Case Studies: Evidence-Based Research on AI in Education

Real implementation. Honest findings. Practical solutions for UK schools navigating AI’s impact on curriculum, assessment, and equity.

Our case studies examine critical challenges facing UK schools as AI transforms education. Each study combines rigorous analysis of policy frameworks, curriculum design, and assessment practice with evidence from actual implementation—documenting what works, what doesn’t, and what we’re still figuring out.

We don’t offer theoretical solutions or vendor promises. We analyse proven models, identify policy gaps, and build frameworks schools can adapt to their contexts. From Thomas Telford’s 30-year evidence on closing opportunity gaps to navigating contradictory DfE guidance on AI assessment, our research provides the analytical foundation for defensible decision-making during regulatory uncertainty.

These aren’t success stories claiming we’ve “solved” AI challenges. They’re honest examinations of complex problems, showing how schools can build robust approaches despite imperfect information, evolving technology, and policy ambiguity. Each case study includes limitations, ongoing challenges, and research priorities—because effective practice emerges through experimentation and refinement, not perfect initial solutions.

Current case studies:

• How can schools assess student work authentically when AI detection tools systematically fail?
• What does effective AI literacy curriculum look like at KS3?
• How can schools close opportunity gaps without relying on parental engagement?
• Where do DfE AI guidance documents create compliance risks for schools?

Research-driven. Practitioner-focused. Built for the messy reality of implementing AI policy in UK schools.