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Hoe onveilig zijn die Lev’s nu echt? Een innovatief experiment met fietsende senioren en verschillende typen Lev's
No full textThe case of cities: skylines versions—affordances in urban planning education.
Full text linkStudies on city‐building games as educational tools show positive results in addressing different learning objectives, but also identify a missing link to reality, as they are mostly computer‐based. Given the differences between existing games and their capabilities, the exact function of these games in an urban planning curriculum is unclear. The city‐building game Cities: Skylines currently has three different versions (Digital, Tabletop, VR). Through an affordance analysis of the game’s three versions, this study analyses how the versions afford four primary knowledge dimensions, and in doing so identifies different educational applications for each version of Cities: Skylines in different planning disciplines. The results show that: (a) the board game is strong in fostering player participation and critical thinking more suited for the social and health studies, public policy, and citizen participation domains of urban planning; (b) the digital version functions as moddable simulator, ensuring familiarity with existing systems and monitoring their effects, useful in logistics and transportation planning; (c) the VR form viscerally involves players in the simulated processes, applicable in design‐focused segments of urban planning, such as sustainable design theory, housing, and land‐use management. The results of this study can help urban planning educators identify possible uses for different versions of Cities: Skylines.</p
Gaming across boundaries: The MSP challenge as boundary object for learning in maritime spatial planning communities.
Full text linkMaritime Spatial Planning (MSP) is a politically guided and stakeholder-driven process involving a range of actors (i.e., planners, stakeholders, scientists, and citizens). Theories of boundary objects offer a lens to understand how actors, in the context of decision and policy-making in organizations, can coordinate without consensus. This seems particularly relevant when institutions and communities are relatively young, and the body of knowledge is fragmented and fluid, such as in the case of MSP. A key question is whether, and how boundary objects can be intentionally designed and used to facilitate social and policy learning in such communities. In this research, the focus is on the use of the MSP Challenge serious games as a boundary object to facilitate learning in ‘Communities of Practice’ (CoP) around MSP. Data were collected through questionnaires of 62 MSP Challenge workshops between 2016 and 2020 with more than 1100 participants. Additionally, 33 interviews with key stakeholders were conducted. The findings show that the MSP Challenge is widely used for various goals and in various settings and that they are interpreted differently by different users. The success of the MSP Challenge relies on the boundary space in which it is implemented, taking into account discrepancies in learning due to variations in the backgrounds and attitudes of the participants towards the object, the activity, and the setting in which it is deployed.</p
Unveiling gender bias in LLM-generated hero and heroine narratives.
Full text linkThis article investigates gender bias in narratives generated by Large Language Models (LLMs) through a two-phase study. Building on our existing work in narrative generation, we employ a structured methodology to analyze the influence of protagonist gender on both the generation and classification of fictional stories. In Phase 1, factual narratives were generated using six LLMs, guided by predefined narrative structures (Hero's Journey and Heroine's Journey). Gender bias was quantified through specialized metrics and statistical analyses, revealing significant disparities in protagonist gender distribution and associations with narrative archetypes. In Phase 2, counterfactual narratives were constructed by altering the protagonists’ genders while preserving all other narrative elements. These narratives were then classified by the same LLMs to assess how gender influences their interpretation of narrative structures. Results indicate that LLMs exhibit difficulty in disentangling the protagonist's gender from the narrative structure, often using gender as a heuristic to classify stories. Male protagonists in emotionally driven narratives were frequently misclassified as following the Heroine's Journey, while female protagonists in logic-driven conflicts were misclassified as adhering to the Hero's Journey. These findings provide empirical evidence of embedded gender biases in LLM-generated narratives, highlighting the need for bias mitigation strategies in AI-driven storytelling to promote diversity and inclusivity in computational narrative generation.</p
