Enhancing Learner Engagement and Personalisation in AI-Powered Quiz Application through Adaptive Learning, Gamification, and Mobile Optimisation

Abstract

This dissertation investigates the integration of adaptive learning techniques, gamification elements, and mobile optimisation into SkillsDotAI, an AI-powered educational platform that dynamically adjusts question difficulty based on real-time user performance. The research addresses three core questions concerning adaptive learning implementation, gamification’s impact on engagement, and mobile accessibility in educational technology. Thesystem employs a sophisticated architecture built on Node.js/Express.js with PostgreSQL database integration, featuring a multi-stage difficulty adjustment algorithm that adapts question complexity across discrete learning phases. Central to the platform is an AI-powered feedback system utilising Claude 3 Haiku, which provides personalised learning guidance based on comprehensive session data analysis. Gamification elements, including achievement badges, global leaderboards, and progress tracking, are implemented to enhance user motivation and engagement. A comprehensive evaluation was conducted with 100 participants who interacted with both adaptive and competitive learning modes. Results demonstrate strong user recognition of adaptive features, with 77% of participants perceiving intelligent difficulty adjustments. Statistical analysis revealed significant positive correlations between perceived adaptability and overall satisfaction (r = 0.305, p = .002), and between feedback helpfulness and satisfaction (r = 0.577, p ≤ .001). The mobile design approach proved highly successful, with 79% of participants using mobile devices and strong positive correlations between mobile preference and satisfaction (r = 0.348, p ≤ .001). Keycontributions include empirical validation of transparent adaptive learning mechanisms, demonstration of relationships between adaptive features and AI-powered feedback, and practical frameworks for mobile-optimised educational technology development. The research provides evidence that users who recognise adaptive system behaviours report higher satisfaction levels, challenging assumptions about transparent versus hidden adaptation strategies. This work advances the field of AI in education by providing a robust technical framework for adaptive learning implementation, comprehensive evaluation methodologies for complex educational systems, and practical insights for developing engaging, accessible learning platforms