Civic Urban AI: Fostering Transformative Pedagogies for Human-Urban-AI Entanglements

Data, platforms and algorithms have become part of the urban infrastructure and ecosystem in many cities throughout the world. ‘Smart city’ systems use advanced software algorithms to support the delivery of various services under the umbrella term urban AI. Urban AI models predict and govern urban life and entail a shift from automation, where agency is distributed and co-dependent, to autonomous urban systems where agency is outsourced. Examples include pattern recognition (image, sound) used for surveillance cameras and noise detection systems, language processing used for municipal chatbots, or robotics in automated vehicles and mobility systems. One problem with this ‘synthetic’ modeling (e.g. through LLMs) is that it ultimately produces reductive urban knowledge based on what already exists and what it is trained on, often at the expense of street-level civic agency, improvisation, innovation, and mutuality. Because of recursive feedback loops it is ultimately a regressive kind of learning. Second, it suggests the possibility of lab-produced and purportedly a-political technologies to act ‘outside-in’ on messy urban reality. It ignores the agency of people and cities, and the ongoing ‘dirty’ work of actually producing and maintaining urban AI systems (and the wide-ranging harms this produces). ‘Synthetic’ urban AI deployment thus involves a gross simplification of both outcomes and processes of learning together and serves to strengthen dominant tech-centric discourses.¹About urban AI, see Michael Batty, “The Emergence and Evolution of Urban AI,” AI & Society 38, no. 3 (2023): 1045-1048; Aale Luusua, Johanna Ylipulli, Marcus Foth, and Alessandro Aurigi. “Urban AI: Understanding the Emerging Role of Artificial Intelligence in Smart Cities,” AI & Society 38, no. 3 (2023): 1039-1044; and Frederico Cugurullo, Federico Caprotti, Matthew Cook, Andrew Karvonen, Pauline MᶜGuirk, and Simon Marvin. “The rise of AI Urbanism in Post-Smart Cities: A Critical Commentary on Urban Artificial Intelligence,” Urban studies 61, no. 6 (2024): 1168-1182. In our view, we need a better conceptualization of how AI systems learn in ‘synthesis’, that is, together in entangled ways with humans and cities.

The triadic relationship between humans, cities and AI is currently highly lopsided. In terms of agency, this fuels three actionability gaps. First, the ever-increasing prevalence of autonomous decision-making pushes humans out of the loop of city governance. Second, ongoing systemic surreptitious datafication and algorithmic processing of urban environments, implies absence of possibilities for consent, or for individuals to “opt out”. Third, people lack a civic voice and power to co-shape desirable urban futures by learning about, for, and with – or even without – AI. This triple actionability gap, which we discuss in more detail in the second part of this contribution, essentially describes the crisis we identify around civicness born out of the simplistic push for synthetic urban AI solutions. The governance of urban life is delegated to AI by stealth through the increasing encroachment of statistically driven imaginaries into civic debate. As an alternative we propose a counternarrative to the “synthetic city” and its fetishization, where agency in today’s cities is instead considered in terms of co-evolving entanglements between humans, urban environments, and algorithmic technologies: a condition of ‘synthesis’, of co-evolution, mutual dependency and co-constitution that still respects the individuality and agency of each part. This fundamentally situated interrelatedness between AI, humans and (urban) environments allows us to re-inscribe agency within diverse urban actors and actants, in what we refer to as Civic Urban AI. We do so by developing perspectives on an ever-expanding type of transformative learning (instead of a reductive learning) happening in pedagogical relationships between AI, humans and cities.

