The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Students'perception of the use of AI Detector System by faculty members in determining the originality of submitted academic requirements
Abstract
Recent studies revealed an overwhelming concern about the misuse of generative Artificial Intelligence (AI) tools by students in completing academic requirements. The detection of AI-generated content using the naked eye was perceived to be difficult, hence the need for more accurate, reliable, and effective detection methods. As a countermeasure, educators are turning to AI content detectors and plagiarism checkers to ascertain the originality of submitted school requirements, raising concerns from students regarding the accuracy and reliability of these tools and the ethical implications and negative consequences of misclassification of genuinely original works as machine-generated outputs. By employing a holistic case study approach, the authors attempted to determine the perceptions of selected university students on the use of AI detection systems by faculty members in checking the originality and novelty of their academic outputs. Through the lenses of various normative ethical theories, the authors also analyzed the ethical issues and concerns raised by selected students to better understand their sentiments and the factors they believe could influence the faculty members' intention to adopt this emerging technology. The results of the study revealed that students have mixed attitudes and perceptions toward the faculty's intention to use AI detectors. While students perceived it as a means to encourage independent learning and critical thinking, they also expressed val…
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“Angeles et al. (2024) report heightened anxiety and distrust when work is flagged before human review.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Angeles et al. (2024)
Citations
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Cited by
Sorted by citations
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025) · Education Sciences · doi:10.3390/educsci15050611 · Cited by 79
GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
Metadata
- Authors
- Angeles, C. N., Samson, B. D., Mama, B. R. Z. I., Luriaga, R. L., Delizo, J. P. D., & Ching, M. R. D.
- Affiliations
- University of the Philippines Manila (PH)
- Cavite State University (PH)
- Mindanao State University - Maguindanao (PH)
- Visayas State University (PH)
- De La Salle University (PH)
- ORCID
- Venue
- Proceedings of the 2024 8th International Conference on E-Commerce, E-Business, and E-Government
- Publisher
- ACM
- Published
- 2024-05-28
- Pages
- 56-61
- OpenAlex
- W4402210688
- Cited by
- 4
- References
- 8
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 1.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on E-Commerce, E-Business and E-Government (ICEEG) (publishedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- AI-generated text detection, generative AI misuse, student perceptions, academic integrity, originality assessment, plagiarism detection, ethical considerations, normative ethics, technology adoption barriers, teacher-student trust, university students
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