The Promise and Perils of AI in Education: Stanford GSE Students Share Their Work

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In this special episode, we sit down with three innovative Stanford Graduate School of Education (GSE) students who are exploring cutting-edge applications of AI in education.

Michael Chrzan is a Master’s student and Dean’s Fellow in the Education Data Science program at Stanford. A former Master Teacher in Detroit, he taught Mathematics and AP Computer Science for seven years. His research uses machine learning to predict large-scale school closures and inform equitable decision-making.

Matías Hoyl is a Computer Science graduate from Chile and an edtech entrepreneur who has founded two startups focused on improving learning through technology. He led a coding bootcamp for women in Latin America, helping them launch tech careers. At Stanford, he is researching AI applications in education, including synthetic student simulations and AI-generated teaching tools.

Samin Khan is an AI researcher specializing in K-12 and higher education and currently an AI Research Scientist at Kiddom. His work focuses on developing AI models for curriculum development, lesson planning, and grading. At Stanford’s Education NLP Lab, he researches dialogue-based pedagogy and student engagement using large language models.

💡 5 Things You’ll Learn in This Episode:

How AI-powered predictive modeling can help districts plan for school closures.
The potential of synthetic students to improve assessment design and instruction.
How AI is supporting teachers with curriculum implementation and real-time feedback.
Why AI tutors may not improve student learning outcomes as expected.
The risks and opportunities of AI in education, especially for equity and accessibility.

✨ Episode Highlights:

[00:06:07] Michael Chrzan on using machine learning to predict school closures.
[00:10:51] Samin Khan on AI’s role in lesson planning and teacher feedback.
[00:16:37] Matías Hoyl on simulating student learning with AI-powered models.
[00:24:09] Balancing AI research with real-world edtech applications.
[00:31:39] The importance of data, bias, and transparency in AI for education.
[00:46:11] Will AI improve or widen equity gaps in education?

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Chapters

1. The Promise and Perils of AI in Education: Stanford GSE Students Share Their Work (00:00:00)

2. Michael Chrzan on using machine learning to predict school closures. (00:06:38)

3. Samin Khan on AI’s role in lesson planning and teacher feedback.  (00:11:22)

4. Matías Hoyl on simulating student learning with AI-powered models. (00:17:31)

5. Balancing AI research with real-world edtech applications. (00:25:03)

6. The importance of data, bias, and transparency in AI for education. (00:32:33)

7. Will AI improve or widen equity gaps in education? (00:47:05)

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