Integration of Artificial Intelligence and Human-Computer Interaction in Digital Learning Systems to Improve the Quality of Student Interaction

Abstract

This study aims to analyze the integration of artificial intelligence and human-computer interaction in digital learning systems to improve the quality of student interaction. The study addresses the growing use of AI-based features in higher education, including chatbots, automated feedback, learning recommendations, and digital learning management systems. A qualitative case study approach was used to explore students’ experiences, perceptions, and interaction patterns in using AI-supported digital learning platforms. Data were collected through semi-structured interviews, non-participant observation, and documentation. The participants consisted of 15 to 20 students from different study programs, 3 to 5 lecturers, and one platform administrator selected through purposive sampling, with snowball sampling used when additional relevant informants were needed. Data were analyzed using reflexive thematic analysis through transcription, repeated reading, initial coding, theme development, theme review, and interpretation. The findings reveal four main themes: AI as immediate academic support, interface clarity as a condition for meaningful interaction, trust and control in AI-assisted learning, and the transformation of student interaction from passive access to active engagement. These findings show that AI can improve student interaction when supported by clear interface design, lecturer guidance, ethical rules, and reflective learning activities. The study contributes to AI-based education research by emphasizing the importance of human-centered design in digital learning. Future research should examine AI and HCI integration across broader institutional and disciplinary contexts.