attribute.
Subsequent studies have also linked learning culture to key performance indicators like care quality, patient satisfaction, and innovation.
Why lack of continuous learning is the Achilles heel of immunization
To advance learning health systems, it is important to translate this research in terms that resonate with the worldview of global health practitioners like epidemiologists and to produce further empirical studies that speak to their evidentiary standards.
Ultimately, this will require expanding mental models about what constitutes legitimate and actionable knowledge for health improvement.
The learning culture framework offers an evidence-based approach to guide this transformation.
References
Abimbola, S. The uses of knowledge in global health. BMJ Glob Health 6, e005802 (2021).
Watkins, K. E. & O’Neil, J. The Dimensions of the Learning Organization Questionnaire (the DLOQ): A Nontechnical Manual. Advances in Developing Human Resources 15, 133–147 (2013).
Watkins, K., & Marsick, V. (1996). (Eds.). In action: Creating the learning organization (Vol. 1). Alexandria, VA: ASTD Press.
Yang, B., Watkins, K. E. & Marsick, V. J. The construct of the learning organization: Dimensions, measurement, and validation. Human Resource Development Quarterly 15, 31–55 (2004).
#ChrisArgyris #doubleLoopLearning #globalHealth #healthSystemsResearch #KarenEWatkins #learningCulture #learningHealthSystems #VictoriaMarsick
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Five examples of double-loop learning in global health
Read this first: What is double-loop learning in global health?
Example 1: Addressing low uptake of a vaccine program
Single–Loop Learning: Improve logistics and supply chain management to ensure consistent vaccine availability at clinics.
Double–Loop Learning: Engage with community leaders to understand cultural beliefs and concerns around vaccination, and co-design a more localized and trustworthy immunization strategy.
What is the difference? Double-loop learning questions the assumption that the primary goal should be to increase uptake at all costs. It considers whether the program design respects community autonomy and addresses their real concerns. It may surface competing values of public health impact vs. community self-determination.
Example 2: Responding to an infectious disease outbreak
Single–Loop Learning: Rapidly mobilize health workers and supplies to affected areas to contain the outbreak following established emergency protocols.
Double–Loop Learning: Critically examine why the health system was vulnerable to this outbreak, and work with communities to redesign surveillance, preparedness and response systems to be more resilient.
What is the difference? Double-loop learning interrogates whether the existing outbreak response system is built on the value of health equity. It asks if the system privileges the needs of some populations over others and perpetuates historical power imbalances. It strives to create a more inclusive, participatory approach to defining outbreak preparedness and response priorities.
Example 3: Implementing a maternal health intervention that shows low adherence
Single–Loop Learning: Retrain health providers to improve their counseling skills and provide better patient education on the intervention.
Double–Loop Learning: Conduct participatory research with women and families to understand their needs, preferences and barriers to care-seeking, and collaborate with them to iteratively adapt the intervention design.
What is the difference? Double-loop learning challenges the implicit assumption that the intervention design is inherently correct and that non-adherence is a ‘user error’. It examines whether the intervention embodies values of respect, humility and co-creation with communities. It seeks to align the intervention with women’s self-articulated reproductive health values and preferences.
Example 4: Evaluating an underperforming community health worker (CHW) program
Single–Loop Learning: Strengthen CHW supervision, increase performance incentives, and optimize the ratio of CHWs to households.
Double–Loop Learning: Facilitate a joint reflection process with CHWs and community representatives to examine program strengths, challenges and equity gaps, and co-create a revised strategy that better aligns with community priorities and integrates CHWs’ insights.
What is the difference? Double-loop learning questions whether the CHW program is driven by the value of empowering communities as agents of their own health vs. treating CHWs as an instrument of technocratic public health aims. It re-centers the program on the value of CHW leadership and community-driven problem definition.
Example 5: Reforming a health financing policy to improve population coverage
Single–Loop Learning: Adjust the premium amounts, enrollment processes and benefit package based on initial uptake data.
Double–Loop Learning: Convene citizen panels and key stakeholders to deliberate on the fundamental goals and values underlying the financing reforms, and recommend redesigning the policy to better advance equity and financial protection.
What is the difference? Double-loop learning interrogates whether the true intent of the policy is to advance equity and financial protection for marginalized groups or simply to expand coverage as an end unto itself. It opens up debate on the core values and theory of change underlying the reforms. It aims to re-anchor the policy in a wholistic vision of equitable universal health coverage.
