Abstract:

The rapid rise of generative artificial intelligence (AI) tools is fundamentally changing teaching and learning processes in higher education. These tools offer personalized support, instant feedback, and assessment opportunities, but they also raise serious ethical and pedagogical questions, such as data privacy, algorithmic bias, academic integrity, and skill development challenges.

The aim of the presentation (in the Hungarian higher education context):
i) to examine the relationship between university lecturers' AI-literacy and their digital competence;
ii) to explore the attitudes, perceptions and practices of university teachers towards generative AI in the fields of education and research; and
iii) to formulate a practical typology and targeted professional development opportunities for each group based on these results. The research was conducted using a mixed approach.

In the first phase, we used a quantitative questionnaire research design. The sampling took place between January 30, 2024 and March 27, 2024, using an online, self-completed method, in Hungarian higher education institutions selected based on expert selection. A total of 1,103 Hungarian university lecturers participated in the study, coming from 13 different educational fields. The data were analyzed using correlation analysis. In the second, qualitative phase of the mixed-methods research, we conducted semi-structured interviews with 32 university teachers between May and September 2024. The data were processed using data-driven thematic analysis, in which two researchers' independent coding showed an 85% match, and thematic saturation was achieved after the 25th interview. The sample included 13 institutions and 7 fields of study. All ethical approvals were obtained, and participation was voluntary and anonymous.

The results showed that teachers' digital competence is positively correlated with AI literacy. This relationship suggests that teachers' ability to effectively use digital tools in teaching promotes the development of their knowledge and skills related to AI. On the other hand, they also showed that educators perceive the impact of AI use not only in their teaching methods, but also in student’s work, learning processes, and teacher-student relationships. We identified four characteristic types of attitudes and behaviors: AI optimists (proactive experimentation, focus on efficiency), AI skeptics (ethical and pedagogical caution, protection of the human dimension), AI pragmatists (demand for quick, practical benefits and institutional frameworks), and AI uncertains (technological anxiety, low self-efficacy). The types can be useful for planning targeted institutional interventions.

In conclusion, we recommend that institutions create a differentiated development environment that includes advanced workshops and mentoring programs for "Explorers," ethical dialogues and policy development for "Resistors," rapid, problem-centered training and subject-specific guidance for "Pragmatists," and individual mentoring and ready-made prompt sets for "Uncertain." In this way, the successful, ethical, and pedagogically adequate integration of AI can be linked not only to individual conditions, but also to the institutional ones.

Keywords:

Generative artificial intelligence, higher education, mixed method.