Evaluating Undergraduate Students’ Trust and Comprehension on Ai-Written and Human-Written Popular Science Articles
Authors
Science Education Department, Taraba State University, Jalingo (Nigeria)
Science Education Department, Taraba State University, Jalingo (Nigeria)
Article Information
DOI: 10.51584/IJRIAS.2026.11080088
Subject Category: Communications Technology
Volume/Issue: 11/8 | Page No: 1223-1228
Publication Timeline
Submitted: 2026-08-16
Accepted: 2026-08-21
Published: 2026-09-09
Abstract
The emergence of artificial intelligence is a turning point to so many things including in the education field and this AI is now being used to produce science writing for the use of the public. Upon this turning point in science education yet only little known about how undergraduate students respond to such materials compared to how they respond to works authored by human. That is why this study evaluated undergraduate students' trust and comprehension of an AI-written and a human-written popular science article covering the same topic. The research design used in this study is the comparative design specifically the project-based evaluation design and a sample of 35 students was selected from a population of 17,000 200-level undergraduate students in Taraba State University. The students read the articles written by AI and the one written by human and then completed a structured questionnaire that measured trust, comprehension and article preference. The data collected was analysed using frequency, percentages and mean scores and the findings of the study showed that students rated the article written by human much higher in trust with a mean range from 3.66 to 3.83 than the article written by AI with a mean range of 2.49 to 2.70 and then a grand mean of 3.34 confirmed the overall preference of undergraduate students for materials authored by human. The findings also showed that comprehension was strong as 62.9% students correctly answered all the comprehension items while 28.6% showed a moderate comprehension and only 8.5% showed low comprehension. And when asked to choose between human-written article and AI-written article, 68.8% of students showed they preferred the human-written article while 31.4% of the students show they preferred the AI-writren article. Then the study concluded that science writing generated by AI can support understanding but is yet to have the confidence of undergraduate students as the one authored by human.
Keywords
AI and Human-Written science articles, Science communication, Trust, Comprehension
Downloads
References
1. Burns, T. W., O'Connor, D. J., & Stocklmayer, S. M. (2020). Science communication: A contemporary definition. Public Understanding of Science, 29(1), 1–15. [Google Scholar] [Crossref]
2. Fischhoff, B., & Scheufele, D. A. (2019). The science of science communication. Proceedings of the National Academy of Sciences, 116(16), 7632–7639. https://doi.org/10.1073/pnas.1805863115 [Google Scholar] [Crossref]
3. Galvez, C. (2025). Artificial intelligence and the future of science communication. Journal of Digital Communication Studies, 12(2), 45–58. [Google Scholar] [Crossref]
4. Huschens, M., Schneider, F. M., & Vogler, D. (2023). Public perceptions of AI-generated communication and automated journalism. Computers in Human Behavior, 145, Article 107746. https://doi.org/10.1016/j.chb.2023.107746 [Google Scholar] [Crossref]
5. Kappel, K., & Holmen, S. J. (2019). Why science communication, and does it work? A taxonomy of science communication aims and a survey of the empirical evidence. Frontiers in Communication, 4, Article 55. https://doi.org/10.3389/fcomm.2019.00055 [Google Scholar] [Crossref]
6. Kitchen, P. J., Kerr, G., Schultz, D. E., McColl, R., & Pals, H. (2019). The elaboration likelihood model: Review, critique and research agenda. European Journal of Marketing, 48(11/12), 2033–2050. [Google Scholar] [Crossref]
7. Kreps, S., McCain, R. M., & Brundage, M. (2022). All the news that's fit to fabricate: AI-generated text as a tool of media misinformation. Journal of Experimental Political Science, 9(1), 104–117. https://doi.org/10.1017/XPS.2020.37 [Google Scholar] [Crossref]
8. Lewis, N. A., Jr., & Wai, J. (2021). Communicating what we know and what isn't so: Science communication in psychology. Perspectives on Psychological Science, 16(5), 898–909. https://doi.org/10.1177/1745691620964062 [Google Scholar] [Crossref]
9. Paivio, A. (1971). Imagery and verbal processes. Holt, Rinehart and Winston. [Google Scholar] [Crossref]
10. Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. Springer-Verlag. [Google Scholar] [Crossref]
11. Vaccaro, K., Sandvig, C., & Karahalios, K. (2024). Audience perception of AI-generated versus human-written text in digital communication. New Media & Society, 26(4), 1123–1140. [Google Scholar] [Crossref]
12. Wintterlin, F., Hendriks, F., Mede, N. G., Bromme, R., & Schafer, M. S. (2022). The role of source credibility in science communication. Frontiers in Communication, 6, Article 822757. https://doi.org/10.3389/fcomm.2021.822757 [Google Scholar] [Crossref]