EPISODE · May 5, 2026 · 41 MIN
The Trust Gap: Transparency, Evidence, and AI in Education
from ASU+GSV Summit Sessions · host ASU+GSV
Recorded live at the 2026 ASU+GSV Summit in San Diego, this session featured Pat Yongpradit, General Manager, Global Education and Workforce Policy at Microsoft; Miriam Vogel, President & CEO at EqualAI; Kim Majerus, Vice President, Worldwide Public Sector Global Education and U.S. State & Local Government at Amazon AWS; Larz May, Founder and Executive Director at #HalfTheStory and Co-founder & CEO at Ginko; Paul Gollash, SVP, Global Mobility Solutions at ETS; and Daphne Li, CEO at Common Sense Privacy.The speakers explored how trust has become the defining barrier to meaningful AI adoption in education. They discussed how the sector remains early in the trust curve, where skepticism is high and concerns around hallucination, bias, overconfidence, and opaque data handling continue to fuel distrust among educators and families.The session examined how trust can be built not through marketing, but through transparent governance, explainable decision pathways, and evidence that withstands scrutiny. The panel addressed critical questions around model selection, evaluation frequency, data storage, access controls, and measurable proof of improved outcomes. Through this conversation, the speakers highlighted how trust can be intentionally engineered into AI-powered products through transparent data policies, rigorous evaluation frameworks, and clear evidence of impact.
What this episode covers
Recorded live at the 2026 ASU+GSV Summit in San Diego, this session featured Pat Yongpradit, General Manager, Global Education and Workforce Policy at Microsoft; Miriam Vogel, President & CEO at EqualAI; Kim Majerus, Vice President, Worldwide Public Sector Global Education and U.S. State & Local Government at Amazon AWS; Larz May, Founder and Executive Director at #HalfTheStory and Co-founder & CEO at Ginko; Paul Gollash, SVP, Global Mobility Solutions at ETS; and Daphne Li, CEO at Common Sense Privacy.The speakers explored how trust has become the defining barrier to meaningful AI adoption in education. They discussed how the sector remains early in the trust curve, where skepticism is high and concerns around hallucination, bias, overconfidence, and opaque data handling continue to fuel distrust among educators and families.The session examined how trust can be built not through marketing, but through transparent governance, explainable decision pathways, and evidence that withstands scrutiny. The panel addressed critical questions around model selection, evaluation frequency, data storage, access controls, and measurable proof of improved outcomes. Through this conversation, the speakers highlighted how trust can be intentionally engineered into AI-powered products through transparent data policies, rigorous evaluation frameworks, and clear evidence of impact.
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The Trust Gap: Transparency, Evidence, and AI in Education
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