Exploring the Impact of Generative AI Literacy on Students’ Academic Performance: An Ethical and Academic Fraud Perspective
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Abstract
This study aims to analyze the impact of generative AI literacy on student academic performance using an ethical and academic fraud perspective. This research is motivated by the increasing use of generative AI technology among students to complete academic assignments. Generative AI tools such as ChatGPT, Deepseek, Blackbox, and others have become popular tools among students, but their use raises ethical dilemmas and the potential for academic fraud that can impact academic integrity. This study used a quantitative approach with data collection through questionnaires distributed to students to identify the extent to which generative AI literacy impacts student academic performance, both positively and negatively. The data analysis technique used in this study was Structural Equation Modeling-Variance Based Structural Equation Modeling (VB-SEM) with a Generalized Structured Component Analysis (GSCA) approach. Furthermore, this study also examines the ethical implications of using this technology and its relationship to academic fraud such as plagiarism and academic dishonesty. The results are expected to provide insights for educational institutions in formulating policies regarding the use of Generative AI that balance technological innovation and academic integrity. This research can also serve as a reference for students in using technology responsibly to support quality academic achievement.
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References
Agustian, R. D., Khasanah, E. F., Sari, S. Y., Ammaryafi, M. F., & Susilowati, N. (2025). Pengaruh Perceived Usefulness , Perceived Ease of Use dan Digital Literacy terhadap Penggunaan Artificial Intelligence oleh Mahasiswa di Era Pendidikan 4.0. 1–11.
Alfaridzi, A. M. ., Amara, E. T. ., Sari, N. M. A. ., Safitri, N. ., & Suhali, M. F. (2026). Determinasi Terhadap Harga Saham Studi Emperis Perusahaan Sub Sektor Otomotif yang Terdaftar di Bursa Efek Indonesia. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 176–181. https://doi.org/10.62421/jibema.v3i3.204
Ali, S., Suchiang, T., Saikia, T. P., & Gulzar, D. (2024). Perceived Benefits And Concerns Of AI Integration. Higher Education : Insights From India, 30(5), 656–668. https://doi.org/https://doi.org/10.53555/kuey.v30i5.5122
Amalia, Sembiring, S. (2024). Hubungan Tingkat Penalaran Moral dengan Kecurangan Akademik pada Mahasiswa Universitas Andalas. 8, 112–130.
Atalla, A. D. G., El-Ashry, A. M., & Mohamed Sobhi Mohamed, S. (2024). The moderating role of ethical awareness in the relationship between nurses’ artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study. BMC Nursing, 23(1), 1–11. https://doi.org/https://doi.org/10.1186/s12912-024-02143-0
Azwar, S. (2007). Sikap manusia: Teori dan pengukurannya.
Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
Bankins, S., & Formosa, P. (2023). The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work. Journal of Business Ethics, 185(4), 725–740.
Biswas, M. (2024). No Teaching Journalism & Mass Communication AI in Journalism and Communication Teaching : Use , Challenges and Opportunities.
Budiman, N. A. B. (2018). Perilaku Kecurangan Akademik Mahasiswa: Dimensi Fraud Diamond dan Gone Theory. Akuntabilitas: Jurnal Ilmu Akuntansi, 11(1), 75–90.
Burrohman, Aulia, N. (2025). ACADEMIC FRAUD AND ARTIFICIAL INTELLIGENCE : A FRAUD DIAMOND THEORY PERSPECTIVE WITH JIMEA. Jurnal Ilmiah MEA ( Manajemen , Ekonomi , Dan Akuntansi, 9(2), 1689–1711.
Chaieb, S., Garrouch, K., & Al-Ali, N. S. (2023). Perceptions of the use and benefits of artificial intelligence applications: survey study. Journal of Medical Artificial Intelligence, 63.
Chan, C. K. Y., & Colloton, T. (2024). Generative AI in Higher Education: The ChatGPT Effect. In Generative AI in Higher Education: The ChatGPT Effect. https://doi.org/https://doi.org/10.4324/9781003459026
Chukwuere. (2024). No The use of ChatGPT in higher education: The advantages and disadvantages. In Aleph (Vol. 87, Issue 1). https://repositorio.ufsc.br/xmlui/bitstream/handle/123456789/167638/341506.pdf?sequence=1&isAllowed=y%0Ahttps://repositorio.ufsm.br/bitstream/handle/1/8314/LOEBLEIN%2C LUCINEIA CARLA.pdf?sequence=1&isAllowed=y%0Ahttps://antigo.mdr.gov.br/saneamento/proees
Fari, D. ., Hidayat, S. ., Chelsea, T. ., Widjayanti, R. ., Hanifati, L. ., Lestari, S. T. ., & Sabiya, N. . (2026). Determinasi Terhadap Nilai Perusahaan. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 197–204. https://doi.org/10.62421/jibema.v3i3.205
Falebita, O. S., & Kok, P. J. (2024). Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes. Journal for STEM Education Research.
