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:
- Download the video file.
- Transcribe it using Whisper AI technology.
- Ask targeted questions to extract meaningful insights from the transcript.
- 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
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Image: The Geneva Learning Foundation Collection © 2025