AI-Human-City Entanglements

Several decades of Humanities scholarship on relational and more-than-human perspectives of cohabitation have produced a spectrum of critical accounts of technology in which pluriversal perspectives stand as counterpoints to human-centered models. Accordingly, within the field of HCI perspectives on the roles and relations of ‘humans’ and ‘computers’ have come to include the physical environment as a third agent in this relationship. Comber, Lampinen and Haapoja note that “interaction is a problematic notion in a world where human-technology relationships are no longer limited to foreground mediation via tangible, visible interfaces but revolve around background contextual processes like datafication, requiring little to no (bi-directional) interaction at all.”²Rob Comber, Airi Lampinen, and Jesse Haapoja, “Towards Post-Interaction Computing: Addressing Immediacy, (un) Intentionality, Instability and Interaction Effects,” Proceedings of the Halfway to the Future Symposium 2019 (2019): 1-8 Indeed, human-computer encounters are complicated by a diversity of other actors, including elements of the built and more-than-human environments. This implies that technological mediation – and by extension the co-constitution of agency between actors – occurs not solely between humans and technologies but is also shaped by physical situations and (urban) contexts. In a similar vein, Frauenberger argues for taking down the dualism between human and computer and the related anthropocentrism associated with traditional HCI discourse, thus moving past “user-centered design” and the “design of user experiences”.³Christopher Frauenberger, “Entanglement HCI The Next Wave?” ACM Transactions on Computer-Human Interaction (Tochi) 27, no. 1 (2019): 1-27. Frauenberger especially draws on Barad’s notion of intra-action, which inverses the ontological primacy of a priori discrete elements engaging in interactions and instead proposes to consider relationships as formative of differential identities.⁴Karen Barad, Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning (Duke University Press, 2007). Frauenberger proposes Entanglement HCI as a new approach that shifts the focus of design from interactions and experiences to materially discursive and performative configurations. In this view, humans and their things are ontologically inseparable. Referring to this conceptual shift as Human-Computer Intra-Action, Hespanhol proposes a relational take on the design process of digital technologies through the notion of ‘relationalities’, where the triad of human-computer-world is seen as a dynamic assemblage made up of synergies and antagonisms, expressed through socio-technical engagements in time and space.⁵Luke Hespanhol, “Human-Computer Intra-Action: A Relational Approach to Digital Media and Technologies,” Frontiers in Computer Science 5 (2023): 1083800. Hayles proposes a similar relational perspective and argues for an ‘integrated cognitive framework’ that extends our views of doing cognitive work to include non-human beings and computational media in cognitive assemblages.⁶N. Katherine Hayles, Bacteria to AI: Human Futures with Our Nonhuman Symbionts (The University of Chicago Press, 2025).

AI-human-city entanglements open the door to conceptualize learning together, where boundaries, arrangements and agency between various actors are continuously and mutually redefined and reconfigured as ‘be(com)ing with AI’.⁷Felicity Colman, Vera Bühlmann, Aislinn O’Donnell, and Iris van der Tuin. Ethics of Coding: A Report on the Algorithmic Condition (Brussels: European Commission, 2018), https://cordis.europa.eu/project/id/732407. AI-human-city entanglements have particular relevance for questions about ethics, responsibility, and civic agency in cities. Civic agency has a vast theoretical legacy from which three perspectives stand out. First, a sociological understanding associated with the work of Goffman and Lofland looks at symbolic interactions in public spaces as forms of everyday civicness.⁸Erving Goffman, The Presentation of Self in Everyday Life (Doubleday, 1956); Lynn Lofland, A World of Strangers: Order and Action in Urban Public Space (Basic Books, 1973). Second, a law and governance understanding entails the institutionalized “civil rights” and duties that come with being a citizen in what Somers calls “the right to have rights.”⁹Margaret R. Somers, Genealogies of Citizenship: Markets, Statelessness, and the Right to Have Rights (Cambridge University Press, 2008). A third perspective anchors civicness in a normative philosophical tradition revolving around “the right to the city”, as famously phrased by Lefebvre, and emphasizes active collective participation and civic duties.¹⁰Henri Lefebvre, “The Right to the City,” in Writings on Cities (Blackwell, 1996), 147-159. It is this last perspective that we adopt here, and extend to also include more-than-human actors and actants as bearing this right to the city. Here is where we see the most consequential disruption posed by urban algorithms: the concealment of city governance, the reliance on stealth logic for decision-making, and the suppression of awareness about mechanisms to actively learn and participate. Citizens can still engage in civic interactions and arguably sustain their civil rights yet are rendered increasingly passive toward the city as a techno-socio-political ecosystem.

