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This paper presents the design and expert validation of a modular adaptive learning architecture, integrating personalization, user experience (UX), gamification, and generative artificial intelligence (GAI). The architecture is composed of 5 components: content layer, GAI, UX, gamification, and adaptative layer. These components collaboratively facilitate learner-centered personalization, enhance user engagement, and provide actionable feedback for instructors, thereby creating a dynamic and personalized learning experience. To assess the conceptual soundness and usability of the architecture, a design-based research approach was adopted. Domain experts interacted with a physical 3D prototype of the system. Data were collected through System Usability Scale (SUS) scores, Likert-scale ratings, open-ended questionnaires, and field observations. Results show high perceived value in UX, GAI, and gamification components, with averages scores of 3.56, 3.38, and 3.31, respectively, indicating strong expert agreement on their functionality, clarity. To illustrate the practical potential of the architecture, a future application scenario is proposed, involving the deployment of the system in an online course on GAI at an ecuadorian university. This projection demonstrates the feasibility of delivering complex content through adaptive and engaging learning experiences tailored to diverse learner profiles.

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Toward an Integrated Architecture for Personalized Learning: Combining Generative AI, Gamification, and UX

  • 会议论文

  • 发表日期：

    2026年

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  • Hernán Ordoñez-Calero,
  • Omar G. Bravo-Quezada,
  • Julián Galindo-Losada

摘要

This paper presents the design and expert validation of a modular adaptive learning architecture, integrating personalization, user experience (UX), gamification, and generative artificial intelligence (GAI). The architecture is composed of 5 components: content layer, GAI, UX, gamification, and adaptative layer. These components collaboratively facilitate learner-centered personalization, enhance user engagement, and provide actionable feedback for instructors, thereby creating a dynamic and personalized learning experience. To assess the conceptual soundness and usability of the architecture, a design-based research approach was adopted. Domain experts interacted with a physical 3D prototype of the system. Data were collected through System Usability Scale (SUS) scores, Likert-scale ratings, open-ended questionnaires, and field observations. Results show high perceived value in UX, GAI, and gamification components, with averages scores of 3.56, 3.38, and 3.31, respectively, indicating strong expert agreement on their functionality, clarity. To illustrate the practical potential of the architecture, a future application scenario is proposed, involving the deployment of the system in an online course on GAI at an ecuadorian university. This projection demonstrates the feasibility of delivering complex content through adaptive and engaging learning experiences tailored to diverse learner profiles.