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ML4STEM Professional Development Program: Enriching K-12 STEM Teaching with Machine Learning


The advances of machine learning (ML) in scientific discovery (SD) reveal exciting opportunities to utilize it as a cross-cutting tool for inquiry-based learning in K-12 STEM classrooms. There are, however, limited efforts on providing teachers with sufficient knowledge and skills to integrate ML into teaching. Our study addresses this gap by proposing a professional development (PD) program named ML4STEM. Based on existing research on supporting teacher learning in innovative technology integration, ML4STEM is composed of Teachers-as-Learners and Teachers-as-Designers sessions. It integrates an accessible ML learning platform designed for students with limited math and computing skills. We implemented this PD program and evaluated its effectiveness with 18 K-12 STEM teachers. Findings confirm that ML4STEM successfully develops teachers’ understanding of teaching STEM with ML as well as fosters positive attitudes toward applying the ML as an in-class teaching technology. Discussions on the implications of our findings from ML4STEM are provided for future PD researchers and designers.

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Availability of data and material

Data is not available to anyone outside of the Institutional Review Board protocol due to ethical restrictions.

Code Availability

The code is available on request from the authors.





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We would like to thank our collaborators doctoral student Zenon Borys from the Warner School of Education, undergrad students Sufian Mushtaq, Saad Ahmad, Abdul Moid Munawar from the Department of Computer Science for helping with system and dataset preparation, Zheng Zhang for helping with data collection, and K-12 teachers for choosing to participate in our study.

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Correspondence to Zhen Bai.

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Tang, J., Zhou, X., Wan, X. et al. ML4STEM Professional Development Program: Enriching K-12 STEM Teaching with Machine Learning. Int J Artif Intell Educ (2022).

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  • Professional development program
  • K-12 STEM teaching
  • Machine learning