Awareness of Technological Isomorphism: Integrating AI into Elementary Mathematics Teaching on Data and Prediction,A Case Study of the Compound Line Graph
2026-06-08 • Computers and Society
Computers and Society
AI summaryⓘ
The authors propose a new idea called "Awareness of Technological Isomorphism," which helps students understand that the way they think about math problems is similar to how AI works with data. This awareness helps students move from just learning math to understanding AI concepts more easily. They explain this idea using learning and thinking theories and show how teachers can use it to connect math lessons with AI learning. The authors tested their idea with a fifth-grade math lesson in China and created a clear teaching plan and evaluation method that others can use too.
Artificial Intelligence (AI)MetacognitionTransfer LearningComputational ThinkingCognitive TransferMathematics EducationPattern RecognitionPredictive ModelingPedagogical FrameworkIsomorphism
Authors
Li Li, Yu Cao
Abstract
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.