With respect to civic agency, we argue that urban AI introduces three actionability gaps. First, humans are taken out of the loop in human-computer interactions as a result of the push for autonomous decision making, predictive modelling, deep learning, etc. Initially, HCI-based society was defined by computer automation, in which algorithms were expressions of human social vernacular: a programmed execution of steps that in theory could be taken by human beings and reflected human logic and social rules. A post-HCI society, by contrast, is characterized by computer logical thinking, the ability of algorithms to (quasi-)autonomously develop their own procedures, decisions, understanding and conclusions following (supervised or unsupervised) machine learning. Second, citizens are effectively becoming resources for – rather than users of – AI. Training, classification and synthesis of new information based on previously learned data happens away from the interfaces used to gather data from the environment and act upon it. In other words, most of the ‘intelligence’ is hidden and largely kept inaccessible from end users. Data from end users may be harvested, often passively and without their knowledge. Moreover, they are mostly on the receiving end of decisions made by AI-trained systems. People unwittingly (and often unwillingly) are datafied and suffer the consequences, for example in the case of workplace monitoring software. Third, citizens are ignored in public consultations about developing, deploying and governing urban AI with civic public values in mind. AI is typically left to “the experts,” with rhetorical and visual tropes such as the complexity and unavoidability of AI-based urban futures, or depictions of an anthropomorphized superior intelligence, in effect serving to bypass citizens.

These actionability gaps thus weaken civic agency in terms of jointly learning with, about, and for urban environments, and diminish the right to the city. Our view on AI-human-city relationality serves the dual purpose of critically examining the below-the-surface entanglements that drive urban AI, while considering the civic and pedagogical dimensions of this entanglement. In the following, we want to suggest a further step for imagining and fostering learning about, for, and with (or without) AI, within a fundamentally entangled condition of Urban Civic AI.

Transformative Learning for Civic AI-Human-City Entanglements

Overcoming this challenge to civicness requires more than just learning about how AI works, making sure it functions accurately, or adopting it ethically. Admittedly, “explainable AI” could lead to more insight and help make AI more civic. Yet too much faith in transparency and explainability may also act as a form of risk exorcism and serve corporate narratives of unavoidable AI futures. It would not fundamentally tackle the above-mentioned civic actionability gaps. We therefore propose a pedagogical grounding of Civic Urban AI, engaging with the normative challenge of humans, cities, and AI systems to engage in entangled reciprocal learning to the benefit of all, instead of tech companies only. We take inspiration from the notion of transformative learning, which considers learning as a process where both learning subject and learning object continually evolve.¹¹John M. Dirkx, “Transformative Learning Theory in the Practice of Adult Education: An Overview,” PAACE Journal of Lifelong Learning 7 (1998): 1-14. Rooted in various constructivist pedagogical approaches, transformative learning emphasizes two-way exchanges and mutuality between the topic of learning and the development, self-growth, and empowerment of learning subjects, grounded in individual and collective reflection. Learning, from this perspective, is a form of intra-action, a dynamic interplay between an assemblage of cultures, media and socio-technical infrastructures in the perpetually “learning city.”¹²Giota Alevizou and Photini Vrikki, “A Learnt City: The Mediated, Affective, and Experiential Layers of London,” Societies 15 (2025): 253, https://doi.org/10.3390/soc15090253. It transforms both learner and knowledge itself through the development of new perspectives and, what Haraway calls, response-able engagements with the surrounding environment.¹³Donna J. Haraway, Staying with the Trouble: Making Kin in the Chthulucene (Duke University Press, 2016).

When applied to urban AI, such a transformative pedagogy underscores joint forms of learning that strengthen civic values and agency. It counterbalances Big Tech and State actors’ dominance in pushing computational urban futures and allows us to consider AI as entangled in evolving and agentic normative/political relationships with cities and humans. We propose three avenues for thinking about Civic Urban AI as a transformative pedagogical challenge, highlighting a synthesis between each of the actors in AI-human-city entanglements: 1) transformative learning with a focus on response-able AI learning, 2) transformative learning with a focus on citizen agency, and 3) transformative learning with a focus on city-wide experiments and lessons. This is supported by two illustrative cases.

