Tag: learning theory

  • Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production

    Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production

    Does the educational purpose of video change with AI?

    The purpose of video in education is undergoing a fundamental transformation in the age of artificial intelligence. This medium, long established in digital learning environments, is changing not just in how we consume it, but in its very role within the learning process.

    Video has always been a problem in education

    Video has always presented significant challenges in educational contexts. Its linear format makes it difficult to skim or scan content. Unlike text, which allows learners to quickly jump between sections, glance at headings, or scan for key information, video requires sequential consumption. This constraint has long been problematic for effective learning.

    Furthermore, in many regions where our learners are based, internet access remains expensive, unreliable, or limited. Downloading or streaming video content can be prohibitively costly in terms of both data usage and time. The result is straightforward: few learners will watch educational videos, regardless of their potential value.

    The bandwidth and attention divide

    This reality creates a significant divide in educational access. While instructional designers and educators in high-resource settings continue to produce video-heavy content, learners in bandwidth-constrained environments have been systematically excluded from these resources. Even when videos are technically accessible, the time investment required to watch linear content often exceeds what busy professionals can allocate to learning activities.

    Emergent AI platforms are scanning YouTube video transcripts to extract precisely what users need. This capability suggests a transformation for the role of video. YouTube and other video platforms are evolving into what might be called “interstitial processors”, mediating layers that support knowledge production and dissemination for subsequent extraction and analysis by both humans and machines.

    A more inclusive workflow for knowledge extraction

    This changing relationship with video content could enable more inclusive approaches to learning. When I discover a potentially valuable educational webinar, I now follow a structured approach to maximize efficiency and accessibility:

    1. Download the video file.
    2. Transcribe it using Whisper AI technology.
    3. Ask targeted questions to extract meaningful insights from the transcript.
    4. Request direct quotes as evidence of key points.

    This method circumvents the traditional requirement to invest 60 minutes or more in viewing content that may ultimately offer limited value. More importantly, it transforms bandwidth-heavy video into lightweight text that can be accessed, searched, and processed even in low-connectivity environments.

    I suspect that it is no accident that YouTube has recently placed additional restrictions on downloading videos from its platform.

    Bridging the resource gap with AI

    Current consumer-grade AI systems like Claude.ai have limitations: they cannot yet process full videos directly. For now, we are restricted to text-based interactions with video content, hence my transcription of downloaded content. However, this constraint will likely dissolve as AI capabilities continue to advance.

    The immediate benefit is that this approach can help bridge the resource gap that has disadvantaged learners in bandwidth-constrained environments. By extracting the knowledge essence from videos, we could make educational content more accessible and equitable across diverse learning contexts.

    The continuing value of educational video production

    Despite these challenges, educational video production continues to be a relevant method for humans and machines that need a way to share what they know. Hence, what we are witnessing is not the diminishing relevance of educational video, but rather a transformation in how its knowledge value is extracted and utilized. The production of video content remains valuable. It is our methods of processing and consuming it that are evolving.

    Aligning with effective networked learning theory

    This shift aligns with contemporary understanding of effective learning. Research consistently demonstrates that passive consumption of information, whether through video or text, remains insufficient for meaningful learning. Genuine knowledge development emerges through active construction – the processes of questioning, connecting, applying, and adapting information within broader contexts.

    The AI-enabled extraction of insights from video content represents a step toward more active engagement with educational materials – transforming passive viewing into targeted interaction with the specific knowledge elements most relevant to individual learning needs.

    Knowledge networks trump media formats

    Our experience with global learning networks demonstrates the importance of moving beyond media format limitations. When health professionals from diverse contexts share practices and adapt them to their specific environments, the medium of exchange becomes secondary to the knowledge being constructed.

    AI tools that can extract and process information from videos help overcome the medium’s inherent limitations, turning static content into formats that can not only be read, viewed, or listened to – but that can also be remixed and fused with other sources. This approach allows learners to engage more directly with knowledge, freed from the constraints of linear consumption and bandwidth requirements.

