An AI-Based Adaptive Learning Framework for Strengthening Civic Competence and Digital Literacy among University Students - Hamidah Azzahrah S Lubis; Rizaldy Khair; Balqis Alhumairah (Universitas Islam Negeri Palopo, 2026)
This study developed and evaluated an AI-based adaptive learning framework intended to strengthen civic competence and digital literacy among university students at Universitas Muhammadiyah Sumatera Utara. The study responded to a limitation found in many adaptive learning systems, namely their dominant emphasis on academic achievement while rarely operationalizing civic-digital indicators as triggers for personalization. Using Design Science Research combined with mixed methods, the study was conducted through six iterative stages: needs assessment, co-design of indicators, instrument development and validation, prototype development, quasi-experimental implementation, and evaluation of usability, explainability, and fairness. The final framework consisted of five interconnected layers: diagnostic profiling, civic-digital analytics, adaptive recommendation engine, explainability and reflection, and governance and fairness control. The intervention involved 124 students in four classes, with two classes assigned to the intervention group and two to the control group. The results showed that the intervention group achieved stronger gains in civic competence and digital literacy than the control group. The prototype also obtained an acceptable usability score (SUS = 81.4; UMUX-Lite = 84.7) and its recommendation explanations were considered understandable by both students and lecturers. The study contributes a localized measurement instrument, a practical ethical-AI implementation blueprint, and an adaptive learning design that integrates academic support with responsible digital citizenship.