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Abstract BackgroundGenerative artificial intelligence (AI) large‐language models (LLMs) have significant potential as research tools. However, the broader implications of using these tools are still emerging. Few studies have explored using LLMs to generate data for qualitative engineering education research. Purpose/HypothesisWe explore the following questions: (i) What are the affordances and limitations of using LLMs to generate qualitative data in engineering education, and (ii) in what ways might these data reproduce and reinforce dominant cultural narratives in engineering education, including narratives of high stress? Design/MethodsWe analyzed similarities and differences between LLM‐generated conversational data (ChatGPT) and qualitative interviews with engineering faculty and undergraduate engineering students from multiple institutions. We identified patterns, affordances, limitations, and underlying biases in generated data. ResultsLLM‐generated content contained similar responses to interview content. Varying the prompt persona (e.g., demographic information) increased the response variety. When prompted for ways to decrease stress in engineering education, LLM responses more readily described opportunities for structural change, while participants' responses more often described personal changes. LLM data more frequently stereotyped a response than participants did, meaning that LLM responses lacked the nuance and variation that naturally occurs in interviews. ConclusionsLLMs may be a useful tool in brainstorming, for example, during protocol development and refinement. However, the bias present in the data indicates that care must be taken when engaging with LLMs to generate data. Specially trained LLMs that are based only on data from engineering education hold promise for future research.
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This content will become publicly available on October 15, 2026
Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theory
Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings — either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.
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- Award ID(s):
- 2107505
- PAR ID:
- 10649925
- Publisher / Repository:
- AAAI Press
- Date Published:
- Journal Name:
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 3065-8365
- Page Range / eLocation ID:
- 2419 to 2430
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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