Collective Urban Intelligence

Phase I: Problem Immersion & Theoretical Foundation (Weeks 1-4) Focus: Moving from general concepts to a well-defined "problem space." Students Critically Evaluate (LO1) dominant models. Week 1: Introduction – Smart Cities vs. Collective Urban Intelligence (CUI). Deconstructing the "Smart City" narrative. Introduction to the CUI framework: Linking data with local wisdom. Week 2: The Critique – Data Extractivism & Algorithmic Bias. Activity: Case study analysis of failed smart city projects (identifying where they excluded vulnerable populations). Focus: Diagnosing structural failures (LO3). Week 3: Methodologies of the Margins. Lecture on participatory mapping, storytelling, and digital platforms. Activity: Analyzing how collective wisdom is mobilized in African vs. European contexts. Week 4: Defining the Problem Space. Formation of project groups. Activity: Problem-framing exercises using Root Cause Analysis (The 5 Whys) to select a specific urban challenge to address. Phase II: The Solution Space (Weeks 5-7) Focus: Brainstorming core practices and tools. Students Apply (LO2) methods to generate concepts. Week 5: Envisioning Alternatives. Guiding Question: What must a good solution do? Activity: Brainstorming sessions to identify practices that operationalize CUI (e.g., Is it a digital tool? A policy charter? A roadmap?). Week 6: Design Science Research Methodology. Introduction to the "Artifact" concept. Setting the success criteria for the seminar's outputs. Week 7: The Concept Pitch. Groups present their initial concept for peer feedback. Milestone: Approval of the project direction. Phase III: Design & Development Sprints (Weeks 8-11) Focus: Hands-on creation. Students Design and Prototype (LO4) their artifacts. Week 8: Sprint 1 – Low-Fidelity Prototyping. Teams draft the skeleton of their artifact (e.g., wireframing the app, outlining the charter chapters, sketching the roadmap). Focus: Translating theory into tangible structure. Week 9: "Testing, Breaking, and Rebuilding." Activity: Rigorous Peer Evaluation. Groups swap artifacts to identify potential ethical risks or biases. Focus: Applying critique to strengthen outputs. Week 10: Sprint 2 – High-Fidelity Refinement. Incorporating feedback to finalize the artifact. Ensuring the solution is grounded in evidence (Data/Literature). Week 11: Preparation for Defense. Structuring the argument. Activity: Mock Q&A sessions to prepare for the oral examination (defending against questions on feasibility and ethics). Phase IV: Final Assessment (Week 12) Focus: Integration and Defense (LO5). Week 12: Final Presentations & Oral Defense. Exhibition of the final Artifacts. 30-minute Group Presentation and Q&A (The Oral Examination). The oral defence also asks about some details from the submitted written report. Note: The Individual Reflection Paper is due 1 week after the final presentation.

After successful participation in the module, students are able to: Formulate precise definitions and conceptual models of Collective Urban Intelligence (CUI) by synthesizing data sources, local knowledge, and power-sensitive theoretical perspectives into a structured conceptual framework. Apply CUI methods to real urban contexts by conducting participatory mapping, narrative elicitation, and platform analysis to demonstrate how collective wisdom is generated, shared, and operationalized in cities. Diagnose and critically evaluate barriers to inclusive urban intelligence by systematically analyzing cases of data extractivism, algorithmic bias, and unequal knowledge infrastructures using predefined analytical criteria. Design and develop context-specific CUI scenarios comparing different cities by constructing detailed use cases, workflows, and implementation pathways that articulate how CUI practices could operate in concrete urban settings. Co-produce a defined urban intelligence artifact such as: a CUI ethics charter, a CUI conceptual model, a CUI practice roadmap, or a city-specific CUI case prototype, through iterative team-based design sprints using evidence-based design methods.

The assessment for this module is a Portfolio Examination consisting of three interconnected parts. This format ensures constructive alignment between the practical design skills and theoretical critical analysis defined in the Learning Outcomes (LOs).

1. The Group Project (The Artifact) (35%) Students work in teams to Design and Prototype a specific socio-technical intervention. Consistent with LO 4, valid formats for this submission are restricted to: a digital mock-up, a policy charter, or a strategic stakeholder roadmap. Mapped Learning Outcomes: Tests the ability to Apply participatory research methods (LO 2) and Design/Prototype a concrete solution (LO 4). Assessment Criteria: Methodological Application: Did the group successfully apply specific methods (e.g., counter-mapping, citizen science tools) in the creation of the artifact? Relevance & Feasibility: Does the prototype address a specific, diagnosed problem in the chosen African or European context?
2. Individual Reflection Paper (3 Pages / ~1,000 words) (30%) Submission due 2 weeks after the block seminar. Each student must submit an individual paper that situates their group's artifact within the course reading list. Mapped Learning Outcomes: Tests the ability to Critically Evaluate epistemological differences (LO 1) and Diagnose structural failures like algorithmic bias (LO 3). Assessment Criteria: Theoretical Integration: Does the student explicitly link their practical work to concepts from the reading list (e.g., Acuto, Bibri, D'Ignazio)? Critical Diagnosis: Does the paper go beyond description to diagnose why their specific intervention is necessary to combat issues like data extractivism or exclusion?
3. The Oral Presentation & Defense (30 Minutes) (35%) Following the artifact demonstration, the group undergoes a professional oral defense. Mapped Learning Outcomes: Tests the ability to Defend design decisions (LO 5) and Formulate ethical mitigation strategies (LO 3). Assessment Criteria: Defense Argumentation: Can the students justify their design choices against potential criticisms regarding feasibility and ethics? Professional Communication: Is the presentation structured logically and delivered professionally?

