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.

Industrial assembly is shifting toward human-robot collaboration (HRC) to leverage the complementary strengths of both agents. However, traditional task allocation referred to as the Robotic Assembly Line Balancing Problem (RALBP) remains labor-intensive and often lacks transparency. We introduce DELEGACT, a framework designed to produce workable, intelligible human-cobot task allocations. The framework uses a Vision-Language Model (VLM) to extract atomic operations from expert demonstration videos, then employs a Large Language Model (LLM) to delegate these tasks based on robot specifications, operator competencies, and material definitions. We provide a proof-of-concept prototype and preliminary testing on illustrative cases. Results demonstrate the system's ability to reason about complex constraints such as precision, weight, and ergonomics. This paper illustrates how off-the-shelf foundation models can automate HRC decision-making via a human-in-the-loop paradigm while preserving operator agency and understanding.

We propose DIVERSE, a framework for systematically exploring the Rashomon set of deep neural networks, the collection of models that match a reference model's accuracy while differing in their predictive behavior. DIVERSE augments a pretrained model with Feature-wise Linear Modulation (FiLM) layers and uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search a latent modulation space, generating diverse model variants without retraining or gradient access. Across MNIST, PneumoniaMNIST, and CIFAR-10, DIVERSE uncovers multiple high-performing yet functionally distinct models. Our experiments show that DIVERSE offers a competitive and efficient exploration of the Rashomon set, making it feasible to construct diverse sets that maintain robustness and performance while supporting well-balanced model multiplicity. While retraining remains the baseline for generating Rashomon sets, DIVERSE achieves comparable diversity at reduced computational cost.