Development of AI-Based Algorithm Learning Media with a Personalized Learning and Mediated Learning Experience Framework - Eki Nugraha; Salman Haykal Ramadhan; Eki Nugraha (Universitas Negeri Malang, 2026)
Algorithm learning in Informatics at Phase E requires media that can adapt to the individual needs of students. However, most conventional media remain static and one-directional. This study develops an AI-based algorithm learning medium that integrates the principles of personalized learning and Mediated Learning Experience (MLE) using the ADDIE model. It also evaluates usability through the System Usability Scale (SUS) and thematic analysis involving 22 tenth-grade high school students. The MLE principles intentionality, meaning, transcendence, and competence are translated into system features through a Socratic AI dialogue architecture. The average SUS score is 77.39 (SD = 12.87; 95% CI [71.68, 83.10]), which falls within the Acceptable range (Grade B–C). Thematic analysis identifies five main themes, with appreciation for content personalization (41%) and technical constraints (36%) emerging as the most prominent findings. The main contribution of this study is conceptual and design-oriented. It proposes a framework that maps personalized learning and MLE principles into concrete features within AI-based educational media.