From demonstration to explanation
A companion to Chapter 19 of the book, Future Research Directions in AI and Learning. The same ideas in several forms: film, conversation, 2 infographics, a mind map and 6 activities. Start with whichever suits you.
Chapter 19 in two minutes
Chapter 19 looks at what research still needs to do. The first generation of studies set out to show that systems function, learners engage and short-term results improve. The next generation has to explain why aligned delivery helps: through cognitive, motivational or social mechanisms, for which learners, and in which contexts.
That means longer studies. Immediate test scores are easy to collect and tend to overstate long-term transfer, so the chapter calls for tracking capability and performance over months or years, and for measures of application in practice rather than recall. In Kirkpatrick's terms, the gap is in behaviour and results.
It also means testing with trainers who did not develop the approach, to rule out expectancy effects and show the results generalise. And it raises open questions about learner data: who owns it, how long it is kept, how it is protected from misuse, and what recourse learners have.
Showing that something works and explaining why it works are different jobs.
Watch or listen
Prefer to take it in by eye or by ear? Both cover the core ideas of Chapter 19 in a few minutes.
Film, 2 min
The next era of AI workplace learning
Where research on AI-supported training goes next, and why proving it works is only the first half of the job. Captions are built in.
Conversation, 6 min
How to prove AI training works
A conversation about longitudinal studies, independent trainers, measuring behaviour rather than recall, and the ethics of learner data.
Chapter 19 at a glance
A quick overview, then the full argument with the evidence. Select either one to see it full size.
Put it into practice
Short activities to run with a group or work through on your own. Each one has a printable card.
With a group (3)
On your own (3)
Go deeper
The research and writing behind Chapter 19.
- Donald Kirkpatrick, ResearchGateEvaluating Training Programs: The Four LevelsA review of the four-level model the chapter uses to show where the evidence on training is thinnest.
- Luckin, Holmes, Griffiths and Forcier, Open UniversityIntelligence Unleashed: An Argument for AI in EducationAn early, careful case for what AI could do in education, and what research it would need.
- OECDOECD Digital Education Outlook 2021Where AI and learning analytics stood in 2021, including the questions about data and governance the chapter raises.
- Neil Selwyn, BloomsburyEducation and Technology: Key Issues and DebatesA critical look at the claims made for educational technology, and why they need testing.






