Adaptive User Interfaces: A Systematic Literature Review

I Gusti Ngurah Darma Paramartha (Department of Information Technology, Universitas Pendidikan Nasional, Denpasar, Indonesia)
Md. Wira Putra Dananjaya (Department of Digital Business, Universitas Pendidikan Nasional, Denpasar, Indonesia)
Adie Wahyudi Okatavia Gama (Department of Information Technology, Universitas Pendidikan Nasional, Denpasar, Indonesia)
Gusi Putu Lestara Permana (Department of Accounting, Universitas Pendidikan Nasional, Denpasar, Indonesia)

Jurnal Krisnadana
Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026

Adaptive User Interfaces (AUIs) dynamically adjust interface elements based on user behavior, context, and preferences to enhance usability and performance. This systematic literature review, conducted following PRISMA 2020 guidelines, synthesizes evidence from 44 studies across five major academic databases. The review examines methodologies, adaptation techniques, implementation platforms, and the impact of AUIs on user experience. Results demonstrate that machine learning—particularly reinforcement learning and deep learning—dominates adaptation techniques and consistently yields superior task performance (6.67–27.3% improvement over static interfaces). Mobile applications and web interfaces are the most prevalent deployment platforms. Key challenges include predictability, privacy, cognitive load management, and user autonomy. Future research should prioritize explainable AI integration, standardized evaluation frameworks, and longitudinal studies.