I am attending the first Futures4Eurupe futures and foresight conference in Vienna, listening to the delightful Petranka Malcheva talking about Wales' approach to long-termism.
Which came up in my last Futuring Is... podcast.
Such an energetic and vibrant bunch of people from so many backgrounds. Exciting!
#Vienna #7WellbeingGoals #Wales #Futures4EuropeConference2025 #foresight #collectiveintelligence #futuringis
Are online meetings reducing our collective intelligence? New research says maybe. A close look at the experiment tells a different story.
#online #meetings #CollectiveIntelligence #TeamWork #TheoryOfMind #eventprofs
@Tattered @sz_duras @osfa_2030 @tg9541 @danahilliot @sallyMA @GautierDay @Car @Carolefoppolo @beatricejess @MariposaCuicui
From all sides, a true #concertdesvolontés! Thank you all for your inspiring contributions and collective intelligence. Let’s keep deconstructing and imagining together.
Read the latest post: https://homohortus31.wordpress.com/2025/05/05/deconstruction-outil-critique-pour-repenser-le-naturel-et-le-construit/ & revisit our journey: https://homohortus31.wordpress.com/2025/05/04/bilan-critique-deconstruction-pouvoir-et-voies-vers-la-transformation-sociale/
#Agamben #Foucault #Derrida #Philosophy #Diderot #Politics #Deconstruction #CollectiveIntelligence
The travel time to #Mars 🔴 is currently between 1/4 and 1/2 year, but ~60% seem not to know anything about it according to a recent #vote https://spacey.space/@spaceflight/114367524800901103
#CollectiveIntelligence #GroupIntelligence #HiveMind #SwarmIntelligence
#Management40 #GPM #ipma #management #CollectiveIntelligence #driftdemocracytoautocracy Mein Neuer Blog-Beitrag 'Von der gesellschaftlichen Kernschmelze oder dem Übergang Demokratie-Autokratie und (vielleicht auch) umgekehrt' in meinem Blog Management 4.0: https://agilemanagement40.com/von-der-gesellschaftlichen-kernschmelze-oder-dem-uebergang-demokratie-autokratie-und-vielleicht-auch-umgekehrt
are you sick and bored of the #ai hype? follow our 2025 activities on #collectiveintelligence - we focus on alternative forms of intelligence and community and how we can use networks in a different way to liberate ourselves from commercial digital tools.
Herzliche Einladung! Zum Auftakt unseres Jahresprogramms 2025 #collectiveintelligence laden wir Euch ein ins Forum Stadtpark, wo die Medienkunstgruppe #cmkk eine interaktive Installation im Rahmen der #ultrablack #nonference präsentiert. Zu erleben vom 2.-12. April 2025 im #forumstadtpark
🚀 Buckle up, folks! 🤓 Mistral OCR is here to revolutionize document understanding, because apparently we've been doing it all wrong since hieroglyphs. Who knew unlocking the "collective intelligence" of PDFs was the next frontier in human enlightenment? 🙄
https://mistral.ai/fr/news/mistral-ocr #MistralOCR #DocumentUnderstanding #TechInnovation #CollectiveIntelligence #HumanEnlightenment #HackerNews #ngated
#Management40 #GPM #ipma #management #CollectiveIntelligence #artificialintelligence #ai #QuantumComputing #CollectiveMind #selbstorganisation #selforganization #consciousness Mein Neuer Blog-Beitrag 'Vom ‚Tor zum Sein‘ oder von der Quantum Collective Mind Theory' in meinem Blog Management 4.0: https://agilemanagement40.com/vom-tor-zum-sein-oder-von-der-quantum-collective-mind-theory
Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research | TechTrends
The latest paper I can proudly add to my list of publications, Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research has been published in the (unfortunately) closed journal TechTrends. Here’s a direct link to the paper that should hopefully bypass the paywall, if it has not been used too often.
I’m 16th of 47 coauthors, led by the truly wonderful Junhong Xiao, who is the primary orchestrator and mastermind behind it. This is a companion piece to our Manifesto for Teaching and Learning in a Time of Generative AI and it starts where the other paper left off, delving further into what we don’t know (or at least do not agree that we know) about and (taking up most of the paper) what we might do about that lack of knowledge. I think this presents a pretty useful and wide-ranging research agenda for anyone with an interest in AI and education.
