How AI Empowers the Double-Helix Approach: Examining a Decision Support Framework for Personalised Learning Trajectories in Gifted Education

Abstract

Artificial intelligence (AI) is expanding opportunities for adaptive and personalised learning that responds to learners’ abilities, interests, and educational needs. However, formal gifted education programmes may still offer limited opportunities for individualised learning pathways. In this study, gifted education programmes refer to formal provision for identified gifted students that supports differentiated depth, complexity, pace, challenge, enrichment, and talent development. This study proposes an AI-Supported Double-Helix Framework for Personalised Learning in Gifted Education and examines preliminary empirical support for its hypothesised relationships. The framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT), AI-Supported Adaptive Learning (ASAL), Personalised Learning Experience (PLE), and curriculum integration. Using a quantitative cross-sectional design, PLS-SEM was applied to data from 437 gifted secondary school students. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and measurement invariance. Performance Expectancy showed the strongest positive association with ASAL, which was positively associated with PLE. PLE was positively associated with perceived Double-Helix Framework Implementation (DHFI), which was subsequently associated with perceived Learning Effectiveness, Talent Development, and Future Readiness. A secondary multi-group analysis indicated that most structural relationships were comparable across gender groups. The study extends UTAUT by linking technology adoption factors with AI-supported adaptation, personalised learning, curriculum integration, and perceived developmental outcomes. It also situates these processes within the context of Education 4.0 through adaptive, data-informed, and future-oriented learning. However, the cross-sectional self-report design provides preliminary empirical support only for statistical relationships among the measured constructs. It does not establish the framework’s educational effectiveness or causal effects on learning. Longitudinal, experimental, behavioural, and performance-based studies are needed to evaluate its educational effectiveness and practical implementation.