4 items with this tag.
One in seven people in the UK are using AI chatbots for health advice instead of seeing a GP. The institutional response has been to warn them off, but that response applies a standard it doesn't consistently apply to anything else in the system. This post argues that the risk comparison driving those warnings is systematically skewed, and that a more honest accounting points toward an entirely different kind of response.
The Chartered Society of Physiotherapy's annual Founders' Lecture, arguing that the question facing the profession is not what AI can do that humans cannot, but how practitioners shape the context in which human-AI collaboration happens. The talk opens with the disorienting evidence that AI now matches or exceeds clinicians on diagnosis, empathy, and ethical reasoning, then rejects the defensive 'sanctuary strategies' this provokes. Its central idea is context sovereignty: because language models are stateless, static, and contextless, the professional context a practitioner brings is not a comfort but a structural necessity — and controlling it is the distinctive human contribution. It closes with the coming shift to patient context sovereignty and the work of meaning-making in an ambiguous moment.
A presentation for the ADAPT International Conference 2023 on expertise and AI in professional education. Argues that generative AI is an unstoppable force — democratising access to professional knowledge and acting as a low-cost, personalised "expert mentor" — meeting the immovable object of universities and their traditional role as gatekeepers of expertise. Asks what formal professional education is for in a world where expertise is abundantly available, and argues for raising expectations through project-based learning and authentic assessment.
A presentation for the 2023 ENPHE Conference on coming to terms with AI in professional education. Argues that the dominant discourse traps us in a false choice between AI as saviour and AI as threat, when what the moment actually demands is a tolerance for ambiguity. Borrowing the antihero archetype, it reframes AI as neither hero nor villain but an ambiguous partner in new human–machine coalitions, and proposes that wise adoption is not a destination but the start of an iterative, cooperative process.