First, we consider how AI-focused learning for cities may happen in entangled civic ways. Millions of “ghost-workers” worldwide are involved in training AI.¹⁴“WageIndicator,” WageIndicator Foundation, accessed August 21. 2026,https://gigpedia.org/resources/blogs/2025/wageindicator-ghost-work-the-invisible-world-of-work-behind-ai. This training/learning dynamic is problematic for many reasons, including the parasitic use of creative works, copyright infringement, the spread of mis/disinformation, bias and selection, western-centeredness, data/AI colonialism, labor extractivism and precariousness, moderator trauma, wasteful energy expenditure, high barriers to entry, and so on.¹⁵For this critique, see Karin van Es and Dennis Nguyen, “‘Your Friendly AI Assistant’ The Anthropomorphic Self-Representations of ChatGPT and its Implications for Imagining AI,” AI & Society 40, no. 5 (2025): 3591-3603. Currently, AI learns by being trained with historical data from which it builds probability models, while humans correct and teach the model (also known as reinforcement learning from human feedback, or RLHF).¹⁶Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. “Deep Reinforcement Learning from Human Preferences,” Advances in Neural Information Processing Systems 30 (2017). New data may be fed back into the model, updating the distribution of probabilities and improving its learning. This learning is one-directional. It is only aimed at improving statistical models producing plausible enough output to be regarded as intelligent by humans, without humans themselves learning and developing in tandem. This is similar to what Dirkx calls “instrumental learning,” where like a one-way street “knowledge is generally viewed as something outside of the learner to be taken in through the learning process,” as a “means of adapting to the needs and demands of the broader, socio-cultural context.”¹⁷Dirkx, “Transformative Learning Theory in the Practice of Adult Education,” 2. By contrast, transformative learning in entangled Civic Urban AI systems must be developed to be continually open to reciprocal learning based on contextualized interactions with citizens and urban systems. Civic Urban AI then becomes response-able in the sense of responding to the ever-changing needs of the environment it is part of, instead of valorizing autonomy above everything else.

An example of response-able AI learning has been the use of natural language processing to support te reo (Māori language) learning and preservation.¹⁸Karen Hao, „A New Vision of Artificial Intelligence for the People,“ Pulitzer Center, April 22, 2022, https://pulitzercenter.org/stories/new-vision-artificial-intelligence-people. The project sought to reverse the downward spiral of te reo, after over a century of native language repressive politics, exacerbated by digital platforms promoting western language and culture. Today’s LLMs are trained on huge datasets that are only available for big dominant languages. This marginalizes native languages even further. To revive te reo, researchers began digitizing archived recordings of spoken language and created a speech recognition model to help transcribe interviews with remaining te reo speakers. They used the help of beginning and intermediate speakers to help train the language model that could transcribe recordings. A community-owned infrastructure had to be built as well in order to be independent from large platforms and achieve data sovereignty.¹⁹Tahu Kukutai and Donna Cormack, “‘Pushing the space’: Data Sovereignty and Self-Determination in Aotearoa NZ,” in Indigenous Data Sovereignty and Policy (Routledge, 2020), 21-35. This case brings together many threads: criticism about western Big Tech logics, indigenous resistance to cultural- and data colonialism, exclusion and marginalization, the practice of “teaching the teachers”, data sovereignty, community curation and ownership. Most relevant for our purposes is that it underlines an important distinction for assessing truly response-able AI learning, between localization, where local trainers, languages and even local cultural contexts are considered during RLHF but the fruits of AI learning still benefit Big Tech, and indigenization, where AI learning is made useful for, and owned by, local communities.²⁰Jason Edward Lewis, Hēmi Whaanga, and Ceyda Yolgörmez,“Abundant Intelligences: Placing AI within Indigenous Knowledge Frameworks,” AI & Society 40 (2025): 2141–2157 https://doi.org/10.1007/s00146-024-02099-4.

Second, the lens of relational transformative learning also offers the contours for citizen-focused learning about AI. In discussing the increasing adoption of generative AI in educational institutions, Conrad argues for the need of critical AI literacy for educators and students, including both technical understanding of how generative AI works and social implications, limitations and potential harms – while acknowledging that this literacy cannot be expected to be gained without institutional support.²¹Kathryn Conrad, “A Blueprint for an AI Bill of Rights for Education,” Critical AI 2, no. 1 (2023), https://doi.org/10.1215/2834703X-11205245. While explainable AI may be valuable, urban AI as a constantly evolving systems technology may just be too complex for the average individual citizen to become fully ‘literate’ about them, just like it would be unreasonable to advocate explainability as a pre-condition for responsible usage of electricity infrastructures. By contrast, we want to underscore the importance of relational pedagogical approaches. For example, strategies like controversing or contestation of urban technologies allow for collective civic debates and learning, while acknowledging that these ever-changing technological systems can never be grasped in their entirety.²²Corelia Baibarac-Duignan and Michiel de Lange, “Controversing the Datafied Smart City: Conceptualising a ‘Making-Controversial’ Approach to Civic Engagement,” Big Data & Society 8, no. 2 (2021): 20539517211025557; Kars Alfrink, Ianus Keller, Gerd Kortuem, and Neelke Doorn. “Contestable AI by Design: Towards a Framework,” Minds and Machines (2022), https://doi.org/10.1007/s11023-022-09611-z.

