INSPIRE CS-AI Fellowship

Innovative New Spaces for Practice and Rehearsal Teacher Education (INSPIRE) Computer Science (CS) – Artificial Intelligence (AI) or INSPIRE CS-AI is new year-long fellowship program for CS teacher educators lead by faculty and staff at the MIT Teaching Systems Lab and Carnegie Mellon University.

About the INSPIRE CS-AI Fellowship

In the 2020-2021 academic year, 24 teacher-educator fellows from across the United States - alongside staff from MIT TSL and CMU - will work together to creatively and playfully co-design equity-focused practice spaces using Teacher Moments and ELK, two learning platforms that helps novice teachers rehearse for and reflect on important decisions in teaching inclusively. Over the course of the fellowship year, fellows will play with and modify existing practice spaces, develop new practice spaces, and use these tools with their preservice teacher students for fun and powerful practice-based professional learning experiences.

This material is based upon work supported by the National Science Foundation under Grant No. 1917668. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

How can I get involved with the INSPIRE CS-AI Fellowship?

Project Publications

Hillaire, G., Larke, L. R., & Reich, J. (2020). Detecting confusion in everyday conversations. The Center for Research on Computation and Society (CRCS) at Harvard. Rising Stars Workshop on AI for Social impact, Cambridge, MA.

Hillaire, G., Larke, L. R., & Reich, J. (2020). Digital storytelling through authoring simulations with Teacher Moments. In D. Schmidt-Crawford (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 1736-1745). Association for the Advancement of Computing in Education (AACE): Online.

Hillaire, G., Reich, J., Thompson, M., & Symonette, D. (2020). Analyzing the relationship between cognitive dissonance and mixed emotion to support emotion regulation in teachers [Poster Session 10]. American Educational Research Association (AERA): San Francisco, CA.

Larke, L., Hillaire, G., Chen, H., Dutt, R., Rosé, C., & Reich, J. (2020, June 19–23). Cognitive dissonance and equity: Designing digital simulations for K-12 computer science teacher education. In M. Gresalfi & I. S. Horn (Eds.), Proceedings of the 14th International Conference of the Learning Sciences (ICLS) (pp. 2405-2406). Online: International Society of the Learning Sciences (ISLS).

Sullivan, F., Hillaire, G., Larke, L. R., & Reich, J. (2020). Using Teacher Moments during the COVID-19 pivot. Journal of Technology and Teacher Education, 28(2), 303-313. Waynesville, NC: Society for Information Technology & Teacher Education.



Fellowship Team



Zoubeid Dagher

INSPIRE CS-AI Fellow

Yin-Chan Janet Liao

INSPIRE CS-AI Fellow

Stephanie Rollag Yoon

INSPIRE CS-AI Fellow

Sara Vogel

INSPIRE CS-AI Fellow

Doctoral Student (CMU)

Meredith Thompson

Research Scientist and Lecturer

Melissa Stange

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

Postdoctoral Associate

Executive Director Of Teaching Systems Lab & Assistant Professor Of Comparative Media Studies/Writing

INSPIRE CS-AI Fellow

jason trumble

INSPIRE CS-AI Fellow

Jana LoBello Miller

INSPIRE CS-AI Fellow

Undergraduate Research Assistant

Postdoctoral Associate

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

Professor (CMU)

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

INSPIRE CS-AI Fellow

Aman Yadav

INSPIRE CS-AI Fellow

Undergraduate Research Assistant

INSPIRE CS-AI Fellow

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