    Rethinking video as a dual-purpose knowledge production format

    We are witnessing the development of new approaches to educational content where media exists simultaneously for direct human consumption and as structured data for AI processing. When the boundaries between content formats become increasingly permeable, with value residing not in the medium itself but in the knowledge that can be extracted and constructed from it.

    Despite the consumption challenges, video remains an exceptional medium for content production that serves both humans and machines. For content creators, video offers unmatched richness in communicating complex ideas through visual demonstration, tone, and emotional connection.

    What is emerging is not a devaluation of video creation but a transformation in how its knowledge is accessed. As AI tools evolve, video becomes increasingly valuable as a comprehensive knowledge repository where information is encoded in multiple dimensions – visual, auditory, and textual through transcripts.

    This makes video uniquely positioned as a “dual-purpose” content format: rich and engaging for those who can consume it directly, while simultaneously serving as a structured data source from which AI can extract targeted insights.

    In this paradigm, video production remains vital while consumption patterns evolve toward more efficient, personalized knowledge extraction.

    The creator’s effort in producing quality video content now yields value across multiple consumption pathways rather than being limited to linear viewing

    How to cite this article: Sadki, R. (2025). Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production. Learning to make a difference. https://doi.org/10.59350/rfr2z-h4y93

    References

    Delello, J.A., Watters, J.B., Garcia-Lopez, A., 2024. Artificial Intelligence in Education: Transforming Learning and Teaching, in: Delello, J.A., McWhorter, R.R. (Eds.), Advances in Business Information Systems and Analytics. IGI Global, pp. 1–26. https://doi.org/10.4018/979-8-3693-3003-6.ch001

    Guo, P.J., Kim, J., Rubin, R., 2014. How video production affects student engagement: An empirical study of MOOC videos, in: Proceedings of the First ACM Conference on Learning@ Scale Conference. ACM, pp. 41–50. https://doi.org/10.1145/2556325.2566239

    Hansch, A., Hillers, L., McConachie, K., Newman, C., Schildhauer, T., Schmidt, P., 2015. Video and Online Learning: Critical Reflections and Findings from the Field. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2577882

    Kumar, L., Singh, D.K., Ansari, M.A., 2024. Role of Video Content Generation in Education Systems Using Generative AI:, in: Doshi, R., Dadhich, M., Poddar, S., Hiran, K.K. (Eds.), Advances in Educational Technologies and Instructional Design. IGI Global, pp. 341–355. https://doi.org/10.4018/979-8-3693-2440-0.ch019

    Mayer, R.E., Fiorella, L., Stull, A., 2020. Five ways to increase the effectiveness of instructional video. Education Tech Research Dev 68, 837–852. https://doi.org/10.1007/s11423-020-09749-6

    Netland, T., Von Dzengelevski, O., Tesch, K., Kwasnitschka, D., 2025. Comparing human-made and AI-generated teaching videos: An experimental study on learning effects. Computers & Education 224, 105164. https://doi.org/10.1016/j.compedu.2024.105164

    Salomon, G., 1984. Television is “easy” and print is “tough”: The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology 76, 647–658. https://doi.org/10.1037/0022-0663.76.4.647

    Sun, M., 2024. An Intelligent Retrieval Method for Audio and Video Content: Deep Learning Technology Based on Artificial Intelligence. IEEE Access 12, 123430–123446. https://doi.org/10.1109/ACCESS.2024.3450920

    Image: The Geneva Learning Foundation Collection © 2025

  • What is the pedagogy of Teach to Reach?

    What is the pedagogy of Teach to Reach?

    In a rural health center in Kenya, a community health worker develops an innovative approach to reaching families who have been hesitant about vaccination.

    Meanwhile, in a Brazilian city, a nurse has gotten everyone involved – including families and communities – onboard to integrate information about HPV vaccination into cervical cancer screening.

    These valuable insights might once have remained isolated, their potential impact limited to their immediate contexts.

    But through Teach to Reach – a peer learning platform, network, and community hosted by The Geneva Learning Foundation – these experiences become part of a larger tapestry of knowledge that transforms how health workers learn and adapt their practices worldwide.

    Since January 2021, the event series has grown to connect over 21,000 health professionals from more than 70 countries, reaching its tenth edition with 21,398 participants in June 2024.

