Research on the Impact of Generative AI on College Students' Honesty Behaviors
Abstract
about new forms of academic misconduct, posing new challenges to the construction of academic integrity systems in universities. Based on
the Theory of Planned Behavior, this paper constructs an analysis framework for academic integrity behaviors in the AI era, traces the development of generative AI technology, classifies college students' AI academic applications into three categories: reasonable use, mild violation,
and serious violation, and systematically reveals the logic of violation behaviors from three dimensions: the ambiguity of rules at the cognitive
level, the efficiency and pressure-driven motivation at the motivational level, and the insufficient technical countermeasures at the regulatory
level. The study further proposes governance paths from three aspects: strengthening students' academic integrity literacy, establishing an AIfriendly academic evaluation system, and constructing a full-chain integrity supervision mechanism.
Keywords
Full Text:
PDFReferences
[1] Turing A. Computing Machinery and Intelligence (1950)[J]. The Essential Turing, 2004:433-464.
[2] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Nets[J]. MIT Press, 2014. DOI:10.3156/JSOFT.29.5_177_2.
[3] Huang D, Wang X, Lei L. Research on the Mechanism and Path of AIGC Empowering Junior High School Students' Programming
Learning[J]. Curriculum and Teaching Methodology, 2026, 9(4).
[4] Liu G, Zhang X. Enhancing student creativity in AIGC design education: the mediating role of learning motivation in reverse engineering pedagogy[J]. Frontiers in Psychology, 2026, 171829753-1829753.
[5] Jing B, Yang H, Pi Z, et al. Exploring the impact of AIGC on learning performance: a moderated mediation analysis between systempaced and self-paced video learning[J]. Interactive Learning Environments, 2026, 34(3):1082-1098.
[6] Zeng L, Wang A, Huang Y, et al. Unveiling college students' adoption of AIGC in design learning: an integrated model[J]. Humanities
and Social Sciences Communications, 2026, 13(1):357-357.
[7] Wu J. AIGC Technology Enabling Innovative Approaches in Visual Design of Online Learning Resources[J]. Journal of Contemporary
Educational Research, 2025, 9(8):348-351.
[8] Hu Z, Wu Y, Chen S, et al. Research on the Practice of Reconstructing Classroom Teaching Mode Based on AIGC[J]. Innovative Applications of AI, 2025, 2(2):122-131.
[9] Guan M. Study of Behavioral Characteristics and Differences in Effectiveness of AIGC Use by College Students[J]. Scientific Journal of
Intelligent Systems Research, 2025, 7(6):74-85.
[10] Guo K, Zhan C, Li X. Factors influencing Chinese college students' intention to use AIGC: a study based on the UTAUT model[J]. International Journal of System Assurance Engineering and Management, 2025, 16(4):1-15.
DOI: http://dx.doi.org/10.70711/neet.v4i10.10304
Refbacks
- There are currently no refbacks.
