Researchers Yuan Lai and Rea Lavi have proposed a novel pedagogical framework called DIMES to address the significant challenges of teaching problem structuring in urban science education. Published in the journal Frontiers of Urban and Rural Planning, the study argues that the increasing complexity of urban systems creates distinct pedagogical hurdles that traditional methods often fail to resolve. The authors identify problem structuring as a crucial early step in addressing ill-structured problems, defining it as the process of defining the scope of a problem, potential ways to address it, and suitable criteria for judging solutions. By integrating design thinking and systems thinking, the DIMES method offers a domain-agnostic approach intended for higher education settings.
Urban science is described as an emerging and transdisciplinary field that attracts deep interest in planning degree programs worldwide. The discipline emphasizes the science of cities and urban information technology by integrating design, engineering, system science, spatial science, behavioral and social science, and decision science. The authors note that while urban science aims to address complexity through data-driven and computational approaches, it poses intricate challenges for students. These challenges are amplified by the rapid deployment of information technology, smart cities, and artificial intelligence, which exacerbate social-technical conflicts within contemporary urban living.
The study highlights that urban science problems are often ill-structured, meaning they are authentic, multidisciplinary, and complex with multiple components and interrelationships. Unlike well-structured problems that have clear goals and single verifiable solutions, ill-structured problems have vague goals and multiple uncertain solutions. The authors argue that solutions to such problems are never strictly correct or incorrect, but rather better or worse depending on inter-subjective success criteria agreed upon by stakeholders. This distinction is critical for students who must navigate ambiguity and define problems holistically in dynamic contexts.
Teaching problem structuring in urban science is particularly difficult due to three key characteristics: transdisciplinary, data-driven, and techno-humanistic nature. The transdisciplinary aspect requires integrating computer science, informatics, urban planning, and social science, fostering diverse problem-solving skills. The data-driven nature sparks philosophical debates between problem-driven and data-driven methodologies, requiring students to adapt to digital resources. The techno-humanistic emphasis on smart city development necessitates balancing physical, social, and technical considerations, often resulting in ill-structured problems involving data privacy, digital equity, and algorithmic justice.
The DIMES method, which stands for Describe, Inquire, Model, Extract, and State, was developed to provide a structured procedure for applying problem structuring instruction. The method is designed for higher education learning settings, from first-year education onward, and has been applied with undergraduate and graduate students in urban planning, architecture, and systems engineering. Each of the five steps is accompanied by instructions, an assessment rubric, and reflection items. The process is intended to be iterative, where each subsequent step may inform potential changes to previous steps, allowing for a dynamic and responsive learning experience.
In the Describe step, students are provided with a problem description, such as the rapid urbanization of former rural villages in large Chinese cities. These villages, characterized by low-quality, high-density informal settlements, aggravate the urban heat island effect due to crowded environments and hardened roads. The Inquire step involves creating a problem case by identifying stakeholders, effects, locations, timing, and root causes. For example, stakeholders include residents, businesses, and city officials, while effects range from adverse health impacts to high air conditioning costs and social isolation.
The Model step utilizes hierarchical conceptual modeling, inspired by Object-Process Methodology, to break down the problem into sub-levels. Students create an Object-Process Diagram that shows how things are linked in the system being modeled. In the provided example, the top function ‘Managing a house in an urban village’ is broken down to identify sub-functions with the largest impact. The Extract step involves identifying leverage points within the problem model based on rules such as depth within the model, effect score, and position in the causal chain. These leverage points represent places where relatively little effort can produce a large impact on overall system outcomes.
The final State step requires composing a problem statement based on the problem case and selected leverage points. In the example, the statement highlights how residents of urban villages in China are negatively affected by health issues, high costs, and social isolation caused by the urban heat island effect and lack of community support. The authors emphasize that this structured yet flexible framework fosters critical thinking during the drafting of the problem case and systems thinking during the creation of the problem model. These cognitive processes directly address workforce competencies identified by national and international organizations.
The study notes that the DIMES method can be applied within twelve hours, including two hours of instructor training, and does not require special equipment or expensive digital resources. It can be implemented using common office software or a dedicated digital platform that includes generative artificial intelligence capabilities. The platform features a chatbot able to assess adherence to instructions in any step, enabling students to check their work frequently while saving instructor time. The role of AI is defined as a virtual teaching assistant, not a replacement for the instructor or student, ensuring that pedagogical integrity is maintained.