• Optional Reading List Acuto, M. (2020). Engaging with global urban governance in the midst of a crisis. Dialogues in Human Geography, 10(2), 221-224. https://doi.org/10.1177/2043820620934232 Bibri, S. E. (2019). On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. Journal of Big Data, 6(1), 25. https://doi.org/10.1186/s40537-019-0182-7 Design Council. (2005). The Double Diamond: A universally accepted depiction of the design process. de Castro Neto, M. and de Melo Cartaxo, T., 2019. Smart and collective urban intelligence. T. Rodrigues & A. Inácio (Eds.), pp.83-94. D'Ignazio, C., & Klein, L. F. (2020). Data Feminism. The MIT Press. Escobar, O. (2020). Transforming lives, communities and systems? Co-production through participatory budgeting. In The palgrave handbook of co-production of public services and outcomes (pp. 285-309). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-53705-0_15 Friedman, B., & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. The MIT Press. Goh, K., 2015. Who’s smart? Whose city? The sociopolitics of urban intelligence. In Planning support systems and smart cities (pp. 169-187). Cham: Springer International Publishing. Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS quarterly, 337-355. https://www.jstor.org/stable/43825912 Hendawy, M. (2025, Jan 13). The violence of the majority: Rethinking AI positionality in decision-making. Internet Policy Review. https://policyreview.info/articles/news/ai-positionality/1820 Hendawy, M., Baum, J., Cenan, A., Frechen, N., Goldmann, A., Heger, P. and Bieber, C., 2025, March. After the hype: the uncertain future of Smart Cities. In Uncertain Journeys into Digital Futures (pp. 283-296). Nomos Verlagsgesellschaft mbH & Co. KG. Hendawy, M. and Ghoz, L., 2024. A starting framework for urban AI applications. Ain Shams Engineering Journal, 15(11), p.102987. Hendawy, M., da Silva, I.F.K. (2023). Hybrid Smartness: Seeking a Balance Between Top-Down and Bottom-Up Smart City Approaches. In: Goodspeed, R., Sengupta, R., Kyttä, M., Pettit, C. (eds) Intelligence for Future Cities. CUPUM 2023. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-031-31746-0_2 Hendawy, M. (2015). Connecting Urban Policy Making and Implementation. IUSD Journal, 3. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625 Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302 Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5–18. Schrage, M. (1999). Serious Play: How the World's Best Companies Simulate to Innovate. Harvard Business School Press.

This module utilizes a hybrid pedagogical model, combining Critical Urban Theory (Seminars) in the first half of the semester with Design Studio Pedagogy (Sprints) in the second half. The teaching approach is grounded in Problem-Based Learning (PBL), where students do not just absorb theory but actively apply it to solve ill-defined, real-world urban challenges.

1. Theoretical Instruction (Weeks 1–6): Interactive Seminars The theoretical basics are taught not through traditional monologues, but through Flipped Classroom techniques. Students are expected to read key texts (e.g., Data Feminism, Smart City critiques) before class. Case Study Analysis: Sessions are dedicated to deconstructing "failed" smart city projects to diagnose structural biases. Socratic Dialogue: Lectures utilize guided questioning to challenge students' assumptions about technology and power. Guest Lectures: Interventions by practitioners to ground theoretical concepts in reality.
2. Practical Application (Weeks 7–11): Design Science Research Sprints The second half of the module shifts to a Studio Format. The classroom becomes a co-working space where the "teacher" acts as a facilitator rather than a lecturer. Iterative Prototyping: Students work in teams using Agile methodologies (weekly sprints) to build, test, and refine their artifacts. Structured Peer Critique: Applying the "Critical Friends" method, groups exchange artifacts to stress-test each other's designs for ethical loopholes and bias. Field Work (Context-Dependent): Methodologies like Root Cause Analysis and Participatory Mapping are practiced to gather requirements. Alignment with Learning Outcomes: Through this integrated format, students bridge the gap between theory and practice. For example: By analyzing case studies of data extractivism, students learn to Diagnose structural failures (LO 3). By engaging in hands-on counter-mapping and prototyping, students learn to Apply participatory research methods (LO 2) and Design and Prototype socio-technical interventions (LO 4). By undergoing the final "Jury" defense, students learn to Defend design decisions (LO 5) in a professional setting.