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What is double-loop learning in global health?
Argyris (1976) defines double-loop learning as occurring “when errors are corrected by changing the governing values and then the actions.” He contrasts this with single-loop learning, where “errors are corrected without altering the underlying governing values.”
- Double-loop learning involves questioning “not only the objective facts but also the reasons and motives behind those facts”.
- It requires becoming aware of one’s own “theories-in-use” – the often tacit beliefs and norms that shape behavior – and subjecting them to critical reflection and change.
This is challenging because it can threaten one’s sense of competence and self-image.
Checking for double-loop learning: ‘Are we doing things right?’ vs. ‘Are we doing the right things?’
In global health, double-loop learning means not just asking “Are we doing things right?” but also “Are we doing the right things?” It means being willing to challenge long-held assumptions about what works, for whom, and under what conditions.
Epistemological assumptions (“we already know the best way”), methodological orthodoxies (“this is not how we do things”), and apolitical stance (“I do health, not politics”) of epidemiology can predispose practitioners to be dismissive of a concept like double-loop learning.
Learn more: Five examples of double-loop learning in global health
Seye Abimbola is part of a growing community of researchers who argue that double-loop learning is critical for advancing equity and self-reliance in global health systems, because global health tends to overlook its own assumptions.
Is it reasonable to posit that some global health interventions have been driven by unchecked assumptions – assumptions about what communities need, what they value, and what will work in their context? How often have we relied on a one-size-fits-all approach, implementing ‘best practices’ from afar without fully understanding local realities? How do we know to what extent programs have thereby failed to meet their goals, wasted precious resources, and may have even caused unintended harm?
As Abimbola (2021) notes, “double-loop learning goes further to question and influence frameworks, models and assumptions around problems and their solutions, and can drive deeper shifts in objectives and policies.”
For example, affected communities hold vital expertise to mitigate health risks.
However, fully leveraging this potential requires global health professionals to fundamentally rethink their roles and assumptions.
- For research to serve the needs of affected communities, it is likely to be useful to reframe these roles and assumptions to see themselves as “subsidiary” partners in service of “primary” community actors (Abimbola, 2021).
- Institutionalizing double-loop learning requires enabling critical reflection and co-production between health workers, managers and citizens (Sheikh & Abimbola, 2021).
- It also depends on developing the learning capacities of communities and health workers in areas like participatory governance, team-based learning and innovation management.
The next logical question is ‘how’ to implement double-loop learning.
Learning culture is a critical concept missing from health systems research.
It provides a practical and actionable framework to operationalize the double-loop learning notion of ‘learning health systems’ and drive transformative change.
Learn more: Learning culture: the missing link in global health between learning and performance
Further reading
Learning-based complex work: how to reframe learning and development
What learning science underpins peer learning for Global Health?
How do we reframe health performance management within complex adaptive systems?
References
Abimbola, S. The uses of knowledge in global health. BMJ Glob Health 6, e005802 (2021). https://doi.org/10.1136/bmjgh-2021-005802
Argyris, C. Single-loop and double-loop models in research on decision making. Administrative science quarterly 363–375 (1976). https://doi.org/10.2307/2391848
Argyris, C. Double-loop learning, teaching, and research. Academy of Management Learning & Education 1, 206–218 (2002). https://www.jstor.org/stable/40214154
Kabir Sheikh & Seye Abimbola. Learning Health Systems: Pathways to Progress. (Alliance for Health Policy and Systems Research, 2021).
Image: The Geneva Learning Foundation Collection © 2024
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#ChrisArgyris #doubleLoopLearning #globalHealth #healthSystemsResearch #KabirSheikh #KarenEWatkins #learningCulture #learningHealthSystems #performance #SeyeAbimbola #VictoriaMarsick
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Read our Ebook No. 2 about why self-organized learning within small groups is much more powerful than individual cramming. https://www.qomenius.com/post/smallgroupmiracle
#qomenius #edtech #learning #selforganization #cohortlearning #cohort #betacodex #learningdesign #learningculture #organizationaldevelopment #leadership #learninganddevelopment
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Read our Ebook No. 2 about why self-organized learning within small groups is much more powerful than individual cramming. https://www.qomenius.com/post/smallgroupmiracle
#qomenius #edtech #learning #selforganization #cohortlearning #cohort #betacodex #learningdesign #learningculture #organizationaldevelopment #leadership #learninganddevelopment