Abstract:

The use of digital and AI-supported learning tools has become integral to students’ learning processes and significantly influences how students construct knowledge and engage in self-regulated learning. This study explores how students describe their use of digital tools and artificial intelligence (AI) in their learning, focusing on the opportunities and risks they perceive and critically reflect on. The findings aim to support educators and higher education institutions in fostering the development of students’ digital competencies.

The theoretical foundation of this study is the European reference framework for digital competencies (DigComp 2.2), which was expanded in 2022 to include AI-related examples. The model distinguishes five key areas of digital competence: information and data literacy, communication and collaboration, digital content creation, safety, and problem solving.

The empirical data are based on qualitative semi-structured interviews conducted with bachelor’s students in business studies at a Swiss University of Applied Sciences and Arts and were analyzed following a structured qualitative content analysis approach as proposed by Kuckartz.

The qualitative analysis shows that students identify various opportunities in using AI tools in their learning process. Students no longer perceive AI tools merely as technical instruments, but rather as interactive co-learners or sparring partners. These tools support dialogic interaction in idea generation, structuring, linguistic revision, and exam preparation, thereby strengthening self-regulation, time management, learning motivation, and perceived efficiency.

At the same time, students express several risks. They are concerned about overreliance on AI tools, which they fear may negatively affect their independent thinking, as well as about potential academic misconduct. While they demonstrate awareness of risks related to inaccurate or imprecise AI outputs, their sensitivity to data protection issues remains limited - particularly regarding the implications of uploading copyrighted materials into AI tools. In collaborative group work, students tend to trust the quality of individually produced contributions rather than establishing shared and quality standards.

In relation to the DigComp 2.2 framework competence areas, the results indicate that competencies related to functional and productive use scenarios are relatively well developed, whereas competencies in data protection, copyright, and collaborative quality assurance remain comparatively weaker.

These findings underscore the need to design teaching and learning settings that specifically guide students toward a reflective and responsible use of AI, addressing not only cognitive but also ethical, legal, and collaborative dimensions of AI competencies.

Keywords:

Education, technology, artificial intelligence (AI), AI literacy, AI in student learning, AI competencies, digital skills, qualitative study.