The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
ai, Post-Truth Realities, and Thai Students’ Information-Seeking Behavior
Open accessAbstract
This paper investigates how ai challenges traditional verification, citation, and evaluation principles, thereby eroding truth. ai -generated content, lacking verifiable citations, operates on probabilistic models that present information as probable rather than factual. The widespread reliance on ai for information-seeking risks diminishing critical evaluation, blending fact with fiction, and compromising academic integrity. To explore these effects, a mixed-method study was conducted involving 240 Chulalongkorn University students who used various online resources like Google, Wikipedia, and ChatGPT to research Rudolf Carnap’s verificationism views. Their choices were analyzed to assess dependency patterns on these platforms. Further, a qualitative analysis compared ai -generated responses with verified sources to gauge their accuracy. This comprehensive approach revealed significant insights into students’ preferences for information sources and the critical importance of validating ai outputs in academic settings, underscoring the nuanced impacts of digital tools on traditional knowledge standards.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Research on academic integrity highlights that AI-generated content may appear coherent and authoritative while lacking verifiable sources or containing fabricated information, thereby increasing the cognitive burden placed on students' evaluative judgment (Park and Nan, 2025; Slutskiy, 2025).”
From Information competence and critical thinking in digital environments evidence from university students in Kazakhstan (2026) · cites this paper as Slutskiy, 2025
Citations
Cites
1 retracted
Yu, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24289 · Cited by 225
RetractedTraining language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Pavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972
Research works printed but not linked: 43
- Alkaissi, Hussam, and Samy I. McFarlane. 2023. “Artificial Hallucinations in ChatGPT: Implications in Scientific Writing.” Cureus 15 (2): e35179. https://doi.org/10.7759/cureus.35179. · DOI · Paper page
- Armstrong, David M. 1973. Belief, Truth and Knowledge. London: Cambridge University Press.
- Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In Proceedings of the 2021 acm Conference on Fairness, Accountability, and Transparency (FAccT ‘21), 610–623. New York: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922. · DOI · Paper page
- Chan, Anastasia. 2023. “gpt-3 and InstructGPT: Technological Dystopianism, Utopianism, and ‘Contextual’ Perspectives in ai Ethics and Industry.” ai and Ethics 3 (1): 53–64. https://doi.org/10.1007/s43681-022-00148-6. · DOI · Paper page
- Davies, Martin. 1981. Meaning, Quantification, Necessity: Themes in Philosophical Logic. London: Routledge & Kegan Paul.
- Karanouh, Maya. 2023. “Mapping ChatGPT in Mainstream Media: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis.” arXiv preprint arXiv:2305.18340. · arXiv
- Leiter, Christoph, Ran Zhang, Yanran Chen, Jonas Belouadi, Daniil Larionov, Vivian Fresen, and Steffen Eger. 2023. “ChatGPT: A Meta-Analysis after 2.5 Months.” arXiv preprint arXiv:2302.13795. · arXiv
- Molyneux, Logan, and Mark Coddington. 2020. “Aggregation, Clickbait and Their Effect on Perceptions of Journalistic Credibility and Quality.” Journalism Practice 14 (4): 429–446. https://doi.org/10.1080/17512786.2019.1628658. · DOI
- Munn, Luke, Liam Magee, and Vanicka Arora. 2023. “Truth Machines: Synthesizing Veracity in ai Language Models.” ai & Society. https://doi.org/10.1007/s00146-023-01756-4. · DOI · Paper page
- Ouchchy, Lydia, Alexandre Coin, and Veljko Dubljević. 2020. “ai in the Headlines: The Portrayal of the Ethical Issues of Artificial Intelligence in the Media.” ai & Society 35: 927–936. https://doi.org/10.1007/s00146-020-00965-5. · DOI
- Pauncefort, Elizabeth. 2021. “Critical Literacy Is at the Heart of the Answer.” In Media, Technology and Education in a Post-Truth Society, edited by Alex Grech, 73–94. Bingley, UK: Emerald Publishing Limited.
- Russell, Bertrand. 1912. The Problems of Philosophy. London: Oxford University Press.
- Salvagno, Michele, Fabio Silvio Taccone, and Alberto Giovanni Gerli. 2023. “Artificial Intelligence Hallucinations.” Critical Care 27: 180. https://doi.org/10.1186/s13054-023-04473-y. · DOI · Paper page
- Soames, Scott. 1984. “What Is a Theory of Truth?” The Journal of Philosophy 81 (8): 411–429.
- Vergeer, Maurice. 2020. “Artificial Intelligence in the Dutch Press: An Analysis of Topics and Trends.” Communication Studies 71 (3): 373–392. https://doi.org/10.1080/10510974.2020.1733038. · DOI
- Vision, Gerald. 2004. Veritas: The Correspondence Theory and Its Critics. Cambridge, MA: mit Press.
- Walker, Ralph C. S. 1989. The Coherence Theory of Truth. London: Routledge.
- Xu, Ruiyun, Yue (Katherine) Feng, and Hailiang Chen. 2023. “ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience.” ssrn Electronic Journal. https://doi.org/10.2139/ssrn.4498671. · DOI
- Yu, Chengcheng, Jinzhe Yan, and Na Cai. 2024. “ChatGPT in Higher Education: Factors Influencing ChatGPT User Satisfaction and Continued Use Intention.” Frontiers in Education 9: 1354929. https://doi.org/10.3389/feduc.2024.1354929. · DOI
- Yu, Ping, Hua Xu, Xia Hu, and Chao Deng. 2023. “Leveraging Generative ai and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.” Healthcare 11 (20): 2776. https://doi.org/10.3390/healthcare11202776. · DOI
- Alkaissi, Hussam, and Samy I. McFarlane. 2023. “Artificial Hallucinations in ChatGPT: Implications in Scientific Writing.” Cureus 15 (2): e35179. https://doi.org/10.7759/cureus.35179. · DOI · Paper page
- Armstrong, David M. 1973. Belief, Truth and Knowledge. London: Cambridge University Press.
- Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In Proceedings of the 2021 acm Conference on Fairness, Accountability, and Transparency (FAccT ‘21), 610–623. New York: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922. · DOI · Paper page
- Chan, Anastasia. 2023. “gpt-3 and InstructGPT: Technological Dystopianism, Utopianism, and ‘Contextual’ Perspectives in ai Ethics and Industry.” ai and Ethics 3 (1): 53–64. https://doi.org/10.1007/s43681-022-00148-6. · DOI · Paper page
- Davies, Martin. 1981. Meaning, Quantification, Necessity: Themes in Philosophical Logic. London: Routledge & Kegan Paul.
- Karanouh, Maya. 2023. “Mapping ChatGPT in Mainstream Media: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis.” arXiv preprint arXiv:2305.18340. · arXiv
- Leiter, Christoph, Ran Zhang, Yanran Chen, Jonas Belouadi, Daniil Larionov, Vivian Fresen, and Steffen Eger. 2023. “ChatGPT: A Meta-Analysis after 2.5 Months.” arXiv preprint arXiv:2302.13795. · arXiv
- Molyneux, Logan, and Mark Coddington. 2020. “Aggregation, Clickbait and Their Effect on Perceptions of Journalistic Credibility and Quality.” Journalism Practice 14 (4): 429–446. https://doi.org/10.1080/17512786.2019.1628658. · DOI
- Munn, Luke, Liam Magee, and Vanicka Arora. 2023. “Truth Machines: Synthesizing Veracity in ai Language Models.” ai & Society. https://doi.org/10.1007/s00146-023-01756-4. · DOI · Paper page
- Ouchchy, Lydia, Alexandre Coin, and Veljko Dubljević. 2020. “ai in the Headlines: The Portrayal of the Ethical Issues of Artificial Intelligence in the Media.” ai & Society 35: 927–936. https://doi.org/10.1007/s00146-020-00965-5. · DOI
- Ouyang, Long, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. “Training Language Models to Follow Instructions with Human Feedback.” arXiv preprint arXiv:2203.02155. · arXiv
- Pauncefort, Elizabeth. 2021. “Critical Literacy Is at the Heart of the Answer.” In Media, Technology and Education in a Post-Truth Society, edited by Alex Grech, 73–94. Bingley, UK: Emerald Publishing Limited.
- Pavlik, John V. 2023. “Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education.” Journalism & Mass Communication Educator 78 (1): 84–93. https://doi.org/10.1177/10776958221149577. · DOI · Paper page
- Russell, Bertrand. 1912. The Problems of Philosophy. London: Oxford University Press.
- Salvagno, Michele, Fabio Silvio Taccone, and Alberto Giovanni Gerli. 2023. “Artificial Intelligence Hallucinations.” Critical Care 27: 180. https://doi.org/10.1186/s13054-023-04473-y. · DOI · Paper page
- Soames, Scott. 1984. “What Is a Theory of Truth?” The Journal of Philosophy 81 (8): 411–429.
- Vergeer, Maurice. 2020. “Artificial Intelligence in the Dutch Press: An Analysis of Topics and Trends.” Communication Studies 71 (3): 373–392. https://doi.org/10.1080/10510974.2020.1733038. · DOI
- Vision, Gerald. 2004. Veritas: The Correspondence Theory and Its Critics. Cambridge, MA: mit Press.
- Walker, Ralph C. S. 1989. The Coherence Theory of Truth. London: Routledge.
- Xu, Ruiyun, Yue (Katherine) Feng, and Hailiang Chen. 2023. “ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience.” ssrn Electronic Journal. https://doi.org/10.2139/ssrn.4498671. · DOI
- Yu, Chengcheng, Jinzhe Yan, and Na Cai. 2024. “ChatGPT in Higher Education: Factors Influencing ChatGPT User Satisfaction and Continued Use Intention.” Frontiers in Education 9: 1354929. https://doi.org/10.3389/feduc.2024.1354929. · DOI
- Yu, Hao. 2024. “The Application and Challenges of ChatGPT in Educational Transformation: New Demands for Teachers’ Roles.” Heliyon 10 (2): e24289. https://doi.org/10.1016/j.heliyon.2024.e24289. · DOI · Paper page
- Yu, Ping, Hua Xu, Xia Hu, and Chao Deng. 2023. “Leveraging Generative ai and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.” Healthcare 11 (20): 2776. https://doi.org/10.3390/healthcare11202776. · DOI
Cited by
Sorted by citations
Yrymbayeva, N., Yermekova, M., Tezekova, O., Issatayeva, B., Zhubandykova, A., & Tuxanbayev, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1809360 · Cited by 2
Metadata
- Authors
- Slutskiy, P.
- Affiliations
- Chulalongkorn University (TH)
- Corresponding author
- Slutskiy, Pavel
- ORCID
- Slutskiy, Pavel
- Venue
- Manusya: Journal of Humanities
- Published
- 2025-11-18
- Volume
- 28
- Issue
- 1
- Pages
- 1-19
- ISSN
- 0859-9920 (ISSN-L)
- 2665-9077
- OpenAlex
- W4417517141
- Cited by
- 2
- References
- 3
- Citations by year
- 2026: 2
- FWCI
- 2.29
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Manusya Journal of Humanities (publishedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Keywords
- artificial intelligence, post-truth, information seeking, Thai university students, AI-generated content, verificationism, Rudolf Carnap, academic integrity, critical appraisal, source validation, ChatGPT, Wikipedia, information source preferences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review