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Learning with Machines

Every single serious discussion about improving the future tends to turn into a discussion about improving education. That's because the future features large disruptions in technology, in employment, in climate, in where and how people live, and in the kinds of ideas that spread quickly around the world. To cope with the challenges of these disruptions - and to be able to take good advantage of all the opportunities arising - that's going to need new sets of skills, new knowledge, new thinking methods, and new strength of character. We - all of us - are going to need to learn lots more things, more quickly than ever before. Hence the need for changes in education. That's where technology can come in, in a positive way. Improvements in education that are enabled by new technologies have long been foretold. Nearly twenty years ago, the famous Harvard Business School professor Clayton Christensen, along with two colleagues, wrote a book called "Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns". In that book, the writers commented that venerable educational institutes were notoriously resistant to change, but the writers thought opportunities with technology would in due course lead to major transformations - with students everywhere being able to learn more topics more quickly in ways more suited to each of their individual needs and learning styles. Well, up until recently, the changes predicted by Clayton Christensen and others have tended to happen more slowly than hoped for. That shouldn't really surprise us. Disruption often goes through an initial disappointing slow phase before accelerating through faster change. And that faster change is what seems to be upon us now, in February 2026. AI systems have upgraded, in just a few years, to being, often, incredibly useful for people wanting to learn new things. But, at the same time, as perhaps with all disruptions, there are potential large downsides to the changes as well as potential large upsides. It's not just that AI systems can mislead people with their made-up hallucinations. It's also that these systems can be used by bad actors to intentionally mislead people. And, moreover, these AIs can leave us distracted, lazy, drained, or paranoid. So how can we, all of us, find the best ways of "Learning with Machines" - to gain and share the benefits, without falling victim to the potential downsides? To discuss this critically important question, London Futurists was joined in this live webinar by three speakers who each have valuable personal insights to share on this question: Bruce Lloyd, Emeritus Professor from London South Bank University Peter Scott, Founder of the Centre for AI in Canadian Learning Alexandra Whittington, Futurist on the Future of Business team at TCS. The webinar was introduced and moderated by David Wood, Chair of London Futurists. For more information about this webinar, see https://www.meetup.com/london-futuris... Other recommended follow-ups: Bruce Lloyd's LinkedIn page (where many AI-generated videos and slidesets have been uploaded):   / bruce-lloyd-623197   About Peter Scott: https://www.peterscott.com/ The Centre for AI in Canadian Learning: https://caicl.org/ About Alexandra Whittington:   / alexandra-whittington-futurist   Learning with Machines: Reflections on 50+ AI Experiments - briefing slides created by Bruce Lloyd and circulated in advance of the webinar: https://londonfuturists.com/wp-conten... "India first country to launch a Traditional Knowledge Digital Library": https://economictimes.indiatimes.com/... "Which Humans?" by Harvard Center for International Development: https://www.hks.harvard.edu/centers/c...