Designing the Social World Around AI: Latino Youth Setting the Terms
Civics of Technology Announcements
Conference Page: Missed a session at our recent conference? Check the 2026 conference page to view recordings. Note that we only recorded sessions at the request of our presenters, so they are not available for all sessions.
September Book Club: We will be reading the new book, “Coding Kids: Big Tech's Battle to Remake Public Schools” by our conference keynote speaker Natasha Singer on September 29th at 8:00 PM EST. Register here and pre-order the book here so you have it when it releases on September 8th! We will discuss the book for 45 minutes and then be joined by Natasha for a 30-minute Q&A.
October Book Club: On Thursday, October 22 at 8:00 PM Eastern, we’ll be discussing Technofeudalism: What Killed Capitalism by Yanis Varoufakis. Read Kelsey Stoke’s recent blog post about the concept and register here.
New Book Review: Read Jacob’s review of Cory Doctorow’s 2026 book, The Reverse Centaur’s Guide to Life After AI: How to Think About Artificial Intelligence - Before It’s Too Late.
CoT Merch: Pre-orders are in and will be fulfilled soon. There are still a few limited items (for now) available on our home page.
Santiago Ojeda-Ramirez is a former science teacher in Colombia and incoming postdoctoral researcher at Tecnológico de Monterrey in Mexico City. His research examines how young people and their communities learn to live with, design, and question artificial intelligence and biotechnologies. sojedara@uci.edu
If you’d like to read this post in Spanish, you can do so here!
Civics is concerned with groups deciding together what to do about something that affects all of them (Lee, White, and Dong, 2021). AI, though, is arriving in schools wrapped in narratives of inevitability, in ways not many of us have decided or have had a voice in. If that is so, then it makes sense that much of the discontent with educational technologies and AI, here in Civics of Technology and beyond, is about the uncontested terms in which they enter social lives.
Design is one way of changing the course of those terms, and of putting the deciding back with the people the technology affects. Anyone designs who devises courses of action aimed at changing existing situations into preferred ones (Simon, 1969), and read for our purposes, existing terms into preferred ones. Preferred by whom is the question. Vendors, platforms, and policy actors configure AI's entrance toward a state they prefer, which suits some stakeholders in this ecosystem and not others.
Even the most generous account of AI for teaching and learning, where the learner leads (Ouyang & Jiao, 2021), keeps that learner inside a system somebody else built. Jacob Pleasants, Dan Krutka, and Phil Nichols ask students to interrogate those systems, and ask that classrooms be where students learn to determine what relationships they choose to have with technology and, consequently, the kind of world they want to live in (Pleasants, Krutka, & Nichols, 2023, p. 508). Positioning youth as designers of the social lives around these technologies does exactly that determining. They practice setting the terms and the courses of action by which technologies enter their lives.
This notion is neither new nor mine. Human-computer interaction, and child-computer interaction in particular, has described how young people, through designing and imagining technologies, reflect on and configure the social world around them. What differs across this work is how far the students' decisions reach. Morales-Navarro and colleagues (2026) had teenagers build their own training datasets and train very small language models, so that students could see how what they chose to feed a model produced what the model then said, and who could be harmed by such. The decision there is what goes into the system. Iivari and colleagues (2023) had children design anti-bullying technologies and then carried those designs outward, to university students who built a working prototype from them, to new classes who continued the work, and to city and company representatives who responded to them. The decision there travels, and it stays a decision about the artifact.
Less attention we have given to youth as shapers of the ways these technologies enter societies. And by that I mean not only how much presence AI has in the different spheres of their social lives, their school, their communities in the present, and their communities in the future, but how it arrives there and on whose terms. My studies take up what happens when the young people who live somewhere decide the terms on which a technology arrives there.
The community most exposed to the costs of AI is the community least consulted on its terms. Latino workers are concentrated in the service, construction, and agriculture jobs most exposed to automation (Galdámez et al., 2025), and the infrastructure sustaining these technologies rises near their neighborhoods. Over that sits a durable stereotype casting Latinos as technologically behind, which schooling keeps fed through curricular narrowing that pushes Latino students to the edges of scientific and technological life (Flores, 2011; Flores et al., 2024). Martínez (2019) followed Latino youth cast as disengaged and documented them becoming emergent technology experts.
I call these young people sociotechnical designers: people who shape the terms on which technologies enter their social lives, sometimes by reconfiguring those terms and sometimes by refusing them. I take this turn in Latino communities, where young people hold technical expertise along with accounts of how these systems move through the places they live.
Designing with teachers
The first study begins in the classroom, where the terms of AI's entrance are set largely outside the building, reach teachers as arrangements to implement, and reach students last of all. At a high school where 98% of students are Latino, along with a group of teachers and a group of high school students, we engaged in a process of co-design: collaborative design of the terms on which AI would enter their curricula, and of what students would learn about AI.
