Bringing Artificial Intelligence (AI) and Machine Learning (ML) into Elementary Classrooms

Faiza Zafar; Carolyn Nichol; Matthew Cushing

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI
Tagged Division: Data Science and Artificial Intelligence (DSAI) Constituent Committee
Tagged Topic: Diversity
Page Count: 10
DOI: 10.18260/1-2--56031
Permanent URL: https://peer.asee.org/56031
Download Count: 2

Paper Authors

biography

Faiza Zafar Rice University

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Dr. Zafar is the Assistant Director for Equitable Research, Evaluation, and Grant Development at the Rice Office of STEM Engagement. She has her Ph.D. in Educational Leadership with an emphasis on Math Education. She earned her B.S. in Chemistry and M.Ed. from the University of St. Thomas, Houston, TX.

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Carolyn Nichol Rice University

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Dr. Carolyn Nichol is a Faculty Fellow in Chemistry and the Director of the Rice Office of STEM Engagement (R-STEM). R-STEM provides teacher professional development to elementary and secondary teachers in science and math content and pedagogy, while also

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biography

Matthew Cushing Rice University orcid.org/0000-0002-7112-5068

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As Executive Director of the Rice Office of STEM Engagement (R-STEM), Matthew oversees all programs and operations for the department. He has been presenting on AI in Education for the last few years at local, regional, and national conferences.

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Abstract

Introduction: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into educational settings has become a powerful tool for engaging students and enhancing their learning experiences. To facilitate this integration, teachers require comprehensive training in these advanced topics. In response, we have developed a virtual professional development program for STEM educators across elementary and high school levels, where teachers take part in a 6-week virtual research experience focused on developing cost-effective health devices with machine learning (ML), with particular attention to diabetes and breast cancer. Teacher participants collaborate with graduate student mentors, engage in discussions with faculty members conducting relevant research, explore real datasets, and create grade-appropriate lesson plans. This paper focuses on the overall program design and the experiences of an elementary STEM teacher who participated in the program. Methods: Retrospective analysis of weekly reflective blog posts and interviews with the elementary teacher after the program served as our primary data sources to help us understand her experience in the program and how she was able to integrate machine-learning concepts into 3rd to 5th-grade classrooms. Results: The teacher successfully translated her acquired knowledge of ML into an engaging lesson for over 300 3rd to 5th-grade students that spanned four class periods. Discussion: Our findings indicate that introducing machine learning at younger educational levels is both feasible and beneficial. The virtual research experience provided educators with the essential knowledge and skills to incorporate these concepts into their classrooms. The program's collaborative environment, usage of real-time data, and the opportunity for teachers to create lessons based on their individual interests proved effective in making AI and ML concepts accessible and relevant for students. Conclusion: Providing targeted professional development to STEM teachers and empowering them with advanced knowledge can enhance student learning. Additionally, programs like this can cultivate a new generation of informed teachers and their students capable of leveraging technology to understand and address critical health issues.

Citation
Format

Zafar, F., & Nichol, C., & Cushing, M. (2025, June), Bringing Artificial Intelligence (AI) and Machine Learning (ML) into Elementary Classrooms Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--56031

TY  - CPAPER
AB  - Introduction: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into educational settings has become a powerful tool for engaging students and enhancing their learning experiences. To facilitate this integration, teachers require comprehensive training in these advanced topics. In response, we have developed a virtual professional development program for STEM educators across elementary and high school levels, where teachers take part in a 6-week virtual research experience focused on developing cost-effective health devices with machine learning (ML), with particular attention to diabetes and breast cancer. Teacher participants collaborate with graduate student mentors, engage in discussions with faculty members conducting relevant research, explore real datasets, and create grade-appropriate lesson plans. This paper focuses on the overall program design and the experiences of an elementary STEM teacher who participated in the program. Methods: Retrospective analysis of weekly reflective blog posts and interviews with the elementary teacher after the program served as our primary data sources to help us understand her experience in the program and how she was able to integrate machine-learning concepts into 3rd to 5th-grade classrooms. Results: The teacher successfully translated her acquired knowledge of ML into an engaging lesson for over 300 3rd to 5th-grade students that spanned four class periods. Discussion: Our findings indicate that introducing machine learning at younger educational levels is both feasible and beneficial. The virtual research experience provided educators with the essential knowledge and skills to incorporate these concepts into their classrooms. The program&#39;s collaborative environment, usage of real-time data, and the opportunity for teachers to create lessons based on their individual interests proved effective in making AI and ML concepts accessible and relevant for students. Conclusion: Providing targeted professional development to STEM teachers and empowering them with advanced knowledge can enhance student learning. Additionally, programs like this can cultivate a new generation of informed teachers and their students capable of leveraging technology to understand and address critical health issues.
AU  - Faiza Zafar
AU  - Carolyn Nichol
AU  - Matthew Cushing
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - Bringing Artificial Intelligence (AI) and Machine Learning (ML) into Elementary Classrooms
UR  - https://peer.asee.org/56031
DO  - 10.18260/1-2--56031
ER  -