Bridging tradition and technology: a theoretical framework for integrating handwriting and AI-enhanced digital stroke practice in online Chinese character instruction

Abstract

Purpose: The research fills the theoretical gap in integrating traditional writing with AI-assisted digital tools for teaching Chinese characters online, addressing whether traditional and AI-assisted teaching are competing alternatives or complementary approaches. This paper adopts a Research Synthesis and Framework Proposal design. Design/methodology/approach: Grounded in Embodied Cognition Theory, Cognitive Load Theory, the Technology Acceptance Model, and Self-Regulated Learning Theory, and developed through a systematic literature review, a framework named DTIF-OCCI is proposed, comprising a four-layer structure and three core dimensions that dynamically adjust handwriting-AI integration ratios across proficiency levels. The framework is validated through a secondary analysis of 16 published studies examining AI stroke recognition performance and handwriting instruction effects. Findings: Evidence from the reviewed literature indicates that AI stroke recognition technology has reached sufficient maturity for instructional application, but handwriting practice remains an indispensable complement for encoding sensorimotor memory. The staged integration plan progressively shifts from AI-heavy scaffolding in the initiation stage to handwriting-dominant practice at higher proficiency levels. Originality/value: This framework supplies technology integration guidelines, extends theories of embodied cognition to digital spaces, generalizes principles of technology-supported language learning to logographic writing systems, and offers a replicable validation approach based on secondary analysis of published literature, without requiring new data collection or ethical approval.