Floridi, L., & Cowls, J. (2022). No A unified framework of five principles for AI in society. Machine Learning and the City. Architecture and Urban Design, 535–545.
Fransiska, I. S., & Utami, H. (2019). Perilaku Kecurangan Akademik Mahasiswa : Perspektif Fraud Diamond Theory. 6(2), 316.
Gandasari, F., Koeswinda, A. S., Putri, A. K., & Putri, D. A. (2024). Etika Pemanfaatan Teknologi Artificial Intelligence dalam Penyusunan Tugas Mahasiswa. 6(5), 5572–5578.
Gerlich, M. (2023). No Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study. Social Sciences, 12(9).
GovTech. (2024). No Study: AI Used in 11% of Student Papers Since April 2023. Government Technology.
Guleria, A., Krishan, K., Sharma, V., & Kanchan, T. (2023). ChatGPT : ethical concerns and challenges in academics and research. https://doi.org/https://doi.org/10.3855/jidc.18738
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
Hirabayashi, S., Jain, R., Jurković, N., & Wu, G. (2024). Harvard Undergraduate Survey on Generative AI. http://arxiv.org/abs/2406.00833
Huang, C., Zhang, Z., Mao, B., & Yao, X. (2023). No An Overview of Artificial Intelligence Ethics. IEEE Transactions on Artificial Intelligence. 4(4), 799–819.
Hwang, H., Cho, G., Jin, M. J., Ryoo, J. H., Choi, Y., & Lee, S. H. (2021). No A knowledge-based multivariate statistical method for examining gene-brain-behavioral/ cognitive relationships: Imaging genetics generalized structured component analysis. PLoS ONE, 16(3), 1–28.
Ikmal, M. F., Sakinah, N., Nuril Jannah, F., Nury Mawaddah, N., & Hidayati, A. N. . (2026). Fintech Syariah Dalam Ekonomi Islam : Inovasi, Regulasi dan Impelementasi. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 182–196. https://doi.org/10.62421/jibema.v3i3.189
Kemendikbud. (2024). PANDUAN PENGGUNAAN GENERATIVE ARTIFICIAL INTELLIGENCE ( GenAI ).
Kuh, G. D., Kinzie, J., & Buckley, J. A. (2006). What Matters to Student Success : A Review of the Literature. Commissioned Report for the National Symposium on Postsecondary Student Success Spearheading a Dialog on Student Success. 18, 156. http://cpe.ky.gov/NR/rdonlyres/AFA304F0-C125-40C2-96E5-7A8C98915797/0/WhatMatterstoStudentSuccessAReviewoftheLiterature.pdf
Lee, J. E., & Maeng, U. (2023). No Perceptions of High School Students on AI Chatbots Use in English Learning: Benefits, Concerns, and Ethical Consideration. Journal of Pan-Pacific Association of Applied Linguistics, 27(2), 53–72.
Maphosa, V. (2024). No The Rise of Artificial Intelligence and Emerging Ethical and Social Concerns. December, 1–20.
Mustofa, R. H., Kuncoro, T. G., Atmono, D., Hermawan, H. D., & S. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8.
Mutiara, R. ., Alfuadi, M. K. ., Putri, F. M. ., Mardini, Manalu, N. P. ., Samosir, F. A. ., & Salsabilla, S. . (2026). Faktor-Faktor Yang Mempengaruhi Performa Karyawan Pada PT. Global Bintang Timur Express di Wilayah Tebet Jakarta. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 205–217. https://doi.org/10.62421/jibema.v3i3.206
Nasman, P. A. dan D. P. (2024). Filsafat, Politik dan Etika Hukum. Jurnal Hukum Lex Generalis, 5(10).
Octavia, A. C. B., & Nugraha, J. (2024). ). Influence of Enjoyment and Trust on the Use of Artificial Intelligence-based Voice Assistant in Vocational Students Using Technology Acceptance Model (TAM). Journal of Office Administration : Education and Practice, 4(1), 10–23.
Ofem, U. J., Owan, V. J., Iyam, M. A., Udeh, M. I., Anake, P. M., & Ovat, S. V. (2024). Students’ perceptions, attitudes and utilisation of ChatGPT for academic dishonesty: Multigroup analyses via PLS‒SEM. Education and Information Technologies, 159–187.
Pambudi. (2023). Pengaruh Perceived Ease of Use , Perceived Usefulness , dan Perceived Enjoyment terhadap Minat untuk terus Menggunakan Aplikasi Investasi di Indonesia. 20, 482–501.
Panagoulias, D. P., Virvou, M., & Tsihrintzis, G. A. (2023). An empirical study concerning the impact of perceived usefulness and ease of use on the adoption of AI-empowered medical applications. Proceedings - 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering. https://doi.org/https://doi.org/10.1109/BIBE60311.2023.00062
Pant, A., Hoda, R., Spiegler, S. V., Tantithamthavorn, C., & Turhan, B. (2024). Ethics in the Age of AI: An Analysis of AI Practitioners’ Awareness and Challenges. ACM Transactions on Software Engineering and Methodology, 33(3).
