A Human-Centered Computing-Based Artificial Intelligence Implementation Strategy to Enhance User Interaction in E-Learning

Abstract

This study aims to analyze an artificial intelligence implementation strategy based on Human-Centered Computing to enhance user interaction in e-learning. The study responds to the growing use of AI in digital learning environments and the need to ensure that AI systems remain transparent, ethical, useful, and aligned with users’ learning needs. This research used a qualitative case study design at a higher education institution in Indonesia that had implemented a Learning Management System with AI-based features, including learning chatbots, automated feedback, material recommendations, and learning analytics. Data were collected through semi-structured interviews, limited participatory observation, and documentation. The participants consisted of students, lecturers, e-learning administrators, and information technology staff selected through purposive sampling and expanded through snowball sampling when needed. The data were analyzed using thematic analysis. The findings reveal five main themes: perceived usefulness of AI-assisted interaction, trust and transparency in AI feedback, user control and human supervision, digital readiness and institutional support, and the need for more human-centered AI design. These findings show that AI can improve e-learning interaction when it supports real learning tasks, provides understandable feedback, maintains human oversight, and receives adequate institutional support. The study contributes to Human-Centered Computing literature by showing that AI-supported learning interaction is not only a technical issue but also a pedagogical, ethical, and social process. Future research should examine AI-based e-learning across different institutions and learning contexts.