Challenges, Barriers, and Pathways to Effective Artificial Intelligence Integration in University Education: A Qualitative Case Study

This study aimed to identify the challenges, barriers, and possible solutions for successful AI integration in university education.Using a qualitative case study approach, the research identified multiple challenges: instructors and students' limited knowledge of AI and its effective use, resistance to change, privacy and personal data concerns, environmental impact worries, personal skepticism towards AI, issues of cheating and plagiarism, suppression of creativity and critical thinking, difficulty evaluating AI-generated information, overdependence on AI content, lack of clear guidance for educators and learners, evaluation and assessment challenges, outdated data, concerns about AI output accuracy and reliability, and limited information available in Slovak.The study also highlighted several strategies for overcoming barriers and effective AI adoption.In particular, it suggests the need for clearer policy frameworks, sharing of effective teaching practices, and increased emphasis on formative assessment, alongside support for faculty self-development and AI training for both educators and students, investing in AI at the institutional level, improving AI reliability, and enhancing methods for detecting AI use.The study highlights the importance of including AI literacy in educational policies and curricula, not just as a technical skill but as a crucial, multi-dimensional competency needed for effective teaching and learning in the AI age.These findings can help deepen the theoretical understanding of the