Workshop group selection

This 3‑hour tutorial invites advanced engineering students to rethink creativity through both engineering and artistic lenses. Instead of treating creativity as a purely human, artistic, or technical skill, it approaches it as a situated process shaped by constraints, materials, data, models, infrastructures, interpretation, and collective imagination (https://coda-madics.github.io). The goal is to reveal the shared foundations of artistic and scientific creativity: experimentation, abstraction, modelling, exploration, iteration, failure, and the production of new knowledge.

Methodologically, students will analyse creative processes using tools from computer science: problem framing, stakeholder and context analysis, situated data practices, reflexive design, and critical questioning. They will examine how creativity is influenced by available data, categories, models, computational infrastructures, and evaluation criteria.

Technically, the tutorial will draw on concepts from data management, modelling, querying, analytics, and AI. Students will explore how databases, metadata, provenance, knowledge graphs, similarity measures, clustering, classification, generative AI, and explainable AI can support or transform creative practices, and how technical choices make certain patterns visible while obscuring others.

The session will rely on concrete examples at the intersection of art, science, and computing. Students will compare an artistic creative process with an engineering design workflow, identifying common structures: formulating a question, building a corpus or dataset, choosing representations, exploring models, interpreting outputs, and communicating results. They will then design a short data-‑ and AI-driven creative inquiry, specifying data sources, queries, analytical methods, AI tools, evaluation criteria, and ethical or political considerations.

By the end, students will be able to describe creativity as a computationally mediated process, identify methodological bridges between engineering and the arts, and mobilise concepts from data management, analytics, mathematics, and AI to support creative inquiry. More broadly, the tutorial aims to help future engineers develop not only technical expertise but also the capacity to question, interpret, and responsibly design computational systems that shape artistic, scientific, and social creativity.

Goal and Value Settings represent a framework for aligning personal or organizational objectives with underlying principles that guide decision-making. By explicitly defining goals, individuals establish clear targets to pursue, while value settings ensure those targets reflect authentic priorities rather than external pressures. This alignment creates coherence between what we aim to achieve and why we believe it matters, reducing cognitive dissonance during challenging moments. Regularly reviewing and adjusting these settings allows adaptation to evolving circumstances without losing sight of core convictions. Ultimately, well-calibrated goal and value settings serve as both compass and anchor—directing forward movement while preserving stability when circumstances shift unexpectedly.

La neutralité désigne l’absence totale d’interférence avec le problème abordé. Parler des OGM, ne pas en parler, choisir ses arguments etc. rien de tout cela n’est très neutre en réalité. L’impartialité consiste à donner la parole à toutes les parties impliquées, syndicats, gouvernement ou encore ONG. Là encore la perfection est difficile à atteindre. L’objectivité renvoie à une connaissance sûre et certaine. Mais bon nombre de paramètres d’une controverse (volatilité, incertitude, ambiguïté, etc.) rendent cet objectif impossible à atteindre.

L’intention de cet atelier est d’aider à clarifier le positionnement individuel de chaque participant·e. L'atelier proposera des lectures, échanges critiques, et la résolution de cas pratiques en recherche et en enseignement mobilisant les concepts de neutralité, impartialité et objectivité.