Year: 2026
Type: dissertation
Study design: Observational Study
Abstract: Artificial intelligence-generated content is becoming increasingly common across digital and social media platforms, raising questions about how audiences evaluate the authenticity of visual information. This study examined college students’ ability to distinguish between AI-generated and real agricultural images. The study also explored students’ perceptions of their own ability, the average college student’s ability, and the role of agricultural involvement in the accuracy of identification of real and AI-generated images. A survey was conducted with Texas Tech University students using 20 social media-style agricultural image stimuli representing 10 agricultural commodities. Participants indicated whether each image was AI-generated or real and rated it on a scale from completely AI-generated to completely real. Results showed that the 224 participants’ accuracy varied by image type and commodity, with some AI-generated images frequently perceived as real and some real images misidentified as AI-generated. Participants most often correctly identified nine out of 20 images. Findings also showed participants rated their own ability to identify AI-generated images higher than the ability of the average college student; although, perceived ability did not strongly align with actual performance. Agricultural involvement was not significantly related to overall image identification accuracy. These findings suggest a need for stronger visual media literacy education and greater transparency in the use of AI-generated agricultural images.
Author: Hutson, Bryce Wayne
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funder: Texas Tech University
OpenAlex ID: W7214137978