Reconstructing a Digital Learning Model Based on Artificial Intelligence and User Experience to Support Higher Education Transformation

Nabila Puspitasari; Rudi Azzahra; Fitri Gunawan

Authors

Nabila Puspitasari Universitas Pendidikan Indonesia Author
Rudi Azzahra Universitas Pendidikan Indonesia Author
Fitri Gunawan Universitas Negeri Malang Author

Keywords:

Artificial intelligence, Digital learning, Higher education Transformation, User experience

Abstract

This study aims to reconstruct a digital learning model based on artificial intelligence and user experience to support higher education transformation. The study addresses the growing use of AI in academic learning, while also examining problems related to platform usability, ethical uncertainty, student dependence, lecturer readiness, and institutional governance. This research used a qualitative case study approach involving students, lecturers, learning management system administrators, academic technology staff, and academic leaders at higher education institutions that have implemented digital learning systems and AI-supported academic services. Data were collected through semi-structured interviews, non-participant observation, and institutional documentation. The data were analyzed using thematic analysis through transcription, open coding, theme development, interpretation, and conceptual model construction. The findings reveal five major themes: AI as a learning support system, user experience as a condition for digital learning acceptance, personalization as a new learning need, ethical tension in AI use, and institutional readiness for digital transformation. The study proposes six components for an AI and UX-based digital learning model, namely user needs analysis, AI-supported learning functions, user experience design, pedagogical integration, ethical governance, and continuous evaluation. The study contributes to digital learning theory by integrating AI-enabled learning, user experience, technology acceptance, and human-centered design. The findings imply that universities need clear AI policies, lecturer training, student AI literacy, accessible platforms, and continuous quality evaluation to support responsible digital learning transformation.

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