Methodologically, it emerged through a collaborative writing process between a very multinational group of international researchers in open, digital, and online learning. It’s not a random sample of people who happen to know one another: the huge group represents a rich mix of (extremely) well-established and (excellent) emerging researchers from a broad set of cultural backgrounds, covering a wide range of research interests in the field. Junhong does a great job of extracting the themes and organizing all of that into a coherent narrative.
In many ways I like this paper more than its companion piece. I think this is because, though its findings are – as the title implies – less well-defined than the first, I am more closely aligned with the underlying assumptions, attitudes and values that underpin the analysis. It grapples more firmly with the wicked problems and it goes deeper into the broader, situated, human nature of the systems in which generative AI is necessarily intertwingled, skimming over the more simplistic conversations about cheating, reliability, and so on to get at some meatier but more fundamental issues that, ultimately, relate to how and why we do this education thing in the first place.
Abstract
Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its potential use and future impact on education. Adopting the methodology of collective writing as an inquiry, this study reports on the participating educators’ perceived grey areas (i.e. issues that are unclear and/or controversial) and recommendations on future research. The grey areas reported cover decision-making on the use of GenAI, AI ethics, appropriate levels of use of GenAI in education, impact on learning and teaching, policy, data, GenAI outputs, humans in the loop and public–private partnerships. Recommended directions for future research include learning and teaching, ethical and legal implications, ownership/authorship, funding, technology, research support, AI metaphor and types of research. Each theme or subtheme is presented in the form of a statement, followed by a justification. These findings serve as a call to action to encourage a continuing debate around GenAI and to engage more educators in research. The paper concludes that unless we can ask the right questions now, we may find that, in the pursuit of greater efficiency, we have lost the very essence of what it means to educate and learn.
Reference
Xiao, J., Bozkurt, A., Nichols, M., Pazurek, A., Stracke, C. M., Bai, J. Y. H., Farrow, R., Mulligan, D., Nerantzi, C., Sharma, R. C., Singh, L., Frumin, I., Swindell, A., Honeychurch, S., Bond, M., Dron, J., Moore, S., Leng, J., van Tryon, P. J. S., … Themeli, C. (2025). Venturing into the Unknown: Critical Insights into Grey Areas and Pioneering Future Directions in Educational Generative AI Research. TechTrends. https://doi.org/10.1007/s11528-025-01060-6#AI #collectiveIntelligence #education #ethics #genAI #generativeAI #instructionalDesign #learning #teaching
Understanding collective stupidity in social computing systems
Here are the slides from a talk I just gave to a group of grad students at AU in our ongoing seminar series, on the nature of collectives and ways we can use and abuse them. It’s a bit of a sprawl covering some 30 odd years of a particularly geeky, semi-philosophical branch of my research career (not much on learning and teaching in this one, but plenty of termites) and winding up with very much a work in progress. I rushed through it at the end of a very long day/week/month/year/life but I hope someone may find it useful!
This is the abstract:
“Collective intelligence” (CI) is a widely-used but fuzzy term that can mean anything from the behaviour of termites, to the ability of an organization to adapt to a changing environment, to the entire human race’s capacity to think, to the ways that our individual neurons give rise to cognition. Common to all, though, is the notion that the combined behaviours of many independent agents can lead to positive emergent changes in the behaviour of the whole and, conversely, that the behaviour of the whole leads to beneficial changes in the behaviours of the agents of which it is formed. Many social computing systems, from Facebook to Amazon, are built to enable or to take advantage of CI. Here I define social computing systems as digital systems that have no value unless they are used by at least two participants, and in which those participants play significant roles in affecting one another’s behaviour. This is a broad definition that embraces Google Search as much as email, wikis, and blogs, and in which the behaviour of humans and the surrounding structures and systems they belong to are at least as important as the algorithms and interfaces that support them. Unfortunately, the same processes that lead to the wisdom of crowds can at least as easily result in the stupidity of mobs, including phenomena like filter bubbles and echo chambers that may be harmful in themselves or that render systems open to abuse such as trolling, disinformation campaigns, vote brigading, and successful state manipulation of elections. If we can build better models of social computing systems, taking into account their human and contextual elements, then we stand a better chance of being able to avoid their harmful effects and using them for good. To this end I have coined the term “ochlotecture”, from the Classical Greek ὄχλος (ochlos), meaning “multitude” and τέκτων (tektōn) meaning “builder”. In this seminar I will identify some of the main ochlotectural elements that contribute to collective intelligence, describe some of the ways it can be undermined, and explore some of the ramifications as they relate to social software design and management.