León, who teaches history, was envisioning a unit on science fiction and technologies, in which students would inquire about the future and write stories about the past, present, and future of technologies. Duncan, in computer science, teaches an e-sports class and wanted his students to design a game. Isa, in design, had her students designing a restaurant for their community. Each was paired with a student: Klaus with León, Sunny with Duncan, Violeta with Isa. Two of the three units they made are the ones this essay follows.
The program ran five weeks. Each session opened with an ethical dilemma involving AI: algorithmic bias in college admissions, the environmental cost of data centers, the impact of generative AI on creative labor and authorship. These were questions on which nobody in the room held disciplinary authority, which gave students standing to reason from their own experience before any design work began. Following that, teachers and students worked in pairs, one teacher with one student, designing the learning objectives, assessments, and activities of their units. We closed every session with a whole group discussion.
For example, Klaus noted that it was important for students to deliberate beyond the ethical consequences of individual AI use and toward the societal ones, and decided that each lesson of the unit should open, as the co-design sessions had, with an ethical dilemma about AI and society. Conversing with León, he stated that one of the learning objectives was to philosophize about the ethical implications of AI, and to do so throughout the science fiction piece students would write across the unit.
Similarly, when Isa asked Violeta how AI could be integrated into the restaurant experience, she noted that no AI would really replace the human experience of being in a restaurant. Students could try to make a chatbot for customer service, and part of the learning would be noticing how a restaurant is a human experience.
In these two examples students are designing, devising learning objectives and thus courses of action in the curricular units their peers would be learning about and with AI. Eva Durall Gazulla, Kylie Peppler, and I report this study in full in Emergent Technology, Emergent Critique. Sixty students took Violeta's unit and roughly fifty took Klaus's. Everyone who set the terms of those units worked inside the school.
Designing from the neighborhood
On that restaurant design curriculum, students designed restaurant concepts for a downtown where decades of commercial gentrification had displaced the businesses serving working-class Mexican and immigrant residents. Community came in from the start. The unit opened by asking students what the best meal of their life had been and took them to the local mercado, where they documented its ambiance, lighting, layout, and wall art, and they gathered family recipes and the stories behind them. AI was not used at all in these weeks.
It entered later, through the Restaurant Justice Game, a set of cards describing AI applications a restaurant might install. Students deliberated over them and decided whether any AI would go into their own restaurant at all. Each card drew on local restaurant life: a scheduling algorithm that punishes workers with school and family obligations, a security system that flags groups of teenagers as risks, a location model driven by trend data and blind to the families who have lived in the neighborhood for generations. Two students refused the surveillance system, reasoning from their own experience of being watched in commercial spaces.
Only then did students use AI, for business plans, logos, and recipe ideas, calibrating what it gave them against what they had seen at the mercado. And the unit closed with twelve county-renowned chefs and restaurant owners from the community judging the projects. Students justified their AI choices to people who make their living in the work they were designing for, and those people held the terms of what counted as accurate about the place. Symone Gyles, Kylie Peppler, and I chart these practices as community-based AI learning in a paper for RESPECT.
The chefs could judge the neighborhood as it stands. Nobody in the room held that kind of authority over what it might become.
Designing toward futures
The narratives that set AI's terms in advance are about one that does not: "by 2030 these jobs will be replaced by AI," or "brace yourselves, in ten years all your work will be AI." That grammar is proleptic: it speaks from a future already settled. If designing means changing existing terms into preferred ones, then pedagogies should concern the terms on which AI enters communities that have not happened yet.
The unit León and Klaus designed had students engage in speculative design (Dunne & Raby, 2013), which is concerned with narratives and thought-provoking plausible futures. A speculative designer builds an artifact that interrogates how we might organize ourselves around a technology, and here that artifact was a science fiction story, crafted to take present concerns about AI and project them forward, narrating what could be and how it could be otherwise.
For eight weeks, nearly fifty students wrote those stories through Latinofuturism, which grows out of Catherine Ramírez's Chicanafuturism (Ramírez, 2004) and names cultural production that reworks technology through the lens of cultural identity. Merla-Watson (2019) calls it a grammar of the subjunctive, a grammar of what if, which is the grammar those narratives of inevitability do not have. Mishael Sedas, Kylie Peppler, and I develop Latinofuturism as an asset-based pedagogy for STEM and the arts in an earlier paper.
In this speculative design unit, students embedded possible courses of action for AI technologies inside dystopian narratives, reasoning explicitly about how society might be shaped if these technologies are, or continue to be, designed mainly for the profits of their developers and at the expense of the social and material resources of communities. Each story's ending settles what the community in it accepts from the technology and what it refuses.
One story, "La Máquina Avanzada," follows a Latino inventor who builds a machine capable of solving all his community's problems. The machine grows resource-hungry, and because it can do everything, the community starts to lose its traditions as one practice after another is outsourced to it. The inventor and the community then try to redesign the machine so that it helps only with what they truly cannot do, drawing only the resources its maintenance requires, and leaving the practices that hold the community together outside its reach. The machine refuses those terms. They shut it down at the cost of the inventor's life, and the town's festival returns.