A transformative pedagogy means approaching AI-human-city entanglements in an attitude of criticality²³Irit Rogoff, “Smuggling – An Embodied Criticality,” European Institute for Progressive Cultural Policies, August 2006, https://web.archive.org/web/20080709002055/http://eipcp.net/dlfiles/rogoff-smuggling. or “thinking with”²⁴Nanna Verhoeff, Urban Screens: Situations, Practices, Concepts (Amsterdam University Press, 2025)., in order to engage in continual reflection on its suggestions and decisions while at the same time not denying its potential contributions. Civic expressions may be enriched by AI contributions to the thinking process yet should still be subjected to critical evaluation of its potential flaws, contradictions, biases, and propensities for errors – like any civic actor would. This is similar to the argument of educator Paulo Freire, where learning should lead to the subversion of systems of oppression through the development of critical consciousness.²⁵Paulo Freire, Pedagogy Of The Oppressed (Seabury Press, 1970). Left unchecked, algorithms become a system of oppression in their own right.²⁶Gerald C. Kane, Amber G. Young, Ann Majchrzak, and Sam Ransbotham. “Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants,” Mis Quarterly 45, no. 1 (2021): 371-396. Learning about living with AI thus entails learning to do so in a state of criticality, not taking AI outputs as facts but as opinions to be counterbalanced through continuous civic engagement.

Third, we wish to highlight city-focused learning, where cities learn with and about AI. This often happens in urban so-called living labs or other infrastructural spaces for experimentation. Here, “multiple stakeholders jointly test, develop and create solutions addressing complex metropolitan challenges”²⁷“Our Living Labs: creating impact for Amsterdam,” Amsterdam Institute for Advanced Metropolitan Solutions, accessed August 21, 2026, https://www.ams-institute.org/how-we-work/ull/our-living-labs/. but there is little room for controversy and contestation. Civic society is rooted in democratic participation and the right of citizens to have a say about issues that may affect them. However, contestability without focus, scattered across a multitude of AI-human-city entanglements, may become an ineffective cacophony – and could reinforce trends toward social fragmentation and civic disengagement weakening representative democracies. While active collegial debate at the level of the individual can be managed within neighborhoods and small communities, to scale direct deliberation up to the entire city is impractical and expect citizens to “do their own research” regarding the impacts of AI and discuss every single topic before decisions can be made would paralyze urban governance. Indeed, there is an inherent tension between the fundamentally intertwined AI-human-city entanglements emerging from intra-actions, and the outside position of an external overseer, controller, custodian, or pedagogue of such an entanglement. A continually oscillating perspective between inside and outside, between “thinking-with” and “thinking-about” AI, may serve as a form of criticality and reflection-in-action²⁸Donald A. Schön, The Reflective Practitioner: How Professionals Think In Action (Basic Books, 1983). through which citizens take up civic response-ability.

