Research seminars

8 December: Fred Martin, Deepti Tagare, Lucretia M. Fraga, Lijun Ni, Lin Zhu, Ismaila Temitayo Sanusi, Hailey Muñiz, Varsha Yerram, and Diane Schilder

From tool users to model builders: Integrating student learning of AI into interdisciplinary middle school inquiry

We present the design and study of a researcher-practitioner partnership (RPP) with two school districts — one in Texas and one in New York — where we work directly with middle school teachers to build foundational artificial intelligence (AI) fluency. We introduced 18 educators in science, maths, English language arts, and social studies to foundational concepts in AI and invited them to invent ways of bringing AI concepts to their students while deepening their required content-area teaching.

In a series of four Zoom online workshops, we introduced teachers to hands-on platforms, including Machine Learning for Kids and Google Teachable Machine. Teachers did mini-projects with these tools and then developed an AI-infused lesson that they brought to their students.

We’ll share findings from the project, including teachers’ innovative activities and our larger insights. Examples of teacher projects include a language-arts ‘Genre DNA’ project where students discovered that AI was easily fooled by typical terms used in literary genres; an image-based classification project in science that revealed model bias; and an AI ethics-focused Model UN simulation exploring the civic implications of algorithmic governance.

Finally, implications for scalable, equitable, and interdisciplinary approaches to AI education will be highlighted, emphasising the move from treating AI as a ‘magic box’ to an object of student critique.

Fred Martin is Professor and Chair of Computer Science at the University of Texas at San Antonio. Over his 30+ year career he has developed constructionist materials for children and teachers on topics such as robotics, data science, computing, and now AI education. 

Deepti Tagare is Assistant Professor in Learning Design and Technology at the University of Texas at San Antonio. She has a joint appointment in the Department of Computer Science. Her research focuses on preparing educators in K–12 and higher education for teaching computational thinking skills, and for providing AI literacy education and AI-integrated education.

Lucretia M. Fraga is a Professor of Instructional Technology at the University of the Incarnate Word, specialising in educator preparation, instructional design, and AI integration. With more than 30 years across K–12 and higher education, her scholarship and leadership focus on professional development, educator AI fluency, ethical implementation, and research-practice partnerships.

Lijun Ni is an Associate Professor of Computing Education at the University at Albany, State University of New York. Her research focuses on K–12 computer science education, teacher preparation and professional learning communities, AI and computational thinking, and broadening participation in STEM.

Lin Zhu is a PhD student at the University at Albany, SUNY, specialising in learning sciences and technology. Her research focuses on human interaction with emerging technologies, with her interests including digital game-based learning, AI education, and teacher preparation and professional learning.

Ismaila Temitayo Sanusi is with the University of Texas at San Antonio and specialises in computer science education research, including democratising AI and machine learning (ML) for young learners and novices.

Hailey Muñiz is a computer science undergraduate researcher at the University of Texas at San Antonio. Her research focuses on gamified and code-based AI education.

Varsha Yerram is a computer science undergraduate research assistant at the University at Albany, SUNY, minoring in cybersecurity. Her research focuses on K–12 AI education, AI tool design, and teacher preparation.

Diane Schilder is an evaluation consultant who has served as principal investigator and principal evaluator for computer science initiatives for nearly three decades. She leads formative and summative evaluations of STEM interventions, including AI education for the National Science Foundation and the US Department of Education, as well as for state agencies, universities, and school districts.