Game-based learning (GBL) has emerged as a powerful instrument for enhancing student engagement and motivation in education. This paper evaluates the impact of Gamify-IT, an educational game designed for programming education, on student motivation and learning experiences. Building on the original Gamify-IT concept, we extended it with novel features, including diverse minigames, leaderboards, and a redesigned achievement system, to align with Marczewski's HEXAD player types. The platform was used in a first-semester programming course by about 100 students, and its impact was evaluated through comparative analyses of two cohorts in consecutive CS1 programming courses: one limited to traditional teaching methods and the other incorporating Gamify-IT. Our results demonstrate that Gamify-IT has a positive impact on student motivation, as evidenced by higher initial and sustained motivation levels throughout the semester. Feedback from a survey regarding the usefulness and experiences of the platform highlights its strengths, including an engaging and innovative learning approach, game design, and visuals, while also identifying areas for improvement, such as addressing technical issues and expanding game variety. Despite these challenges, Gamify-IT demonstrates the potential of GBL in programming education, offering valuable insights for designing inclusive and effective gamified educational tools for programming education.

Lecturers are increasingly attempting to use large language models (LLMs) to simplify and make the creation of exercises for students more efficient. Efforts are also being made to automate the exercise creation process in software engineering (SE) education. This study explores the use of advanced LLMs, including GPT-4 and LaMDA, for automated programming exercise creation in higher education and compares the results with related work using GPT-3.5-turbo. Utilizing applications such as ChatGPT, Bing AI Chat, and Google Bard, we identify LLMs capable of initiating different exercise designs. However, manual refinement is crucial for accuracy. Common error patterns across LLMs highlight challenges in complex programming concepts, while specific strengths in various topics showcase model distinctions. This research underscores LLMs' value in exercise generation, emphasizing the critical role of human supervision in refining these processes. Our concise insights cater to educators, practitioners, and other researchers seeking to enhance SE education through LLM applications.

The integration of Artificial Intelligence (AI) tech-nologies into educational settings has paved the way for inno-vative teaching and learning approaches. In Software Engineering (SE) education, using Unified Modeling Language (UML) diagrams is a fundamental teaching element for understanding complex software systems. This research addresses ChatGPT's ability to utilize UML class and sequence diagrams to create SE modeling exercises. We use ChatGPT to generate exercises based on the information from uploaded UML diagrams by analyzing textual UML representations such as Mermaid and graphical diagrams. The research explores ChatGPT's ability to synthesize UML-specific information from class and sequence diagrams, enabling the generation of various exercises tailored to strengthen conceptual understanding and practical application. Furthermore, we investigate generating graphical UML class and sequence diagrams based on natural language as input. By bridging the gap between AI -driven natural language understanding and the comprehension of UML diagrams, this study highlights the potential of ChatGPT to improve SE education. Our concise findings address educators, practitioners, and other researchers engaged in the field of SE education with a special focus on UML.

In recent years, artificial intelligence (AI) has been increasingly used in education and supports teachers in creating educational material and students in their learning progress. AI-driven learning support has recently been further strengthened by the release of ChatGPT, in which users can retrieve explanations for various concepts in a few minutes through chat. However, to what extent the use of AI models, such as ChatGPT, is suitable for the creation of didactically and content-wise good exercises for programming courses is not yet known. Therefore, in this paper, we investigate the use of AI-generated exercises for beginner and intermediate programming courses in higher education using ChatGPT. We created 12 exercise sheets with ChatGPT for a beginner to intermediate programming course focusing on the objects-first approach. We report our process, prompts, and experience using ChatGPT for this task and outline good practices we identified. The generated exercises are assessed and revised, primarily using ChatGPT, until they met the requirements of the programming course. We assessed the quality of these exercises by using them in our external teaching assignment course at the University of Education Ludwigsburg and let the students evaluate them. Results indicate the quality of the generated exercises and the time-saving for creating them using ChatGPT. However, our experience showed that while it is fast to generate a good version of an exercise, almost every exercise requires minor manual changes to improve its quality.