The AI Methodology Mapis a pedagogical resource (interactive toolkit and teaching material) and theoretical framework designed to structure, visually represent, and explore Generative AI (GenAI) web-based applications for digital methods-led research. The map is a conceptual, empirical and interactive structure that organises knowledge and methodological frameworks for engaging with GenAI. The map is developed for implementation in workshops or AI sprints, allowing anyone to independently repeat the procedures without needing mediators. Individuals can use the map's essential theoretical points as a guide to engage with GenAI and take advantage of the external teaching resources.
Rather than serving as a digital methods protocol or recipe (what and how it was done), the map focuses on "what to look at." It elicits ways of knowing GenAI through technical, empirical, and conceptual viewpoints. Through practical engagement with GenAI, the expected results are geared toward developing a specific mindset required to advance digital methods research.
The map presumes that users possess a basic understanding of generative methods, such as what generative AI is and its potential, limitations, and challenges.
The Map advances a technicity perspective as a practical and theoretical approach to engage with GenAI for digital methods research, which is operationalised through five educational entry points (look at the Map, left side) that cultivates an awareness component about GenAI and its potential for and as research methods.
You will explore generative methods by 1) navigating an interactive visualisation that gathers information about several GenAI proprieatry applications and open-source-models, 2) while responding to crucial questions about GenAI apps and associated large language models (LLMs):
Questions about the generative methods in use What generative method is used, and what LLM is operating?
Questions about the API documentation Is API documentation available? What are its affordances and limitations? What can we learn from that?
Questions about the LLM and associated training dataset What is the model in use? Is it an open-source or proprietary model? Who developed it? What type of input is required? If possible, adjust the model temperature; what does it mean? Can we identify the dataset used to train the model? What are the limitations or potential biases one might encounter in the LLM currently in use?
Questions about the necessary input to generate new content (output) What inputs are required? What kind of output does one get?
Questions about required skills What is required to use this app or open-source code?
3)The explorations and findings are to be visually documented, such as in teaching resources for documenting the map exploration, which allows for and empowers collective discussions among all involved.
You will engage with and use GenAI content, interfaces and models as means of research. This method's rationale moves from medium specificity to the definition of the research aim accordingly. You will define and document your decisions based on what you have learned from Method 1.
Define and document What is the output data to reach? (e.g. text, image, audio) Why? Which input will you be employing to generate new content? Why? What is the GenAI app, model and/or interface mediating your research? Or serving as an object of critique? What is your research aim? What prompt(s)? Why?
The explorations and findings are to be visually documented, such as in teaching resources for documenting the map exploration.
This method allows experimental and exploratory analysis of GenAI models, outputs, and interfaces. You will organise a workflow responsive to Method 2. The following steps and decisions will support you in designing a digital method project with GenAI outputs and/or interfaces.
What techniques and methods support analysing and visualising GenAI outputs, models or interfaces? (please check the How to Cite section for network vision methods to repurpose visual-generate content).
The explorations and findings are to be visually documented, such as in teaching resources for documenting the map exploration.
The map is developed for implementation in workshops or AI sprints. It allows anyone to independently repeat the procedures without needing mediators. The map's essential theoretical points can guide engaging with and critically reflecting on GenAI and practically following its interconnected methods to advance digital methods research. It is up to you whether to follow these possibilities separately or embrace them in a combination mode 👩🏻💻🤓✨❣️
GenAI Apps interactive visualisation
Teaching resources for documenting the map exploration
Generative Methods Conference presentation and songified abstract
If you want to report an issue, you can submit it to the issue tracker of this repository.
The code contained in this repository and the executable distributions are licensed under the terms of the MIT License. The project is released under the terms of the CC BY SA license.
The map’s applications were developed within the context of the research project Designing With: A New Educational Module to Integrate Artificial Intelligence, Machine Learning and Data Visualization in Design Curricula (Botta et al., 2024). We want to thank the project team for their dedication and contributions throughout the development of this work. A special thank you to all the students who participated in the workshops—your engagement and insights were invaluable. We also wish to acknowledge the Generative Methods Conference, songified abstracts here where we first introduced the results of the AI Methodology Map.
Omena, J. J., Autuori, A., Leite Vasconcelos, E., Subet, M., & Botta, M. (2024). AI Methodology Map. Practical and Theoretical Approach to Engage with GenAI for Digital Methods Research. Sociologica, 18(2), 109–144. https://doi.org/10.6092/issn.1971-8853/19566