#collective #collectiveCognition #collectiveIntelligence #extendedMind #socialMedia #stigmergy
Sandy Pentland's research findings on social physics and meetings echo the focus of my meeting design work for over thirty years
https://www.conferencesthatwork.com/index.php/event-design/2014/05/social-physics-and-meetings
#meetings #EventDesign #SocialPhysics #SandyPentland #CollectiveIntelligence #eventprofs
Hanzi Freinacht on society's capacity to manage complexity through the fostering of collective intelligence:
#Complexity #SocietalProgress #SystemsChange #SocialChange #CollectiveIntelligence
Looking at @inaturalist.bsky.social annual statistics I am both delighted and questioning the impact of City Nature Challenge (CNC) on the dataset. I love CNC because it's a distributed, volunteered, unfunded, loosely coordinated, #CollectiveIntelligence example. This makes it similar to Wikipedia and OpenStreetMap. AFAIK these are the only examples for this type of open data dataset. Any others? #CitizenScience #ParticipatoryResearch #Crowdsourcing
Are online meetings reducing our collective intelligence? New research says maybe. A close look at the experiment tells a different story.
#online #meetings #CollectiveIntelligence #TeamWork #TheoryOfMind #eventprofs
Knowing-in-action: Bridging the theory-practice divide in global health
The gap between theoretical knowledge and practical implementation remains one of the most persistent challenges in global health. This divide manifests in multiple ways: research that fails to address practitioners’ urgent needs, innovations from the field that never inform formal evidence systems, and capacity building approaches that cannot meet the massive scale of learning required. Donald Schön’s seminal 1995 analysis of the “dilemma of rigor or relevance” in professional practice offers crucial insights for “knowing-in-action“. It can help us understand why transforming global health requires new ways of knowing – a new epistemology.
Listen to this article below. Subscribe to The Geneva Learning Foundation’s podcast for more audio content.
Schön’s analysis: The dilemma of rigor or relevance
Schön begins by examining how knowledge becomes institutionalized through education. Using elementary school mathematics as an example, he describes how knowledge is broken into discrete units (“math facts”), organized into progressive modules, assembled into curricula, and measured through standardized tests. This systematization shapes not just content but the entire organization of time, space, and institutional arrangements.
From this foundation, Schön introduces his central metaphor of two contrasting landscapes in professional practice that prevent “knowing-in-action”. As he describes it:
“In the varied topography of professional practice, there is a high, hard ground overlooking a swamp. On the high ground, manageable problems lend themselves to solution through the use of research-based theory and technique. In the swampy lowlands, problems are messy and confusing and incapable of technical solution.”
The cruel irony, Schön observes, lies in the relative importance of these terrains: “The problems of the high ground tend to be relatively unimportant to individuals or to society at large, however great their technical interest may be, while in the swamp lie the problems of greatest human concern.”
This creates what Schön calls the “dilemma of rigor or relevance” – practitioners must choose between remaining on the high ground where they can maintain technical rigor or descending into the swamp where they must rely on experience, intuition, and what he terms “muddling through.”
The historical roots of the divide
Schön traces this dilemma to the epistemology embedded in modern research universities. Drawing on Edward Shils’s historical analysis, he describes how American scholars returning from Germany after the Civil War brought back “the German idea of the university as a place in which to do research that contributes to fundamental knowledge, preferably through science.”
