Abstract:

Problem-based learning and case studies are increasingly established in higher education as effective pedagogical methods for achieving higher cognitive goals according to Bloom's taxonomy. These methods can be further improved with the help of artificial intelligence.

Problem-based learning involves presenting students with real, complex challenges or tasks that require critical thinking, analytical skills, and problem-solving. Students are encouraged to actively seek solutions, collaborate, and think beyond established frameworks. With the proper use of artificial intelligence, problem tasks can be customized for individual students, allowing flexibility and personalized learning. Teachers can adjust task difficulty levels, provide additional information or feedback, and monitor the progress of each student using artificial intelligence.

And case studies allow for hands-on exploration of real-life situations or scenarios, encouraging students to apply their knowledge to solve concrete problems. Using artificial intelligence, case studies can be adapted to the learners' current needs and allow them to simulate real-life environments. AI can help analyse data, recommend solutions and improve understanding of complex situations.

Through problem-based learning and case studies grounded in artificial intelligence, higher education teachers promote higher cognitive processes such as analysis, synthesis, evaluation, and creativity. Students become more active participants in the learning process and develop skills crucial for their future professional endeavors. This approach facilitates a deeper understanding of the material and the retention of knowledge, aligning with the key goals of higher education.

The article will showcase examples from the fields of veterinary medicine, engineering, and psychology, emphasizing the concepts of problem-based learning and case studies. These diverse disciplines provide varied insights into the application of these pedagogical methods and how they adapt to the specific needs and challenges of each discipline.

Keywords:

Problem-based learning, case studies, higher education, artificial intelligence.