Flo Vincent, Content Designer, The University of Edinburgh

Generative AI tools are already influencing how prospective students research degrees — but many university websites aren’t designed for AI to interpret accurately. In this session, you’ll learn how the University of Edinburgh redesigned course content to make entry requirements and programme information machine-readable, improving accuracy in AI-generated answers and reducing the risk of misinformation.

Guus Goorts, Education Marketing Coach, Crystal Marketing BV

Publishing more content won’t improve visibility if it lacks originality or expertise. This session demonstrates a practical workflow for turning subject-matter expertise into articles that perform in AI Overviews and large language models, while keeping the process manageable for both experts and content teams.

Joel Goodman, VP Growth Strategy, Squiz

Prospective students are increasingly relying on AI tools instead of traditional search results. If your content isn’t informing those answers, your institution risks being overlooked. This session explains what answer engine optimisation means in practice and how to audit your content for AI-driven discovery.

Tracy Playle, Founder, ContentEd and CEO, Pickle Jar Communications

If we’re to optimise our content better for generative AI - or any technology for that matter - we need to surface our content in a way that machines and intelligent systems can actually understand. This means getting to grips with structured content, semantic markup, knowledge graphs, and much more that might sound “techy” but is important for us all to understand. This session will help you to start making sense of it all.

Brian W Piper, Founder, AIreFlow Solutions

The shift from traditional search optimisation to answer engine optimisation can feel disruptive, but the core principles of effective content still apply. This session explains what has changed, what still matters, and how to adapt your existing strategy without starting from scratch.