How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI.
Saved in:
| Title: | How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI. |
|---|---|
| Authors: | Di Lodovico, Chiara1,2 (AUTHOR), Torrielli, Federico1 (AUTHOR), Di Caro, Luigi1 (AUTHOR), Rapp, Amon1 (AUTHOR) amon.rapp@unito.it |
| Source: | International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 23, p14846-14870. 25p. |
| Subjects: | Generative artificial intelligence, Stable Diffusion, Intuition, Qualitative research, Artificial intelligence, Theorists |
| Abstract: | Generative Artificial Intelligence (GenAI) text-to-image models have made significant progress in emulating human-like outputs. However, understanding the inner functioning of these models remains a challenge due to their complexity and black-box nature. It has been observed that individuals naturally develop informal conceptualizations, termed "folk theories," to explain the behaviors of algorithmic systems. The specific nature of GenAI text-to-image models, which are obscure in their working principles, yet carry out activities that are peculiar to humans, makes it interesting to investigate people's theorization about this technology. With this aim, we conducted a qualitative interview study with 20 participants and observed how they accounted for the outputs of Stable Diffusion. The study findings show that participants developed a wide spectrum of conceptualizations, including folk theories that appear distinctive of GenAI text-to-image technology, also ascribing to the model a variety of "mental states." Furthermore, we found that theory building follows different inductive and deductive trajectories, with participants employing diverse strategies to explain the functioning of the technology. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 189570796 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Di+Lodovico%2C+Chiara%22">Di Lodovico, Chiara</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Torrielli%2C+Federico%22">Torrielli, Federico</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Di+Caro%2C+Luigi%22">Di Caro, Luigi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rapp%2C+Amon%22">Rapp, Amon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amon.rapp@unito.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Dec2025, Vol. 41 Issue 23, p14846-14870. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Stable+Diffusion%22">Stable Diffusion</searchLink><br /><searchLink fieldCode="DE" term="%22Intuition%22">Intuition</searchLink><br /><searchLink fieldCode="DE" term="%22Qualitative+research%22">Qualitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Theorists%22">Theorists</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Generative Artificial Intelligence (GenAI) text-to-image models have made significant progress in emulating human-like outputs. However, understanding the inner functioning of these models remains a challenge due to their complexity and black-box nature. It has been observed that individuals naturally develop informal conceptualizations, termed "folk theories," to explain the behaviors of algorithmic systems. The specific nature of GenAI text-to-image models, which are obscure in their working principles, yet carry out activities that are peculiar to humans, makes it interesting to investigate people's theorization about this technology. With this aim, we conducted a qualitative interview study with 20 participants and observed how they accounted for the outputs of Stable Diffusion. The study findings show that participants developed a wide spectrum of conceptualizations, including folk theories that appear distinctive of GenAI text-to-image technology, also ascribing to the model a variety of "mental states." Furthermore, we found that theory building follows different inductive and deductive trajectories, with participants employing diverse strategies to explain the functioning of the technology. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189570796 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10447318.2025.2491009 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 14846 Subjects: – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Stable Diffusion Type: general – SubjectFull: Intuition Type: general – SubjectFull: Qualitative research Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Theorists Type: general Titles: – TitleFull: How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Di Lodovico, Chiara – PersonEntity: Name: NameFull: Torrielli, Federico – PersonEntity: Name: NameFull: Di Caro, Luigi – PersonEntity: Name: NameFull: Rapp, Amon IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10447318 Numbering: – Type: volume Value: 41 – Type: issue Value: 23 Titles: – TitleFull: International Journal of Human-Computer Interaction Type: main |
| ResultId | 1 |