An example of transformative learning of citizens about and with AI, and of entangled city-focused learning and the importance of controversy for civic debate, can be seen in the case of Amsterdam Municipality developing an “AI” model to combat welfare fraud.²⁹“Unprecedented access to high-stakes algorithmic experiment tests promise of Ethical AI,” Lighthouse Reports, published June 11, 2025, https://www.lighthousereports.com/investigation/the-limits-of-ethical-ai; Eileen Guo, Gabriel Geiger, and Justin-Casimir Braun, “Inside Amsterdam’s high-stakes experiment to create fair welfare AI,” MIT Technology Review, June 11, 2025, https://www.technologyreview.com/2025/06/11/1118233/amsterdam-fair-welfare-ai-discriminatory-algorithms-failure. The municipality had piloted a “Responsible AI” algorithm called “Smart Check” in an attempt to maximize fairness. From the various reports based on joint investigations by Lighthouse, MIT Tech Review and Dutch newspaper Trouw, it appears that the municipality never managed to get rid of bias and that the algorithm was not very effective, even after having spent hundreds of thousands of euros, consulting experts, doing bias tests, building technical safeguards and asking for feedback from the people potentially affected and modifying the initial algorithm to combat bias. So, they pulled the plug. Relevant for our point about transformative learning about AI is that the municipality remained open to learn from actual ongoing ‘intra-actions’ with not only the AI system they developed, but also with the city and civil society this system was to bear on. The municipality was keenly aware of recent scandals involving biased algorithms in the Netherlands and beyond. Controversy and contestation thus in this case were important dynamics for transformative learning.

Synthesis/Conclusion

This contribution proposes the concept of Civic Urban AI as a spotlight to focus on “learning together” in situated entanglement of AI, urban environments and human agency. We argue that any potential benefits of synthetic machine learning in ’smart cities’ has to align with a more ‘synthesized’ situated urban pedagogical praxis. The concept of Civic Urban AI challenges dominant tech-centric discourses that exclusively locate agency within ‘smart’ AI-driven techno-urban systems. We suggest widening our perspectives on urban AI beyond ‘synthetic’ but ultimately reductive urban knowledge generation and cybernetic forms of governance, which we consider as coming at the expense of more relational street-level forms of civic agency and learning. Through identifying three ‘actionability gaps’ we developed a counternarrative where civic agency emerges from intra-actions between AI, humans, and (urban) environments that are fundamentally situated and entangled. For urban AI to become truly civic, we argue, the synthesis of humans, cities and AI systems requires new ways of learning together in transformative pedagogies. These transformative pedagogies underpinning Civic Urban AI are critical to foster democratic approaches to urban governance, rooted in transparency, accountability, citizen agency – including civic controversy – and significantly contribute to trust on the mechanisms of decision- and policymaking. Ultimately, this proposal suggests a civic “middle ground” between laissez-faire market-driven Big Tech and top-down state regulated AI for and in cities. With this perspective of Civic Urban AI comes the task of learning about, from and with AI. Embracing this, actors in the entangled constellation of AI-humans-cities emerge and transform together by learning from each other to the benefit of all instead of the few.

Luke Hespanhol

Associate Professor Luke Hespanhol is the Head of Design at the School of Architecture, Design and Planning, University of Sydney. His research includes social design, civic participation, storytelling, cities, digital placemaking, technology-mediated social interactions, and cross cultural education. Luke has pioneered mobility programs in the Indo Pacific, and contributed to public dissemination of research on societal impacts of technology and design of sustainable futures. Luke holds a certificate in Dispute Resolution by Harvard University, USA, and a fellowship from the Social Impact Hub, Australia.

Michiel de Lange

Michiel de Lange is an Associate Professor in New Media Studies at the Department of Media and Culture Studies, Utrecht University. His research specializes in (mobile) media technologies and urban culture. He is the co-founder of the [urban interfaces] research platform and chairs the Special Interest Group “Inclusion in the Datafied City” at Utrecht University. He has (co-)lead various funded research projects, including “Designing for Controversies in Responsible Smart Cities” (2019-2022) and “The Hackable City” (2014-2017). He is currently co-lead in the NWO-KEM funded project URRRRRban: Urban Resilience through Responsive, Relational, Representative and Responsible Policy-making.

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Nanna Verhoeff

Nanna Verhoeff is Professor of Screen Cultures & Society at Utrecht University. She specializes in developing concepts and methods for the analysis of performative technologies and situated arts and media. She is co-lead of the [urban interfaces] research platform and the Creative Humanities Academy in Utrecht. She published a trilogy on screen cultures – Urban Screens: Situations, Practices, Concepts (2025), Mobile Screens: The Visual Regime of Navigation (2012) and The West in Early Cinema: After the Beginning (2006) with Amsterdam University Press. With Iris van der Tuin, she published Critical Concepts for the Creative Humanities (2022) with Roman & Littlefield.

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