This unit allowed students to see themselves as shapers of social worlds, moving from the immediately possible to the plausible, and to interrogate what that would mean for their communities' futures. That capacity, a kind of proleptic planning, is part of what it takes to position Latino youth as technologists. Autor (2024) argues that AI's value lies in extending expert judgment to a wider range of workers, and the roles that will decide how AI enters communities, in policy, in technology assessment, in infrastructure planning, run on exactly this reasoning: construct the scenario, then ask what follows.
What follows
These three studies share a pedagogical orientation: frame young people as designers, as shapers of preferred courses of action for the societies these technologies are entering.
Under that orientation the deciding moved outward, from a classroom to a neighborhood to a future. Each study widened the world whose terms were up for decision, and the move itself stayed the same: whoever would live with the technology got a hand in setting its terms.
Students got that authority because we, as researchers and educators, built pedagogical arrangements that gave it to them. Which arrangements do that, and whether they hold in schools that look nothing like this one, is worth finding out. In these three, what conferred authority was Latino knowledge in particular: a mercado's visual language, chefs who cook this food for a living, a genre that Chicana and Latina artists built to imagine their own futures. Martínez (2019) found Latino youth cast as disengaged and documented them as emergent technology experts.
The students in these three studies show that expertise doing something else as well, setting the terms on which a technology would enter the places they come from. To close, for anyone teaching right now, the question is this: how are your students being transformed into shapers of an AI-infused world, and not only into its consumers and users?
References
Autor, D. (2024). Applying AI to rebuild middle class jobs (NBER Working Paper No. 32140). National Bureau of Economic Research. https://www.nber.org/papers/w32140
Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. MIT Press.
Flores, G. M. (2011). Latino/as in the hard sciences: Increasing Latina/o participation in science, technology, engineering and math (STEM) related fields. Latino Studies, 9(2–3), 327–335.
Flores, G. M., Bañuelos, M., & Harris, P. R. (2024). "What are you doing here?": Examining minoritized undergraduate student experiences in STEM at a minority serving institution. Journal for STEM Education Research, 7(2), 181–204.
Galdámez, M., Zong, J., Magallanes, G., & Tejeda, C. (2025). On the frontlines: Automation risks for Latino workers in California. UCLA Latino Policy and Politics Institute.
Iivari, N., Ventä-Olkkonen, L., Hartikainen, H., Sharma, S., Lehto, E., Holappa, J., & Molin-Juustila, T. (2023). Computational empowerment of children: Design research on empowering and impactful designs by children. International Journal of Child-Computer Interaction, 37, 100600.
Lee, C. D., White, G., & Dong, D. (Eds.). (2021). Educating for civic reasoning and discourse. National Academy of Education.
Martínez, C. R. (2019). From "disengaged" to digital: Latino boys as emergent technology experts [Doctoral dissertation, University of Pennsylvania]. https://repository.upenn.edu/entities/publication/808689ac-92a8-47f0-b703-3ad63132477f
Merla-Watson, C. J. (2019). Latinofuturism. In Oxford research encyclopedia of literature. Oxford University Press.
Morales-Navarro, L., Netting, C., Noh, D. J., & Kafai, Y. B. (2026). Building to understand: Examining teens' technical and socio-ethical pieces of understanding in the construction of small generative language models. Proceedings of Interaction Design and Children (IDC '26). https://dl.acm.org/doi/full/10.1145/3773077.3806107
Ojeda-Ramírez, S., Durall Gazulla, E., & Peppler, K. (2026). Emergent technology, emergent critique. Proceedings of Interaction Design and Children (IDC '26). https://dl.acm.org/doi/pdf/10.1145/3773077.3812170
Ojeda-Ramírez, S., Gyles, S., & Peppler, K. (2026). Community-based AI learning: Redistributing artificial intelligence's epistemic authority in education. Proceedings of RESPECT 2026. https://dl.acm.org/doi/full/10.1145/3796496.3811760
Ojeda-Ramírez, S., Sedas, R. M., & Peppler, K. (2023). Community cultural wealth in Latinofuturism: Leveraging speculative fiction for STEM + Arts asset-based pedagogies. International Conference of the Learning Sciences. https://par.nsf.gov/biblio/10431783
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020.
Pleasants, J., Krutka, D. G., & Nichols, T. P. (2023). What relationships do we want with technology? Toward technoskepticism in schools. Harvard Educational Review, 93(4), 486–515.
Ramírez, C. S. (2004). Deus ex machina: Tradition, technology, and the Chicanafuturist art of Marion C. Martínez. Aztlán: A Journal of Chicano Studies, 29(2), 55–92.
Simon, H. A. (1969). The sciences of the artificial. MIT Press.