Abstract:

Higher education must equip students with skills for complex, multidisciplinary challenges. Traditional approaches relying on fixed deadlines and traditional exams often limit opportunities for growth and continuous skill development. This contribution presents an iterative and incremental teaching method, applied for five years in a row in a master-level Computer Science course on Human–AI Interaction. Our approach emphasizes formative feedback, collaborative learning, and individual progression. Students work on group assignments and an individual project, with no strict deadlines and unlimited opportunities during the semester to resubmit until a "pass" is achieved. Compact feedback sessions after each iteration serve both as assessment moments and teaching opportunities, clarifying expectations and guiding improvement. The method is grounded in mastery learning, formative assessment, and the High Impact Learning that Lasts model, fostering motivation and self-determination. Survey data and performance analysis of a study conducted two years ago, show positive effects on learning outcomes and student motivation: students valued the clarity of assessment, the removal of "one chance" exams, and the freedom to iteratively improve. Over five years of teaching, this approach has proven effective in balancing diverse prior knowledge, building applicable skills, and sustaining motivation during the semester. We conclude that incremental and iterative teaching constitutes a viable model for skill-oriented higher education, adaptable across contexts where collaboration, feedback, and progression are central.