DOI: 10.18848/2325-128x/cgp/a360 ISSN: 2325-1298

Beyond Learner Classification

Chanumpa Dechnitirat, Nithit Keadtisuke, Benjamas Kutintara, Chaiyasit Dankittikul, Chumporn Moorapun, Phattranis Suphavarophas
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The rapid advancement of technology and Artificial Intelligence (AI) has profoundly reshaped design education. However, most Learning Style Assessments (LSAs) were developed in pre-digital educational contexts, creating a conceptual gap in explaining the complexities of learning dynamics in the digital era. This study examines research trends and LSAs in design education following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Relevant studies were identified through Scopus, Education Resources Information Center (ERIC), and Google Scholar, yielding thirty-one eligible articles. Findings revealed four research categories in learning style (LS) research, notably pedagogical adaptation and learning environments, which rose from 0% in Period 1 to 22.2% in Period 2. Eight types of LSAs were identified. The Learning Style Inventory (LSI) remains the most used instrument in design education, while increasing adoption of Visual, Auditory, Kinesthetic (VAK) and Visual, Auditory, Read/Write, Kinesthetic (VARK) models reflects growing attention to perceptual dimensions in digital learning, alongside a broader tendency toward combinations of LSAs. These trends converge on an emerging learning ecosystem framework, in which LS is understood as an emergent property of interactions among learners, technology, and context rather than a static classification. This review proposes a conceptual framework that repositions LS as a dynamic, ecosystem-embedded process and calls for developing integrative, context-responsive LSAs and testing this framework across diverse design education settings. Implications for educators include moving from one-time LS diagnosis toward ongoing, context-responsive pedagogy.

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