‘Today’s world is a world of machines. We live among machines, they help us with everything we do in our work and recreation. But what do we know about their moods, their natures, their animal defects, if not through arid and pedantic technical knowledge?’ (Munari, 1938)

This chapter takes an auto-ethnographic stance to reflect on my experiences as a design researcher, education designer, and educator working withstudents, and in collaboration with a generative AI system. It asks how design education can prepare students for technological futures that unfold faster than our vocabularies can describe them. Artificial Intelligence dominates today’s headlines; tomorrow it may be more analogue, embodied technologies such as memory weaving, atmosphere capture, or emotional prosthetics. The chapter reflects on a bachelor course Making Sense: Embodied Practices for Collective Futures, in which third-year art and design students from the Willem de Kooning Academy in Rotterdam explored their own social ecologies; webs of relationships, flows, and interdependencies that structure practice and life. Through embodied exercises, transplantation into unfamiliar contexts, and small speculative experiments, students learned to situate vulnerability, loss, and adaptation at the centre of design research. A central objective was to help them develop their own research questions and (theoretical) frameworks, rather than inherit mine uncritically, and to recognize that frameworks themselves are always reframed. Alongside this, I examine my collaboration with ChatGPT as a nonhuman co-author. Auto-ethnographic reflection makes visible my uncertainties, projections, and resistances in this partnership. The chapter argues that utopian or dystopian futures will not be decided by technologies alone but by how designers learn to situate them within ecologies of practice and meaning. As Munari suggested, machines can become works of art. applied design research, I believe, holds the potential to realize this transformation, cultivating futures where machine-learning technologies are not monsters to be feared but collaborators in creating more thoughtful, embodied, and collective ways of making.