Bridging Math and Machine Learning through Concreteness-Fading and Generative AI Tools

• In Event: Innovative Design for Learning and Human Flourishing

Abstract

As artificial intelligence becomes embedded in daily life, foundational literacy in machine learning (ML) is increasingly essential for all students—not just future computer scientists. This study presents a curriculum integrating ML into high school mathematics using a concreteness-fading instructional framework and generative AI (GAI) tools. In a two-day pilot with 25 students, participants explored image classification through physical, visual, and symbolic representations before building their own classifiers using GAI. Quantitative results showed significant improvement in ML knowledge, while qualitative feedback indicated increased self-efficacy and engagement. Though students encountered some technical challenges, GAI lowered barriers to entry. Findings suggest that embedding ML into math instruction can foster deeper understanding and interest, with implications for scalable, equitable AI education in K–12 settings.

Authors

• Hyunju Oh, University of Florida

• Rui Guo, University of Florida

• Chenglu Li, University of Utah