This was, as Schön notes, “a very strange idea in 1870,” running counter to the prevailing British model of universities as sanctuaries for liberal arts or finishing schools for gentlemen. The new model first took root at Johns Hopkins University, whose president embraced the “bizarre notion that professors should be recruited, promoted, and granted tenure on the basis of their contributions to fundamental knowledge.”
This shift created what Schön terms the “Veblenian bargain” (named after Thorstein Veblen), establishing a separation between:
Knowing-in-action in global health: From fragmentation to integration
The historical division between theory and practice that Schön identified continues to shape global health in profound and often problematic ways. This manifests in three interconnected challenges that demand our urgent attention: the knowledge-practice gap, the scale challenge, and the complexity challenge. Yet emerging approaches suggest potential paths forward, particularly through structured peer learning networks that could help bridge Schön’s “high ground” and “swamp.”
Three fundamental challenges
Challenge #1: The knowing-in-action divide
The separation between research institutions and field practice creates not just an academic concern but a practical crisis in healthcare delivery. Consider the response to COVID-19: while research institutions rapidly generated new knowledge about the virus, frontline health workers struggled to translate this into practical approaches for their specific contexts. Their hard-won insights about what worked in different settings rarely made it back into formal evidence systems, epitomizing the one-way flow of knowledge that impoverishes both research and practice.
This pattern repeats across global health. Research agendas, shaped by academic incentives and funding priorities, often fail to address practitioners’ most pressing challenges. A community health worker in rural Bangladesh facing complex challenges around vaccine hesitancy may struggle to find relevant guidance – while global experts are convinced that they already have all the answers. Meanwhile, local solutions to building vaccine confidence remain uncaptured by formal knowledge systems.
The rise of implementation science attempts to bridge this divide, yet often remains subordinate to “pure” research in academic hierarchies. This reflects Schön’s observation about the privileging of high ground problems over swampy ones, even when the latter hold greater practical significance.
Challenge #2: The scale imperative
Traditional approaches to professional education face fundamental limitations in meeting the massive need for health worker capacity building. The World Health Organization projects a shortfall of 10 million health workers by 2030, mostly in low- and middle-income countries. Conventional training approaches that rely on cascading knowledge through workshops and formal courses can reach only a fraction of those who need support.
More fundamentally, these knowledge transmission models prove inadequate for addressing complex local realities. A standardized curriculum developed by experts, no matter how well-designed, cannot anticipate the diverse challenges health workers face across different contexts. When a district immunization manager in Nigeria must adapt vaccination strategies for nomadic populations during a drought, they need more than pre-packaged knowledge – they need ways to learn from others who are facing similar challenges.
Resource constraints further limit the reach of conventional approaches. The cost of traditional training programmes, both in money and time away from service delivery, makes it impossible to scale them to meet the need. Yet the human cost of this capacity gap, measured in preventable illness and death, demands urgent solutions.
Challenge #3: The complexity conundrum
Contemporary global health faces challenges that fundamentally resist standardized technical solutions. Climate change exemplifies this complexity, creating cascading effects on health systems and communities that cannot be addressed through linear interventions. When rising temperatures alter disease patterns while simultaneously disrupting cold chains for vaccine delivery, no single technical fix suffices.
Similarly, emerging and re-emerging infectious diseases demand responses that cross traditional boundaries between animal and human health, environmental factors, and social determinants. Health workforce development must grapple with complex systemic issues around motivation, retention, and capacity building. The COVID-19 pandemic demonstrated how traditional approaches to health system strengthening often prove inadequate in the face of complex adaptive challenges.
Emerging solutions: A new paradigm for learning and practice
Recent innovations suggest promising approaches to bridging these divides through structured peer learning networks. Digital platforms enable health workers to share experiences and solutions across geographical boundaries, creating new possibilities for scaled learning that maintains local relevance.
Solution #1: The power of structured peer learning
Experience from digital learning networks demonstrates how structured peer interaction can enable more efficient and effective knowledge sharing than traditional top-down approaches. When health workers can directly connect with peers facing similar challenges, they not only share solutions but collectively generate new knowledge through their interactions.
These networks provide mechanisms for validating practical knowledge through peer review processes that complement traditional academic validation. A successful intervention developed by a rural clinic in Thailand can be critically examined by peers, adapted for different contexts, and rapidly disseminated across the network. This creates a more dynamic and responsive knowledge ecosystem than traditional publication cycles allow.
