2026 ASEE Annual Conference & Exposition

AI-Play: A Framework for Teaching Artificial Intelligence Concepts through Unplugged Activities in Early Childhood Education

Presented at Computers in Education (CoED): Learning, Engagement & Inclusion (5 of 9) -- T308B

As artificial intelligence (AI) becomes increasingly embedded in modern society make it essential to introduce its literacy to young children to helps children develop accurate understandings of AI rather than misconceptions formed through uninformed exposure. However, most existing AI education materials rely heavily on digital technologies and abstract representations that are often unsuitable for early learners and difficult for parents and educators without technical backgrounds to implement effectively.

This paper presents AI-Play, a framework designed to make AI education more approachable for early childhood through unplugged or low-tech, play-based learning activities. The framework provides structured guidance for educators and parents to help children explore fundamental AI ideas through games, and hands-on experiences. Importantly, this approach is not only beneficial for young children and their families, but also offers an inclusive pathway for non-traditional learners and individuals from non-computer science backgrounds to understand AI concepts in an engaging and accessible manner. The framework also embeds key principles of AI ethics, safety, and responsible technology use, ensuring that learning occurs in a secure and age-appropriate environment.

The study outlines the framework’s design, grounded in early childhood learning theories and computational thinking research, and evaluates its usability through pilot implementations during Hour of Code family event. The event featured multiple interactive booths, each focusing on a specific AI concept. Data were collected through parent surveys assessing their confidence, understanding, and ability to replicate the activities independently at home. Additionally, children’s AI activity sheets were analyzed to evaluate concept comprehension and engagement.

Authors

Safia Malallah Kansas State University [biography]

Dr. Safia Malallah is a teaching assistant professor at Kansas State University, where she completed her Ph.D. in Computer Science and early childhood. Her research is dedicated to advancing computer science and data science education across the PreK-12 and undergraduate levels. Dr. Malallah is particularly passionate about designing innovative and accessible learning experiences that cultivate essential computational skills in students
Lior Shamir Kansas State University [biography]

Associate professor of computer science at Kansas State University.
Joshua Levi Weese Kansas State University [biography]

Dr. Josh Weese is a Teaching Associate Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students.
Russell Feldhausen http://orcid.org/0000-0001-7236-3933 Kansas State University [biography]

Russell Feldhausen received a bachelor’s degree in computer science in 2008, and a master’s degree in computer science in 2018, both from Kansas State University. He is currently pursuing a doctorate in computer science with a focus on computer science education, also at K-State. Feldhausen’s research interest is computer science education, targeting rural populations and exploring ways to integrate mastery learning into CS curricula. He is also actively involved in many K-12 outreach programs providing curricula and teacher training throughout Kansas.
Dr. Ejiro U Osiobe http://orcid.org/https://0000-0002-1107-6620 [biography]

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Friday Emmanuel James Kansas State University [biography]

Friday James is a PhD Candidate at Kansas State University. He has a double-majored Bachelor's degree in Statistics/Computer Science from University of Agriculture, Makurdi - Nigeria. He got a Master's degree in Statistics and a Master's degree in Computer Science from University of Ilorin - Nigeria and Kansas State University - Kansas USA in 2015 and 2021 respectively. His research interest cuts across the use of machine learning and data science in Computing Science Education to improve teaching and learning.

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