Integrating <scp>AI</scp> language models in qualitative research: Replicating interview data analysis with <scp>ChatGPT</scp>

Jalali, Mohammad S.; Akhavan, Ali

doi:10.1002/sdr.1772

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Beyond basic preparation, it aids in categorizing and tagging data, generating descriptive statistics, and scripting for complex manipulations across different programming languages. Additionally, ChatGPT holds promise in processing text data, such as analyzing interview transcripts to extract model mechanisms like variables and causal relationships to assist in constructing causal loop diagrams (Jalali and Akhavan, 2024). Recent advancements in AI capabilities for processing extensive and varied text formats are opening up exciting new avenues for research.…”

Section: Discussionmentioning

confidence: 99%

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT

Akhavan,

Jalali

2024

System Dynamics Review

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Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This article endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID‐19's impact on economic growth in the United States. However, our guidelines are generic and can be applied to a broader range of generative AI tools. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method. We emphasize while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

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Section: Discussionmentioning

confidence: 99%

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT

Akhavan,

Jalali

2024

System Dynamics Review

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“…Generative AI can be used throughout the life cycle of M&S (Figure 1), starting with the creation of a conceptual model (i.e., a 'sketch' or 'diagram'), then moving to a mathematical or formal model (e.g., using a language for specification such as SysML), implementing the specification as a computational model via computer code (e.g., NetLogo), and performing simulations to generate insights that are shared with end-users and/or analyzed to suggest revisions in the conceptual model [5][6][7]. Our focus was on conceptual modeling, which has been the subject of several studies in generative AI either to create models from text [8][9][10] or to explain models as text [11].…”

Section: Introductionmentioning

confidence: 99%

A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence

Gandee,

Glaze,

Giabbanelli

2024

Mathematics

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While comprehensive knowledge networks can be instrumental in finding solutions to complex problems or supporting the development of detailed simulation models, their large number of nodes and edges can become a hindrance. When the representation of a network becomes opaque, they stop fulfilling their role as a shared representation of a system between participants and modelers; hence, participants are less engaged in the model-building process. Combating the information overload created by large conceptual models is not merely a matter of changing formats: shifting from an unwieldy diagram to enormous amounts of text does not promote engagement. Rather, we posit that participants need an environment that provides details on demand and where interactions with a model rely primarily on a familiar format (i.e., text). In this study, we developed a visual analytics environment where linked visualizations allow participants to interact with large conceptual models, as shown in a case study with hundreds of nodes and almost a thousand relationships. Our environment leverages several advances in generative AI to automatically transform (i) a conceptual model into detailed paragraphs, (ii) detailed text into an executive summary of a model, (iii) prompts about the model into a safe version that avoids sensitive topics, and (iv) a description of the model into a complementary illustration. By releasing our work open source along with a video of our case study, we encourage other modelers to use this approach with their participants. Their feedback and future usability studies are key to respond to the needs of participants by improving our environment given individual preferences, models, and application domains.

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Integrating AI language models in qualitative research: Replicating interview data analysis with ChatGPT

Cited by 2 publications

References 30 publications

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT

A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence

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