AI Methodology Map. Practical and Theoretical Approach to Engage with GenAI for Digital Methods Research

This essay accounts for a novel way to explore generative artificial intelligence (GenAI) applications for digital methods research, based on the AI Methodology Map. The map is a pedagogical resource and a theoretical framework designed to structure, visually represent, and explore GenAI web-based a...

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Bibliographic Details
Published in:	Sociologica (Bologna) Vol. 18; no. 2; pp. 109 - 144
Main Authors:	Janna Joceli Omena, Antonella Autuori, Eduardo Leite Vasconcelos, Matteo Subet, Massimo Botta
Format:	Journal Article
Language:	English
Published:	University of Bologna 01-10-2024
Subjects:	ai in education algorithmic race stereotypes digital methods genai generative artificial intelligence image networks technicity
Online Access:	Get full text
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Summary:	This essay accounts for a novel way to explore generative artificial intelligence (GenAI) applications for digital methods research, based on the AI Methodology Map. The map is a pedagogical resource and a theoretical framework designed to structure, visually represent, and explore GenAI web-based applications. As an external object, the map functions as a valuable teaching material and interactive toolkit. As a theoretical framework, it is embodied in a static representation that provides principles for engaging with GenAI. Aligned with digital methods’ practical, technical, and theoretical foundations, the map facilitates explorations and critical examinations of GenAI and is supported by visual thinking and data practice documentation. The essay then outlines the map principles, its system of methods, educational entry points, and applications. The organization is as follows: First, we review GenAI methods, discussing how to access them, and their current uses in social research and the classroom context. Second, we define the AI Methodology Map and unpack the theory it embodies by navigating through the three interconnected methods constituting it: making room for, repurposing and designing digital methods-oriented projects with GenAI. Third, we discuss how the map bridges GenAI concepts, technicity, applications and the practice of digital methods, exposing its potential and reproducibility in educational settings. Finally, we demonstrate the AI Methodology Map’s application, employing a digital methodology to analyze algorithmic race stereotypes in image collections generated by nine prominent GenAI apps. In conclusion, the essay unveils methodological challenges, presenting provocations and critiques on repurposing GenAI for social research. By encompassing practice, materiality and theoretical perspective, we argued that the AI Methodology Map bridges theoretical and empirical engagement with GenAI, serving them together or separately, thus framing the essay’s main contribution. We expect that the AI Methodology Map’s reproducibility will likely lead to further discussions, expanding those we present here.
ISSN:	1971-8853
DOI:	10.6092/issn.1971-8853/19566