Artificial Intelligence in Learning Design: Acceptance, Perceived Effectiveness, and Barriers (Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM), 2025)
This study mapped perceptions of AI in learning design in the Educational Technology Study Program at Universitas Negeri Surabaya (UNESA). A 25-item, 5-point Likert questionnaire (acceptance, perceived effectiveness, limitations; TAM-informed) was completed by 16 lecturers and 130 students selected purposively (users of, or strongly interested in, AI). Content validity met conventional thresholds (all I-CVI ? 0.78; S-CVI > 0.90). Agreement on acceptance (10 items) averaged 82.6% for students (range 80.0–88.5%) and 85.0% for lecturers (range 81.25–87.5%). Agreement on perceived effectiveness (8 items) averaged 85.4% for students (range 80.8–89.2%) and 87.5% for lecturers (range 81.25–93.75%), indicating that respondents believe AI can accelerate material preparation, support adaptive/diagnostic feedback, and enable more personalized learning. Limits were also evident (7 items): difficulty understanding AI (65.4% students; 62.5% lecturers), context relevance of AI outputs (58.5%; 62.5%), curricular alignment (56.9%; 56.3%), feeling safe sharing data (53.9%; 56.3%), and LMS integration (60.8%; 68.8%). Reported training was uneven (61.5% students; 68.8% lecturers), implying roughly 32–38% lacked training. Given the single-site, descriptive design, findings are self-reports—not causal or broadly generalizable. Implications point to pilot-first adoption, targeted capacity building, clearer privacy/ethics governance, and infrastructure alignment before any scale-up.