Last Friday, 24 January, marked the UNESCO International Day of Education. And as part of that, the UNESCO Institute for Lifelong Learning hosted a webinar on ‘Lifelong learning in the age of AI’ aiming “to bring together policymakers, practitioners, and researchers to revisit the idea of lifelong learning in the age of emerging technologies, with a thematic focus on lifelong learning as a concept, workplace learning, digital competencies of adult educators, and bridging the grey digital divide.”
Current policies on AI and lifelong learning, they said, often adopt an instrumental and technologically deterministic approach, prioritising efficiency over human development and agency. UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the promise of lifelong learning opportunities for all, while ensuring that its application in the learning contexts is guided by the core principles of inclusion and equity.”
The webinar would discuss current trends, in policy, research, and innovative practices in emerging technologies such as AI and its relation to lifelong learning and the concept of agency.
One of the speakers was Rebecca Eynon, Professor of Education, the Internet and Society, at the University of Oxford with a presentation entitled 'Reconfiguring Lifelong Learning in the Age of AI: Insights from policy and research'. In many ways her presentation was prescient, coming as it did two days before the news of the DeepSeek Open Source model broke.
Rebecca began by questioning what is Al in lifelong learning? Is it an approach or an academic methodology? Motivations of engagement are about researching and facilitating learning (and are often more about knowledge acquisition and are psychological in focus), while remaining cautious about the current hype around Al in Education. They also encompass relations between Al and humans while working with Al. Al is assumed to contribute to increased efficiency of humans and learning and Al is implemented and conceptualized as a peer or colleague. Al is viewed as part of a wider reconfiguration of humans and their contexts.
Artificial Intelligence is currently hailed as a 'solution' to perceived problems in education. Though few sociologists of education would agree with its deterministic claims, this Al solutionist thinking is gaining significant currency.
Rebecca went on to explain research using a relatively novel method for sociology - a knowledge graph - which together with Bourdieusean theory, she said, facilitated a critical examination of how and why different stakeholders in education, educational technology and policy are valorising Al. including their main concepts and motivation.
Drawing on this analysis, she argues that Al is currently being mobilised in education in problematic ways and advocates for more systematic sociological thinking and research to re-orientate the field to account for society's structural conditions. She pointed to the dominance of the commercial sector the prevalence of personalisation. The commercial sector is tending to dominate conversations about Al and education. But the commercial motivations are based on the needs of the market, and promotes an individual view of learning where economic agendas predominate.
There is, Rebecca said, almost an absence of Al policy and specific education actors may well intensify economic and individual notions of education. This has likely implications for what kinds of systems are designed for education . Although this points to an intensification of economic and individual notions of education this is not inevitable. Change is complex, and there is fragility in the ed tech market, with some signs of discontent with Al. She pointed to increasing calls for ethical and equitable AI,.
Rebecca concluded her presentation by pointing to the need to make visible and understand the networks around AI in education and the complex ecology to change them. She said we have to work as a community to demand alternative Al futures for Lifelong Learning.
About the Image
Through distortion, this image depicts a pixelized and reconfigured portrait of Ada Lovelace cast on a microchip. Ada Lovelace was an English mathematician who discovered that a computer could follow a sequence of instructions beyond pure calculation. Her contributions laid the groundwork for programmable computing that underpins algorithms driving AI advancement. As GPU (Graphics Processing Units) microchips are maximizing parallel computing for accelerating tasks like machine learning, the image blending Lovelace’s historical contributions with modern computational technology.