The authors acknowledge that the integration of DIMES into urban science curricula aligns with the demands of fast-evolving smart city development and the disruptive potential of generative AI. These technologies reshape urban systems and necessitate pedagogical strategies that balance technical proficiency with critical, ethical, and creative reasoning. Educators must emphasize collaborative, human-centered problem-solving while leveraging AI for data analysis and scenario generation. The study raises questions about when students should employ large language models for problem-solving and what foundational skills ensure responsible AI use.
Limitations of the study include the lack of longitudinal evidence to draw definitive conclusions about the long-term impact of DIMES on students’ career development and their ability to address real-world urban challenges. The authors suggest that future research should explore the longitudinal impacts of the method on student competencies, its scalability across different institutional contexts, and its interplay with emerging technologies. Case studies examining its application in real-world urban projects and interdisciplinary collaborations will further refine its utility.
The growing complexity of urban systems underscores the critical need for innovating pedagogy in urban science education. By bridging design thinking and systems thinking, the DIMES method provides a structured framework for problem structuring that remains adaptable to diverse disciplines and evolving contexts. As urban science continues to evolve, integrating such methods can empower future practitioners to address the interconnected, systemic challenges of cities, ensuring resilience, equity, and sustainability in an era of rapid urban transformation.
Urban science education developed out of intellectual roots tracing back to the 1960s aerospace technology boom and the emergence of system science, evolving alongside data-intensive research frequently described as the fourth paradigm of science. Unlike conventional urban planning programs that primarily emphasize physical space and settlement development, the emerging discipline approaches cities through the analytical lens of complexity science. This approach pursues two primary objectives: treating cities as scientific research subjects through systematic observation and computational analytics, and developing technological solutions for governance, operation, management, and planning. Across international institutions, curricula have broadly coalesced around two distinct orientations. One pathway centers on applied urban informatics, which treats metropolitan areas as living laboratories by deploying information technology for urban instrumentation. The second pathway focuses on the science of cities, investigating complex adaptive systems to identify universal patterns and governing laws across urban form, scale, network structures, and evolutionary dynamics.
To prepare students for these multifaceted systems, planning schools have historically relied on project-based learning, using studio courses and simulated scenarios to produce tangible designs. However, scholars argue that such workflows may prove insufficient for cultivating the scientific reasoning and conceptual foundation required in urban science. In contrast, the pedagogical methodology proposed by the authors is grounded in problem-based learning. In this active instructional format, learners confront messy, ill-structured dilemmas without receiving comprehensive informational roadmaps in advance, allowing instructors to act as facilitators rather than authoritative mentors. This collaborative model directly targets a recognized challenge in broader technical education, where academic achievement often diverges from higher-order thinking skills.
The methodology also synthesizes several theoretical concepts to guide students through systematic analysis. It draws upon Object-Process Methodology to structure system elements textually and graphically across hierarchical sub-levels, alongside system leverage point theory, which ranks systemic interventions on an effectiveness scale ranging from static standards to overarching mindsets. During classroom implementation, such as training modules examining urban heat dynamics in Beijing, student teams follow precise procedural guidelines. When analyzing functional models, if multiple sub-functions tie in their initial impact scores, teams resolve the tie by scoring individual effects on a scale of one or two, or by prioritizing components with fewer associated variables to minimize intervention effort. Student performance is judged through dual-criteria rubrics assessing adherence to instructions separately from substantive correctness, supplemented by individual reflective exercises. Looking forward, the researchers plan to expand platform capabilities by introducing an instructor-oriented chatbot designed to summarize team reflections and streamline pedagogical facilitation.
Subject of Research: Pedagogical methodology for problem structuring in urban science education
Article Title: Problem structuring in urban science education: Why, what, and how
Article References: Lai, Y., & Lavi, R. (2025). Problem structuring in urban science education: Why, what, and how. Frontiers of Urban and Rural Planning, 3(1), Article 13. https://doi.org/10.1007/s44243-025-00064-3
Image Credits: AI Generated
DOI: 10.1007/s44243-025-00064-3
Keywords: urban science, problem structuring, DIMES method, higher education, smart cities, systems thinking, design thinking, pedagogy, ill-structured problems, urban planning, Problem, structuring
News Source: Courtney Benton. (November 12, 2026). New DIMES Method Tackles Urban Problem Structuring. Scienmag.
