It's Bad but It's Good: An Exploration of AI Cheating on Higher Educational Students Perspectives
Abstract
The ubiquitous artificial intelligence (AI) technology in the academic field is like a double-edged sword. The phenomenon has attracted researchers’ attention with more empirical evidence of the bright sights of AI adoption. To provide balanced views, this study aimed to extensively explore students’ perspectives on AI cheating behavior. The study consists of two phases, with a mixed-method design. The first phase adopts a quantitative in examining the relationship between external and internal factors in the decision to use AI technology being moderated by workplace spirituality. The second phase involves a qualitative approach aiming at gaining deeper perspectives on AI adoption among university students. The result of the first study revealed that only perceived injustice, intelligence, and self-efficacy affect AI cheating behavior. Meanwhile, spirituality lowers the possibility of AI cheating triggered by low intelligence and self-efficacy. The first phase of the study confirmed the adapted planned behavior framework in explaining students’ AI cheating mechanism. The second study further extends the study of understanding students’ AI cheating motivation, frequency, and consequences. Despite the unethical image and negative consequences, students still find it beneficial to their academic advancement. Finally, this study enhances the existing knowledge on AI cheating by setting the ground for further development of related constructs surrounding students’ AI cheating behavior. The finding suggested several ways universities could establish policies to mitigate students’ AI cheating behavior.
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Other students view AI as legitimate support tools that are accessible for anyone to use.
“Conversely, other students view AI as legitimate support tools that are accessible for anyone to use [ 84].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [84]
Students who depend heavily on AI struggle to construct sentences with the use of AI support, suggesting these tools risk displacing the development of essential academic skills.
“Notably, students who depend heavily on AI struggle to construct sentences with the use of AI support, suggesting that these tools risk displacing the development of essential academic skills [ 84].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [84]
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- Authors
- Sholikhah, Z., Pramuka, B. A., Adawiyah, W. R., Yanuar, I., Respati, Y. A., & Pramuka, M. F.
- Affiliations
- Yogyakarta State University (ID)
- Jenderal Soedirman University (ID)
- Risk Operation Department PT Alumak Indonesia,Jakarta,Indonesia
- Venue
- 2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC)
- Publisher
- IEEE
- Published
- 2024-09-25
- Pages
- 1-8
- OpenAlex
- W4411271706
- Cited by
- 4
- References
- 44
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 1.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC) (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
- Keywords
- generative AI in education, academic dishonesty, university students, workplace spirituality, perceived injustice, self-efficacy, artificial intelligence adoption, academic integrity policy
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