    Scale matters, but this level of engagement begs the question: how and why does it work?

    The challenge in global health is not just about what people need to learn – it is about reimagining how learning happens and gets applied in complex, rapidly-changing environments to improve performance, improve health outcomes, and prepare the next generation of leaders.

    Traditional approaches to professional development, built around expert-led training and top-down knowledge transfer, often fail to create lasting change.

    They tend to ignore the rich knowledge that exists in practice – what we know when we are there every day, side-by-side with the community we serve – and the complex ways that learning actually occurs in professional networks and communities.

    Teach to Reach is one component in The Geneva Learning Foundation’s emergent model for learning and change.

    This article describes the pedagogical patterns that Teach to Reach brings to life.

    A new vision for digital-first, networked professional learning

    Teach to Reach represents a shift in how we think about professional learning in global health.

    Its pedagogical pattern draws from three complementary theoretical frameworks that together create a more complete understanding of how professionals learn and how that learning translates into improved practice.

    At its foundation lies Bill Cope’s and Mary Kalantzis’s New Learning framework, which recognizes that knowledge creation in the digital age requires new approaches to learning and assessment.

    Teach to Reach then integrates insights from Watkins and Marsick’s research on the strong relationship between learning culture (a measure of the capacity for change) and performance and George Siemens’s learning theory of connectivism to create something syncretic: a learning approach that simultaneously builds individual capability, organizational capacity, and network strength.

    Active knowledge making

    The prevailing model of professional development often treats learners as empty vessels to be filled with expert knowledge.

    Drawing from constructivist learning theory, it positions health workers as knowledge creators rather than passive recipients.

    When a community health worker in Kenya shares how they’ve adapted vaccination strategies for remote communities, they are not just describing their work – they’re creating valuable knowledge that others can learn from and adapt.

    The role of experts is even more significant in this model: experts become “Guides on the side”, listening to challenges and their contexts to identify what expert knowledge is most likely to be useful to a specific challenge and context.

    (This is the oft-neglected “downstream” to the “upstream” work that goes into the creation of global guidelines.)

    This principle manifests in how questions are framed.

    Instead of asking “What should you do when faced with vaccine hesitancy?” Teach to Reach asks “Tell us about a time when you successfully addressed vaccine hesitancy in your community.” This subtle shift transforms the learning dynamic from theoretical to practical, from passive to active.

    Collaborative intelligence

    The concept of collaborative intelligence, inspired by social learning theory, recognizes that knowledge in complex fields like global health is distributed across many individuals and contexts.

    No single expert or institution holds all the answers.

    By creating structures for health workers to share and learn from each other’s experiences, Teach to Reach taps into what cognitive scientists call “distributed cognition” – the idea that knowledge and understanding emerge from networks of people rather than individual minds.

    This plays out practically in how experiences are shared and synthesized.

    When a nurse in Brazil shares their approach to integrating COVID-19 vaccination with routine immunization, their experience becomes part of a larger tapestry of knowledge that includes perspectives from diverse contexts and roles.

    Metacognitive reflection

    Metacognition – thinking about thinking – is crucial for professional development, yet it is often overlooked in traditional training.

    Teach to Reach deliberately builds in opportunities for metacognitive reflection through its question design and response framework.

    When participants share experiences, they are prompted not just to describe what happened, but to analyze why they made certain decisions and what they learned from the experience.
    This reflective practice helps health workers develop deeper understanding of their own practice and decision-making processes.

    It transforms individual experiences into learning opportunities that benefit both the sharer and the wider community.

    Recursive feedback

    Learning is not linear – it is a cyclical process of sharing, reflecting, applying, and refining.

    Teach to Reach’s model of recursive feedback, inspired by systems thinking, creates multiple opportunities for participants to engage with and build upon each other’s experiences.

    This goes beyond communities of practice, because the community component is part of a broader, dynamic and ongoing process.

    Executing a complex pedagogical pattern

    The pedagogical pattern of Teach to Reach come to life through a carefully designed implementation framework over a six-month period, before, during, and after the live event.

