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Research Article

Artificial Intelligence–Driven Learning in Criminology Education: Students’ Perceptions of AI Presence, Acceptance and Risks

741 reads
Charlie Anselmo , Jonnel Concepcion , Queenie Mae Elcano , Stephanie Kyle Manuel , Giovannie Belen , Ghe Carillo , Kathleen Ann Bagon , Marlon Bagon
Psych Educ Multidisc J, 2026, 52 (9), 1067-1077, DOI: https://doi.org/10.70838/pemj.520905, ISSN 2822-4353
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Abstract

This study examined the perceived presence, acceptance, and risks of AI-driven learning among BS Criminology students, addressing the growing integration of artificial intelligence in higher education and the limited empirical evidence in criminology education. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study employed a quantitative descriptive–correlational design involving 201 criminology students selected through purposive sampling. Data were gathered using a validated survey instrument and analyzed using descriptive statistics, Pearson’s correlation, and multiple linear regression, with reliability and assumption checks conducted before inferential analyses. The results indicated a high perceived AI presence in terms of performance expectancy, pedagogical support, information accuracy, and facilitating conditions, alongside moderate to high levels of acceptance reflected in behavioral intention, actual use, and satisfaction. A strong positive relationship was found between perceived AI presence and acceptance (r = .83, p < .001). Regression analysis identified pedagogical support and facilitating conditions as the strongest predictors of acceptance, jointly explaining 78.4% of the variance. Students also demonstrated high awareness of the risks related to misinformation, overreliance, and academic integrity. The findings affirm the applicability of the UTAUT framework in criminology education and highlight that effective AI adoption depends on guided pedagogical integration, institutional support, and ethical safeguards. The study concluded that AI should function as a supplementary learning tool supported by structured instructional strategies and clear ethical guidelines.

Keywords perceived risk technology acceptance utaut criminology education Artificial intelligence in education AI-driven learning
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The team behind this paper

8 authors, 3 institutions.

This paper Isabela State University Isabela State University 1 author Bestlink College of the Philippines — Philippines Bestlink College of the… 5 authors Bestlink College of the Philippines Novaliches — Philippines Bestlink College of the… 2 authors Charlie Anselmo — corresponding author CA Charlie Anselmo ✉ Jonnel Concepcion JC Jonnel Concepcion Queenie Mae Elcano QE Queenie Mae Elcano Stephanie Kyle Manuel SM Stephanie Kyle Manuel Kathleen Ann Bagon KB Kathleen Ann Bagon Marlon Bagon MB Marlon Bagon Giovannie Belen GB Giovannie Belen Ghe Carillo GC Ghe Carillo

Readership

741 reads over 8 months.

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March 2026 October 2026

Related work on SciMatic

Closest of 8 articles sharing keywords with this one. A rarer shared keyword counts for more than a common one.

On SciChain

This article is token #7283 of SciMatic Articles (ERC-1155): 1,000,000 tokens, minted once, never more. The chain also stores a fingerprint (SHA-256) of the article's record, so anyone can check that it has not been altered.

HolderWalletTokens
SciMatic 0xc35709…277732 100,000 (10%)
Charlie Anselmo 0x046523…4f5907 112,500 (11.25%)
Jonnel Concepcion 0x04cF57…805C44 112,500 (11.25%)
Queenie Mae Elcano 0xCDfD72…dcA80D 112,500 (11.25%)
Stephanie Kyle Manuel 0x90BDaD…Ff1AeD 112,500 (11.25%)
Giovannie Belen 0x6d8431…d7A340 112,500 (11.25%)
Ghe Carillo 0xaA126b…612F38 112,500 (11.25%)
Kathleen Ann Bagon 0x0cC410…060b39 112,500 (11.25%)
Marlon Bagon 0x3B2914…656EFc 112,500 (11.25%)
Content hash 0x828026364bde33e631abd5fb718fcc990bbaf557189e52e6d021883ca88cdb32
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Bibliographic Information

Charlie Anselmo, Jonnel Concepcion, Queenie Mae Elcano, Stephanie Kyle Manuel, Giovannie Belen, Ghe Carillo, Kathleen Ann Bagon, Marlon Bagon, (2026). Artificial Intelligence–Driven Learning in Criminology Education: Students’ Perceptions of AI Presence, Acceptance and Risks, Psychology and Education: A Multidisciplinary Journal, 52(9): 1067-1077
Bibtex Citation
@article{charlie_anselmo2026pemj,
author = {Charlie Anselmo and Jonnel Concepcion and Queenie Mae Elcano and Stephanie Kyle Manuel and Giovannie Belen and Ghe Carillo and Kathleen Ann Bagon and Marlon Bagon},
title = {Artificial Intelligence–Driven Learning in Criminology Education: Students’ Perceptions of AI Presence, Acceptance and Risks},
journal = {Psychology and Education: A Multidisciplinary Journal},
year = {2026},
volume = {52},
number = {9},
pages = {1067-1077},
doi = {10.70838/pemj.520905},
url = {https://scimatic.org/show_manuscript/7283}
}
APA Citation
Anselmo, C., Concepcion, J., Elcano, Q.M., Manuel, S.K., Belen, G., Carillo, G., Bagon, K.A., Bagon, M., (2026). Artificial Intelligence–Driven Learning in Criminology Education: Students’ Perceptions of AI Presence, Acceptance and Risks. Psychology and Education: A Multidisciplinary Journal, 52(9), 1067-1077. https://doi.org/10.70838/pemj.520905

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