Bringing Artificial Intelligence Literacy Into Online Education: Machine-Learning Integration Through Geometry in K–12 Teacher Professional Development - Sung, Woonhee; Gunpinar, Yasemin (International Review of Research in Open and Distributed Learning)

(untitled) (Athabasca University Press (AU Press), 2026)
This study examined an online professional development program integrating artificial intelligence (AI) literacy into mathematics instruction through unplugged, explainable machine-learning activities. Ten K–12 educators created explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible and accessible without requiring complex software or technological tools. The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes. Findings demonstrated positive changes across all constructs, with participants’ AI self-efficacy increasing from below-moderate to above-moderate levels. Sentiment analysis revealed dramatic shifts from negative to positive perceptions of AI in education, with 30% of participants initially using negative descriptors versus 0% post intervention. Thematic analysis revealed three key outcomes: (a) AI concepts became explainable and learnable, (b) participants gained enhanced understanding of classification processes, and (c) participants valued the practical applicability of unplugged approaches. The study demonstrates that effective AI literacy education can be delivered through conceptual understanding rather than technological implementation, providing an accessible pathway for K–12 AI integration regardless of resource constraints.