Date
Publisher
arXiv
The deep integration of Artificial Intelligence (AI) into elementary mathematics education necessitates a conceptual tool capable of explaining students' cognitive transition from disciplinary knowledge to AI understanding. This study proposes a novel core concept, "Awareness of Technological Isomorphism, " defined as a student's metacognitive realization that their own mathematical cognitive operations (e.g., observing trends, inducing patterns, and making predictions) share an underlying logical structure with AI technical operations (e.g., pattern recognition and predictive modeling). This awareness, in turn, facilitates cognitive transfer from disciplinary mathematics to AI comprehension. Underpinned by transfer learning and metacognitive theories, this study clarifies the distinct essence of this concept from traditional "computational thinking." We demonstrate the explanatory power of this framework in two ways: elucidating the mechanism of students' cognitive leap from mathematics to AI, and guiding instructors to identify "isomorphic interfaces" within disciplinary curricula. On this basis, a three-stage pedagogical pathway--spanning "Perception, Comprehension, and Creation"--is constructed alongside a corresponding evaluation rubric. This framework is empirically validated through a case study based on the "Compound Line Graph" lesson from a fifth-grade mathematics textbook in China, offering a highly replicable operational framework for the deep convergence of disciplinary instruction and AI literacy education.
Study at a glance
- Question: How can teaching technological isomorphism help elementary students connect mathematical reasoning to AI concepts?
- Setting and data: Fifth-grade mathematics classroom, China; sample size not stated.
- Method: Design-based research using a classroom case study and discourse analysis.
Why it matters
AI remains a mere add-on in elementary mathematics, fostering use rather than understanding. Instruction lacks a conceptual framework to explain how students connect disciplinary mathematical reasoning with AI technical operations.
Key findings
- Dual-track comparative analysis prompted students to spontaneously express that AI identified data trends using logic comparable to their own manual estimation.
- Examining an AI prediction failure helped students recognize that models rely solely on historical data while human inference can incorporate unobserved contextual information.
- Post-lesson responses suggested students began viewing AI as an entity fundamentally grounded in mathematical structures rather than an enigmatic tool.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
