Artificial intelligence; cooperative learning; case study; programming; questionnaire; teaching

Programming education needs to respond to the current demands on programmers' skills and knowledge, which are changing rapidly due to the development of artificial intelligence and machine learning. There is now a need to emphasise skills in analysis and subsequent problem solving, rather than simply producing code that can be largely automated and left to artificial intelligence. Key skills for programmers include the ability to analyse and design solutions for programs, to write, read and modify code, and to use appropriate tools. And not just the ability to use development tools, but also artificial intelligence and machine learning tools. The education system needs to respond to these dynamic changes, and it is appropriate to use new, modern and more appropriate teaching methods.This paper discusses the use of group work, cooperative learning, learning by error and artificial intelligence in teaching programming. Cooperative learning is one of the communicative approaches that use small group work with an emphasis on positive interdependence and responsibility of the group and the individual, shared leadership and group feedback. In cooperative situations, higher productivity is usually observed than when people work independently. The ability to cooperate is also seen as a key skill in practical life. For these reasons, cooperative learning is an appropriate modern pedagogical approach, but it is less used in schools than it should be. This paper describes the results and experiences of case studies that have been implemented in programming education. Students in small groups carried activities in which they had to solve problems, analyses and create new programs together. The results of the case studies and experiments show that when cooperative approaches are used properly, students are usually more motivated, more focused on solving problems and tend to perform better than individuals. The paper will also include an analysis of students' experiences with the use of artificial intelligence in code generation and the results of a questionnaire survey in which students evaluated the use of collaborative approaches.

Programming education needs to respond to the current demands on programmers' skills and knowledge, which are changing rapidly due to the development of artificial intelligence and machine learning. There is now a need to emphasise skills in analysis and subsequent problem solving, rather than simply producing code that can be largely automated and left to artificial intelligence. Key skills for programmers include the ability to analyse and design solutions for programs, to write, read and modify code, and to use appropriate tools. And not just the ability to use development tools, but also artificial intelligence and machine learning tools. The education system needs to respond to these dynamic changes, and it is appropriate to use new, modern and more appropriate teaching methods.This paper discusses the use of group work, cooperative learning, learning by error and artificial intelligence in teaching programming. Cooperative learning is one of the communicative approaches that use small group work with an emphasis on positive interdependence and responsibility of the group and the individual, shared leadership and group feedback. In cooperative situations, higher productivity is usually observed than when people work independently. The ability to cooperate is also seen as a key skill in practical life. For these reasons, cooperative learning is an appropriate modern pedagogical approach, but it is less used in schools than it should be. This paper describes the results and experiences of case studies that have been implemented in programming education. Students in small groups carried activities in which they had to solve problems, analyses and create new programs together. The results of the case studies and experiments show that when cooperative approaches are used properly, students are usually more motivated, more focused on solving problems and tend to perform better than individuals. The paper will also include an analysis of students' experiences with the use of artificial intelligence in code generation and the results of a questionnaire survey in which students evaluated the use of collaborative approaches.