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Perceived Platform Design and Learning Transfer Readiness in Simulation-Based AI Education

  • Industry Promotion Research
  • Abbr : IPR
  • 2026, 11(4), pp.285~300
  • DOI : 10.21186/IPR.2026.11.4.285
  • Publisher : Industrial Promotion Institute
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : October 3, 2026
  • Accepted : October 20, 2026
  • Published : October 31, 2026

Hye-Sook Ha 1,  Seung-In Kim 1

1홍익대학교

Accredited

ABSTRACT

This study examined associations between perceived platform design and learning transfer readiness in simulation-based AI education. Participants reviewed an introductory video and 12 Figma prototype screens without coding or interacting with live AI. Cross-sectional survey data included 1,031 responses for primary analysis and 892 for sensitivity analysis. Thirty-five items measured design, psychological dimensions, and readiness across 11 factors. Structural equation modeling used multiple-testing adjustment, bootstrapping, and alternative estimators. Self-efficacy and situational interest were positively associated with readiness across four analyses. However, self-efficacy and task transfer readiness lacked discriminant validity (HTMT = .954), and indirect associations were sensitive to estimation and screening. Findings may inform designers’ support features, students’ reflection, and educators’ guidance, but establish neither actual transfer nor causal effects. Independent measurement validation, randomized controlled trials on an operational platform, objective post-tests, delayed assessments, and workplace follow-up are needed.

Citation status

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