    This extended timeframe is not arbitrary – it is based on research showing that sustained engagement over time leads to deeper learning and more lasting change than one-off learning events.
    The core of the learning process is the Teach to Reach Questions – weekly prompts that guide participants through progressively more complex reflection and sharing.

    These questions are crafted to elicit not just information, but insight and understanding.

    They follow a deliberate sequence that moves from description to analysis to reflection to application, mirroring the natural cycle of experiential learning.

    Communication as pedagogy

    In Teach to Reach, communication is not just about delivering information – it is an integral part of the learning process.

    The model uses what scholars call “pedagogical communication” – communication designed specifically to facilitate learning.

    This manifests in several ways:

    • Personal and warm tone that creates psychological safety for sharing
    • Clear calls to action that guide participants through the learning process
    • Multiple touchpoints that reinforce learning and maintain engagement
    • Progressive engagement that builds complexity gradually

    Learning culture and performance

    Watkins and Marsick’s work helps us understand why Teach to Reach’s approach is so effective.

    Learning culture – the set of organizational values, practices, and systems that support continuous learning – is crucial for translating individual insights into improved organizational performance.

    Teach to Reach deliberately builds elements of strong learning cultures into its design.

    Furthermore, the Geneva Learning Foundation’s research found that continuous learning is the weakest dimension of learning culture in immunization – and probably global health.

    Hence, Teach to Reach itself provides a mechanism to strengthen specifically this dimension.

    Take the simple act of asking questions about real work experiences.

    This is not just about gathering information – it’s about creating what Watkins and Marsick call “inquiry and dialogue,” a fundamental dimension of learning organizations.

    When health workers share their experiences, they are not just describing what happened.

    They are engaging in a form of collaborative inquiry that helps everyone involved develop deeper understanding.

    Networks of knowledge

    George Siemens’s connectivism theory provides another crucial lens for understanding Teach to Reach’s effectiveness.

    In today’s world, knowledge is not just what is in our heads – it is distributed across networks of people and resources.

    Teach to Reach creates and strengthens these networks through its unique approach to asynchronous peer learning.

    The process begins with carefully designed questions that prompt health workers to share specific experiences.

    But it does not stop there.

    These experiences become nodes in a growing network of knowledge, connected through themes, challenges, and solutions.

    When a health worker in India reads about how a colleague in Nigeria addressed a particular challenge, they are not just learning about one solution – they are becoming part of a network that makes everyone’s practice stronger.

    From theory to practice

    What makes Teach to Reach particularly powerful is how it fuses multiple theories of learning into a practical model that works in real-world conditions.

    The model recognizes that learning must be accessible to health workers dealing with limited connectivity, heavy workloads, and diverse linguistic and cultural contexts.

    New Learning’s emphasis on multimodal meaning-making supports the use of multiple communication channels ensuring accessibility.

    Learning culture principles guide the creation of supportive structures that make continuous learning possible even in challenging conditions.

    Connectivist insights inform how knowledge is shared and distributed across the network.

    Creating sustainable change

    The real test of any learning approach is whether it creates sustainable change in practice.

    By simultaneously building individual capability, organizational capacity, and network strength, it creates the conditions for continuous improvement and adaptation.

    Health workers do not just learn new approaches – they develop the capacity to learn continuously from their own experience and the experiences of others.

    Organizations do not just gain new knowledge – they develop stronger learning cultures that support ongoing innovation.

    And the broader health system gains not just a collection of good practices, but a living network of practitioners who continue to learn and adapt together.

    Looking forward

    As global health challenges have become more complex, the need for more effective approaches to professional learning becomes more urgent.

    Teach to Reach’s pedagogical model, grounded in complementary theoretical frameworks and proven in practice, offers valuable insights for anyone interested in creating impactful professional learning experiences.

    The model suggests that effective professional learning in complex fields like global health requires more than just good content or engaging delivery.

    It requires careful attention to how learning cultures are built, how networks are strengthened, and how individual learning connects to organizational and system performance.

    Most importantly, it reminds us that the most powerful learning often happens not through traditional training but through thoughtfully structured opportunities for professionals to learn from and with each other.

