Intersectoral Knowledge in AI and Urban Studies: A Framework for Transdisciplinary Research
This work addresses the problem of reconciling divergent disciplinary viewpoints for early career researchers and transdisciplinary teams in AI and urban studies, though it is incremental as it builds on existing transdisciplinary concepts.
The paper tackles the challenge of validating and integrating knowledge across different disciplines in AI and urban studies by proposing a six-dimensional framework based on an analysis of highly cited research from 2014 to 2024, revealing a predominance of perspectives like critical realism and positivism while identifying less common stances such as idealism and mixed methods.
Transdisciplinary approaches are increasingly essential for addressing grand societal challenges, particularly in complex domains such as Artificial Intelligence (AI), urban planning, and social sciences. However, effectively validating and integrating knowledge across distinct epistemic and ontological perspectives poses significant difficulties. This article proposes a six-dimensional framework for assessing and strengthening transdisciplinary knowledge validity in AI and city studies, based on an extensive analysis of the most cited research (2014--2024). Specifically, the framework classifies research orientations according to ontological, epistemological, methodological, teleological, axiological, and valorization dimensions. Our findings show a predominance of perspectives aligned with critical realism (ontological), positivism (epistemological), analytical methods (methodological), consequentialism (teleological), epistemic values (axiological), and social/economic valorization. Less common stances, such as idealism, mixed methods, and cultural valorization, are also examined for their potential to enrich knowledge production. We highlight how early career researchers and transdisciplinary teams can leverage this framework to reconcile divergent disciplinary viewpoints and promote socially accountable outcomes.