Solution #2: Network effects and collective intelligence
The potential of practitioner networks extends beyond simple knowledge sharing. When properly structured, these networks create possibilities for:
Most importantly, these networks can help bridge Schön’s high ground and swamp by creating dialogue between different forms of knowledge and practice. They provide spaces where academic research can inform field practice while simultaneously allowing field insights to shape research agendas.
Four principles toward knowing-in-action for global health
Drawing on Schön’s call for a “new epistemology,” we can identify four principles for transforming how we know what we know in global health:
Principle #1: Valuing multiple forms of knowledge
The complexity of contemporary health challenges demands recognition of multiple valid forms of knowledge. The practical wisdom developed by a community health worker through years of service deserves attention alongside randomized controlled trials. This requires challenging existing hierarchies of evidence while maintaining rigorous standards for validating knowledge claims.
Principle #2: Enabling knowledge creation from practice
Health workers must be supported as knowledge producers, not just knowledge consumers. This means creating structures for systematically capturing and validating field insights, building evidence from implementation experience, and enabling continuous learning from practice. Digital platforms can provide scaffolding for this knowledge creation while ensuring quality through peer review processes.
Principle #3: Scaling through networked learning
Traditional scaling approaches that rely on standardization and top-down dissemination must be complemented by networked learning to create and amplify knowing-in-action. This means building systems that can:
Principle #4: Embracing complexity
Rather than seeking to reduce complexity through standardization, health systems must build capacity for working effectively within complex adaptive systems. This means supporting adaptive learning, enabling context-specific solutions, and building capacity for systems thinking at all levels.
The challenges facing global health today demand new ways of creating, validating, and sharing knowledge. By embracing approaches that bridge Schön’s high ground and swamp, we may find paths toward health systems that are both more rigorous and more relevant to the communities they serve.
Looking forward
Schön’s analysis helps explain why traditional approaches to global health knowledge and learning often fall short. More importantly, it points toward solutions that could help bridge the theory-practice divide to support knowing-in-action:
Schön’s insights remain remarkably relevant to contemporary global health challenges. His call for a new epistemology that can bridge theory and practice speaks directly to our current needs. By embracing new approaches to learning and knowledge creation that honor both rigor and relevance, we may find ways to address the complex challenges that lie ahead.
The key lies not in choosing between high ground and swamp, but in building new kinds of bridges between them – bridges that can support the massive scale of learning needed while maintaining the local relevance essential for impact. Recent innovations in peer learning networks and digital platforms suggest this bridging may be increasingly possible, offering hope for more effective global health practice in an increasingly complex world.
The challenge now is to develop and implement these bridging approaches at the scale needed to support global health workers worldwide. This will require new ways of thinking about knowledge, learning, and practice – ways that honor both the rigor of research and the wisdom of experience. The future of global health may depend on our success in this endeavor.