    In this way, Teach to Reach is a demonstration of what becomes possible when we reimagine how professional learning happens in service of better health outcomes worldwide.

    Image: The Geneva Learning Foundation Collection © 2024

  • Teaching and learning in The Walking Dead (S05E14)

    Teaching and learning in The Walking Dead (S05E14)

    In this episode, the young Noah has asked to meet with Reg, an elderly architect or engineer who had the know-how to build the wall that protects the community of Alexandria, which some believe has survived zombies and other predators mostly by sheer luck.

    Noah recognizes that it’s more than luck – and wants to Reg to pass on knowledge and expertise that is different from that needed only to avert death. Reg shows him a notebook in which he’s kept personal notes on events, and offers one of the notebooks so that Noah can begin to keep a record.

    Outcome? Noah dies in the next episode. So much for transmissive learning and container views of knowledge.

    (It appears that YouTube will prevent viewers in some countries from accessing the brief excerpt I’ve posted there. Apologies if you are unable to see it.)

    – How is it that you called this extremely early morning meeting, yet I’m the one bringing breakfast?
    – ‘Cause you’re a good guy.
    – The evidence seems to go in that direction.
    – What’s up?
    – Can we start meeting in the mornings?
    – So I can bring you steel-cut oatmeal and ask you why we’re meeting?
    – So you can teach me how to build things.
    – You want to be an architect?
    – I want to make sure those walls stay up.
    – Do you think they could fall?
    – I think they could get knocked in. Could be years from now, could be when I’m your age.
    – (chuckles) I’ll still be around when you’re my age.
    – Well, it wouldn’t hurt if I knew some of what you knew. For the walls, the houses. Some new buildings.
    – So you’re in it for the long haul?
    – Yeah. What are you writing?
    – Oh, I write everything down. Everything of note. Now you should.
    – There’s gonna be a lot to remember.
    – This is the beginning of this place. You should record all that. Along with everything I’m gonna teach you about building things. (turns off water)
    – Oh, no, thank you.

    Transcript source

  • Education is the science of sciences

    Education is the science of sciences

    “We want to talk about science as a certain kind of ‘knowing’.

    Specifically, we want to use it to name those deeper forms of knowing that are the purpose of education.

    Science in this broader sense consists of things you do to know that are premeditated, things you set out to know in a carefully considered way.

    It involved out-of-the ordinary knowledge-making efforts that have a peculiar intensity of focus, rather than things you get to know as an incidental consequence of doing something or being somewhere.

    Science has special methods or techniques for knowing.

    These methods are connected with specialized traditions of knowledge making and bodies of knowledge.

    In these senses, history, language studies and mathematics are sciences, as are chemistry, physics and biology.

    Education is the science of learning (and, of course, teaching).

    Its subject is how people come to know.

    It teaches learners the methods for making knowledge that is, in our broad sense, scientific.

    It teaches what has been learned and can be learned using these methods.

    In this sense, education is privileged to be the science of sciences.

    As a discipline itself, the science of education develops knowledge about the processes of coming to know.”

    Kalantzis, M., Cope, B., 2012. New learning: elements of a science of education, Second edition. ed. Cambridge University Press.

    Image: Neurons in the brain. Bryan Jones, University of Utah

     

  • Flow

    Flow

    In our studies, we found that every flow activity, whether it involved competition, chance, or any other dimension of experience, had this in common: It provided a sense of discovery, a creative feeling of transporting the person into a new reality. It pushed the person to higher levels of performance, and led to previously undreamed-of states of consciousness. In short, it transformed the self by making it more complex. In this growth of the self lies the key to flow activities.

    Flow channel states
    Flow channel states

    Source: Csikszentmihalyi, M., 1990. Flow : the psychology of optimal experience, 1st ed. ed. Harper & Row, New York. Photo: Fluid Painting 79 Acrylic On Canvas (Mark Chadwick/Flickr).
  • A few of my favorite excerpts from George Siemens’s Knowing Knowledge (2006)

    A few of my favorite excerpts from George Siemens’s Knowing Knowledge (2006)

    My own practice (and no doubt yours) has been shaped by many different learning theorists. George Siemens, for me, stands out articulating what I felt but did not know how to express about the changing nature of knowledge in the Digital Age. Below I’ve compiled a few of my favorite excerpts from his book Knowing Knowledge, published in 2006, two years before he taught the first Massive Open Online Course (MOOC) with Alec Couros and Stephen Downes.