Patel, K. (2024). Ethical Reflections on Data-Centric AI: Balancing Benefits and Risks. International Journal of Artificial Intelligence Research and Development, 2(1), 1–17.
Putri, E., & Very, J. (2025). YUME : Journal of Management Extending the Technology Acceptance Model ( TAM ) in E- Commerce : The Impact of AI Awareness , Usability , and Trust on Shopee Adoption. 8(2), 1–10.
Raharjo,Rohmadi, S. H. (2025). Artificial Intelligence in Indonesian Education : A Critical Review of Ethical Considerations , Implementation Challenges , and Educational Management Perspectives. 10(1), 50–68.
Resnik, D. B., & Hosseini, M. (2024). The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool. AI and Ethics. https://doi.org/https://doi.org/10.1007/s43681-024-00493-8
Rios Campos, C., Cánova, E. S. M., Zaquinaula, I. R. A., Zaquinaula, H. E. A., Vargas, D. J. C., Peña, W. S., Idrogo, C. E. T., & Arteaga, R. M. Y. (2023). Artificial Intelligence and Education. South Florida Journal of Development, 4(2), 641–651.
Risnina, N. N., Permatasari, S. T. I., Nurulhusna, A. Z., Anjelita, F. M., Wulaningtyas, C., & Rakhmawati, N. A. (2023). Pengaruh ChatGPT Terhadap Proses Pembelajaran Mahasiswa di Institut Teknologi Sepuluh Nopember. Jurnal Pendidikan, Bahasa Dan Budaya, 2(4), 119–132.
Russell, S. J., Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
Salim, J. S., Agustina, T., & Khairany, S. J. (2025). Persepsi Mahasiswa Atas Kegunaan dan Kemudahan Teknologi Terhadap Adopsi Teknologi Kecerdasan Buatan dalam Akuntansi. 5(2), 434–441.
Saraswati, A. R., Karmina, V. A., Efendi, M. P., Candrakanti, Z., & Rakhmawati, N. A. (2023). Analisis Pengaruh ChatGPT Terhadap Tingkat Kemalasan Berpikir Mahasiswa ITS Dalam Proses Pengerjaan Tugas. Jurnal Pendidikan, Bahasa Dan Budaya, 2(4), 40–48.
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1).
Silva, J. M. D. D. E., Baridwan, Z., & Ximenes, P. (2022). Factors influencing academic cheating of accounting education in timor leste: theory of fraud triangle approach and theory of reasoned action. International Journal of Multicultural and Multireligious Understanding, 9(10), 444–455.
Sirait, N. C., Gustiningrum, V. ., Ananda, A. ., Widra, N. A. ., Cahyani, S. G. ., Putri, G. A. ., & Nurhadi, A. . (2026). Determinasi Mengenai Harga Saham. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 218–229. https://doi.org/10.62421/jibema.v3i3.182
Slameto. (2015). Belajar dan Faktor-faktor yang Mempengaruhi. Rineka Cipta.
Syahdina, A., Chan, M. ., Lesmana, R. F., Sinaga, Y. S., Oktavia, E., & Aryadafa, F. (2026). Determinasi Terhadap Kinerja Perusahaan. JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, Dan Akuntansi, 3(3), 165–175. https://doi.org/10.62421/jibema.v3i3.180
Torun, F. (2025). The Perspectives of Academicians and Students Regarding the Use of Generative Artificial Intelligence in Higher Education The Perspectives of Academicians and Students Regarding the Use of Generative Artificial Intelligence in Higher Education To cite thi. https://doi.org/https://doi.org/10.46328/ijte.883
UNESCO. (2023). Guidance for Generative AI in Education and Research.
Valdivieso, T., & González, O. (2025). Generative AI Tools in Salvadoran Higher Education: Balancing Equity, Ethics, and Knowledge Management in the Global South. Education Sciences, 15(2), 1–31.
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, 1–13.
Wood, D. A., Achhpilia, M. P., Adams, M. T., Aghazadeh, S., Akinyele, K., Akpan, M., Allee, K. D., Allen, A. M., Almer, E. D., Ames, D., Arity, V., Barr-Pulliam, D., Basoglu, K. A., Belnap, A., Bentley, J. W., Berg, T., Berglund, N. R., Berry, E., Bhandari, A., … Zoet, E. (2023). The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions? Issues in Accounting Education, 38(4), 81–108. https://doi.org/10.2308/ISSUES-2023-013
Zafar,Shaheen,Rehan. (2024). Use of ChatGPT and Generative AI in Higher Education : Opportunities , Obstacles and Impact on Student Performance. 5(1), 1–12.