Listen to the AI podcast deep dive about this article
Reference
Schön, Donald A., 1995. Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning 27, 27–34. https://doi.org/10.1080/00091383.1995.10544673
Image: The Geneva Learning Foundation Collection © 2024
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#1 #2 #3 #4 #CollectiveIntelligence #DonaldASchön #epistemology #globalHealth #knowDoGap #networkedLearning #peerLearning #scale
The collective ochlotecture of large language models: slides from my talk at CI.edu, 2024
Here are my slides from the 1st International Symposium on Educating for Collective Intelligence, last week, here is my paper on which it was based, and here is the video of the talk itself:
You can find this and videos of the rest of the stunning line-up of speakers at https://www.youtube.com/playlist?list=PLcS9QDvS_uS6kGxefLFr3kFToVIvIpisn It was an incredibly engaging and energizing event: the chat alone was a masterclass in collective intelligence that was difficult to follow at times but that was filled with rich insights and enlightening debates. The symposium site, that has all this and more, is at https://cic.uts.edu.au/events/collective-intelligence-edu-2024/
With just 10 minutes to make the case and 10 minutes for discussion, none of us were able to go into much depth in our talks. In mine I introduced the term “ochlotecture”, from the Classical Greek ὄχλος (ochlos), meaning “multitude” and τέκτων (tektōn) meaning “builder” to describe the structures and processes that define the stuff that gives shape and form to collections of people and their interactions. I think we need such a term because there are virtually infinite ways that such things can be configured, and the configuration makes all the difference. We blithely talk of things like groups, teams, clubs, companies, squads, and, of course, collectives, assuming that others will share an understanding of what we mean when, of course, they don’t. There were at least half a dozen quite distinct uses of the term “collective intelligence” in this symposium alone. I’m still working on a big paper on this subject that goes into some depth on the various dimensions of interest as they pertain to a wide range of social organizations but, for this talk, I was only concerned with the ochlotecture of collectives (a term I much prefer to “collective intelligence” because intelligence is such a slippery word, and collective stupidity is at least as common). From an ochlotectural perspective, these consist of a means of collecting crowd-generated information, processing it, and presenting the processed results back to the crowd. Human collective ochlotectures often contain other elements – group norms, structural hierarchies, schedules, digital media, etc – but I think those are the defining features. If I am right then large language models (LLMs) are collectives, too, because that is exactly what they do. Unlike most other collectives, though (a collectively driven search engine like Google Search being one of a few partial exceptions) the processing is unique to each run of the cycle, generated via a prompt or similar input. This is what makes them so powerful, and it is what makes their mimicry of human soft technique so compelling.
I did eventually get around to the theme of the conference. I spent a while discussing why LLMs are troubling – the fact that we learn values, attitudes, ways of being, etc from interacting with them; the risks to our collective intelligence caused by them being part of the crowd, not just aggregators and processors of its outputs; and the potential loss of the soft, creative skills they can replace – and ended with what that implies for how we should act as educators: essentially, to focus on the tacit curriculum that has, till now, always come from free; to focus on community because learning to be human from and with other humans is what it is all about; and to decouple credentials so as to reduce the focus on measurable outcomes that AIs can both teach and achieve better than an average human. I also suggested a couple of principles for dealing with generative AIs: to treat them as partners rather than tools, and to use them to support and nurture human connections, as ochlotects as much as parts of the ochlotecture.
I had a point to make in a short time, so the way I presented it was a bit of a caricature of my more considered views on the matter. If you want a more balanced view, and to get a bit more of the theoretical backdrop to all this, Tim Fawns’s talk (that follows mine and that will probably play automatically after it if you play the video above) says it all, with far greater erudition and lucidity, and adds a few very valuable layers of its own. Though he uses different words and explains it far better than I, his notion of entanglement closely echoes my own ideas about the nature of technology and the roles it plays in our cognition. I like the word “intertwingled” more than “entangled” because of its more positive associations and the sense of emergent order it conveys, but we mean substantially the same thing: in fact, the example he gave of a car is one that I have frequently used myself, in exactly the same way.
#collective #collectiveIntelligence #education #genAI #generativeAI #intelligence #LLM #ochlotecture #teaching
Good news in our preprint about #polarization:
Demand for BIPARTISAN #news analysis was strong!
People in the #US preferred fact-checking teams that engaged in #AdversarialCollaboration at least as much as copartisan and/or professional teams.
Follow the manuscript or authors on #GoogleScholar: https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1017561778629165218
If you prefer a link directly to our preprint: https://doi.org/10.31219/osf.io/gp9w7
#journalism #socialMedia #decisionScience #collectiveIntelligence #socialPsychology #politicalPsychology #epistemology #xPhi
In #CollectiveIntelligence and #agile settings, we tend to use a lot of sticky notes (“Post-It®”, “stickies”…). Yet I so often see unreadable ones that ruin a lot of the value we would otherwise get from workshops! This 4 minutes video is the updated English version of the videos you might already know me for from a few years ago on how to best handle sticky notes for readability in a professional setting 😊
https://www.youtube.com/watch?v=7Ug1I9OMA_A
The first international symposium on educating for collective intelligence. This Thursday!! https://cic.uts.edu.au/events/collective-intelligence-edu-2024/
#education #collectiveintelligence #civicintelligence