    Learning has many dimensions. No one model or definition will fit every situation. CONTEXT IS CENTRAL. Learning is a peer to knowledge. To learn is to come to know. To know is to have learned. We seek knowledge so that we can make sense. Knowledge today requires a shift from cognitive processing to pattern recognition.

    Figure 5 Knowledge types

    Construction, while a useful metaphor, fails to align with our growing understanding that our mind is a connection-creating structure. We do not always construct (which is high cognitive load), but we do constantly connect.

    We learn foundational elements through courses…but we innovate through our own learning.

    Figure 17 Learning and knowledge domains

    The changing nature of knowledge

    The Achilles heel of existing theories rests in the pace of knowledge growth. All existing theories place processing (or interpretation) of knowledge on the individual doing the learning. This model works well if the knowledge flow is moderate. A constructivist view of learning, for example, suggests that we process, interpret, and derive personal meaning from different information formats. What happens, however, when knowledge is more of a deluge than a trickle? What happens when knowledge flows too fast for processing or interpreting?

    Figure 23 Knowledge as process, not product

    Knowledge has broken free from its moorings, its shackles. Those, like Francis Bacon, who equate knowledge with power, find that the masses are flooding the pools and reservoirs of the elite. […] The filters, gatekeepers, and organizers are awakening to a sea of change that leaves them adrift, clinging to their old methods of creating, controlling, and distributing knowledge. […] Left in the wake of cataclysmic change are the knowledge creation and holding structures of the past. The ideologies and philosophies of reality and knowing—battle spaces of thought and theory for the last several millennia—have fallen as guides.

    Libraries, schools, businesses—engines of productivity and society—are stretching under the heavy burden of change. New epistemological and ontological theories are being formed, as we will discuss shortly with connective knowledge. These changes do not wash away previous definitions of knowledge, but instead serve as the fertile top of multiple soil layers. […]

    Or consider email in its earlier days—many printed out a paper copy of emails, at least the important ones, and filed them in a file cabinet. Today we are beginning to see a shift with email products that archive and make email searchable and allow individuals to apply metadata at point of use (tagging).

    Knowledge has to be accessible at the point of need. Container-views of knowledge, artificially demarcated (courses, modules) for communication, are restrictive for this type of flow and easy-access learning.

    Everything is going digital. The end user is gaining control, elements are decentralizing, connections are being formed between formerly disparate resources and fields of information, and everything seems to be speeding up.

    “Know where” and “know who” are more important today that knowing what and how.

    Figure 16 Know Where

    Once flow becomes too rapid and complex, we need a model that allows individuals to learn and function in spite of the pace and flow.

    We need to separate the learner from the knowledge they hold. It is not really as absurd as it sounds. Consider the tools and processes we currently use for learning. Courses are static, textbooks are written years before actual use, classrooms are available at set times, and so on.

    The underlying assumption of corporate training and higher education centers on the notion that the world has not really changed.

    But it has. Employees cannot stay current by taking a course periodically. Content distribution models (books and courses) cannot keep pace with information and knowledge growth. Problems are becoming so complex that they cannot be contained in the mind of one individual—problems are held in a distributed manner across networks, with each node holding a part of the entire puzzle. Employees require the ability to rapidly form connections with other specialized nodes (people or knowledge objects). Rapidly creating connections with others results in a more holistic view of the problem or opportunity, a key requirement for decision making and action in a complex environment.

    How do we separate the learner from the knowledge? By focusing not on the content they need to know (content changes constantly and requires continual updating), but on the connections to nodes which continually filter and update content.

    Here is what the connectivism implementation cycle looks like as a mind map. (Click on the image to download the PDF).

    Connectivism implementation cycle (George Siemens, 2006)

    Source: George Siemens, Knowing Knowledge (2006).

    Image: TEDxNYED