Speculative design is an emerging form of critical material engagement with possible futures. Designers working speculatively call attention to current and future sociotechnical dilemmas, and aim to provoke debate about the moral, political and ethical implications of sociotechnical innovation. Despite the popularity of speculative design and its presence in a variety of domains, there are very few resources that address it as a pedagogical practice. We attempt to fill this gap by presenting the structure, reasoning and outcomes of a graduate course on speculative design we taught during the academic year 2022-3. The article describes class activities and outcomes, discusses the benefits and challenges of teaching speculative design (especially in a design-engineering program), and concludes by identifying the most considerable obstacles awaiting those who want to integrate speculative design into the curriculum. As such, the article provides a useful resource for those interested in understanding the benefits of speculative design as a critical pedagogical practice, and for those who wish to bring that understanding into the classroom.

This paper introduces queering as a methodological intervention in human–AI entanglements, aimed at pluralizing the self and resisting normative algorithmic logics. Expanding the role of queerness in HCI beyond its traditional associations with gender and sexuality, we conceptualize queering as a relational, performative, and political disturbance that disrupts the interpellative forces of AI systems. Our exploration unfolds through Undoing Gracia, an autotheoretical experiment seeded with the first author’s autobiographical memories and values, in which Grace interacts with two digital twin agents, Lex and Tortugi, within the speculative world of Gracia. This multi-agent simulation probes the algorithmic borderlands of subjectivity, as the self is iteratively co-performed and transformed through interaction with the agents. Rather than focusing on technical intervention, the experiment explores co-performance, opening new directions for designing human–AI relations grounded in relationality, plurality, and speculative experimentation. The first author designed and performed the autotheoretical experiment, while the co-authors contributed to the theoretical articulation and critical analysis.