The Trust Gap: Transparency, Evidence, and AI in Education

Trust is now the defining barrier to meaningful AI adoption in education. We are still early in the trust curve, where skepticism remains high and hallucination, bias, overconfidence, and opaque data handling fuel distrust among educators and families. We explore how trust is built not through marketing but through transparent governance, explainable decision pathways, and evidence that withstands scrutiny.

Which model are you using? How often is it evaluated? What data is stored? Who has access? What evidence proves it improves outcomes

This panel explores how trust can be engineered into AI-powered products — through model selection, evaluation frameworks, transparent data policies, and measurable evidence of impact.