Generative AI Use Patterns and Ethical Dilemmas among Pre-Service Teachers

https://doi.org/10.51574/jrip.v6i2.5619

Authors

  • Made Susi Lissia Andayani Informatics Engineering Education Study Program, Faculty of Engineering and Vocational Education, Universitas Pendidikan Ganesha, Bali, Indonesia
  • Luh Putu Eka Damayanthi Informatics Engineering Education Study Program, Faculty of Engineering and Vocational Education, Universitas Pendidikan Ganesha, Bali, Indonesia

Keywords:

Cognitive engagement, Ethical dilemmas, Generative AI, Learning, Pre-service teachers

Abstract

Generative Artificial Intelligence (GenAI) is increasingly embedded in higher-education learning, offering efficiency while raising questions about the boundary between technological assistance and delegation of thinking. This study explores GenAI-use patterns, ethical dilemmas, and cognitive engagement among pre-service teachers. An exploratory qualitative study involved 24 second-semester Informatics Engineering Education students selected purposively. Over six weeks, data were collected through semi-structured interviews, three focus-group discussions, classroom observations, and reviews of task artifacts and prompt histories. Thematic analysis involved familiarization, coding, category development, theme construction, and cross-source comparison. Three patterns emerged: Cognitive Partner (6 students; 25.0%), Practical Adaptor (13; 54.2%), and Passive Outsourcing (5; 20.8%). Eighteen students (75.0%) interpreted the absence of an explicit lecturer prohibition as implicit permission to use GenAI, while 16 (66.7%) reported anxiety about possible misclassification by AI detectors. The findings show that educational implications depend less on frequency of use than on verification, independent revision, and students' retention of cognitive responsibility. Teacher education should therefore frame GenAI as cognitive support, clarify task-specific boundaries, and require accountable verification rather than allow AI to substitute for learning.

Downloads

Download data is not yet available.

References

Alam, M. M., Farhaz, S., Haq, S. M. A., & Ferdous, M. (2026). Generative Artificial Intelligence integration in higher education: A constructivist learning theory approach. Computers and Education Open, 10, 100378. https://doi.org/10.1016/j.caeo.2026.100378

Aldemir, T., Kilinc, S., Bicer, A., Grant, P., Davis, T., & Sweany, N. W. (2025). Intelligent-TPACK in practice: design and evidence from a three-week teacher preparation module. Computers and Education Open, 9, 100306. https://doi.org/10.1016/j.caeo.2025.100306

Boos, J., Eder, T., & Lachner, A. (2026). Perceived utility moderates motivational intervention effects in learning to teach responsibly with GenAI. Computers and Education Open, 10, 100324. https://doi.org/10.1016/j.caeo.2025.100324

Borzanović, A., Simić, A., Papić-Blagojević, N., Klašnja-Milićević, A., Sanin, C., Levula, A., Islam, M. R., & Ivanović, M. (2026). Student perceptions of generative AI tools in higher education: A cross-regional study of use, ethics, and educational utility. Education and Information Technologies, 31(12), 4327–4367. https://doi.org/10.1007/s10639-026-13964-8

Cena, E., McParland, A., Toivo, W., Dalton, B., Mundy, M., O’Connor, P. A., Robertson, A. E., Swingler, M., Wilson, P., & Duncan, C. (2026). Studying with GenAI: Student views on the opportunities and risks of GenAI in higher education. Education and Information Technologies, 31(11), 3655–3681. https://doi.org/10.1007/s10639-026-13923-3

Dayagbil, F. T., Boholano, H. B., & Sumalinog, G. G. (2025). Are they in or out? Exploring pre-service teachers’ knowledge, perceptions, and experiences regarding artificial intelligence (AI) in teaching and learning. Frontiers in Education, 10, 1665205. https://doi.org/10.3389/feduc.2025.1665205

Espartinez, A. S. (2024). Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Computers and Education: Artificial Intelligence, 7, 100264. https://doi.org/10.1016/j.caeai.2024.100264

Gruenhagen, J. H., Sinclair, P. M., Carroll, J. A., Baker, P. R. A., Wilson, A., & Demant, D. (2024). The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence, 7, 100273. https://doi.org/10.1016/j.caeai.2024.100273

Hadinejad, N., Sperling, K., & McGrath, C. (2025). Generative AI chatbots in higher education: Student experiences and perceived ethical challenges. Computers and Education Open, 9, 100311. https://doi.org/10.1016/j.caeo.2025.100311

Huang, D., Hash, N., Cummings, J. J., & Prena, K. (2025). Academic cheating with generative AI: Exploring a moral extension of the theory of planned behavior. Computers and Education: Artificial Intelligence, 8, 100424. https://doi.org/10.1016/j.caeai.2025.100424

Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348

Kaharuddin, A., García García, J., Magfirah, I., & Yulismayanti, Y. (2025). Validating a TPCK-S instrument for hologram-based mathematics teaching. Indonesian Journal on Learning and Advanced Education (IJOLAE), 525–536.

Kim, E. (2026). From AI anxiety to strategic regulation: How university students transform generative AI into a strategic learning resource. Computers and Education: Artificial Intelligence, 10, 100622. https://doi.org/10.1016/j.caeai.2026.100622

Kofinas, A. K., Tsay, C. H. H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522–2549. https://doi.org/10.1111/bjet.13585

Kohnke, L., Zou, D., Lai, C., & Gu, M. M. (2026). Preparing pre-service teachers for responsible generative AI use: Curriculum implications for ethics, privacy, and AI literacy. Computers and Education: Artificial Intelligence, 10, 100617. https://doi.org/10.1016/j.caeai.2026.100617

Lindkvist, A., Curbo, S., & Hultgren, C. (2026). Choosing not to use generative AI in higher education: a mixed methods study of student reasoning, assessment design, and AI literacy. Frontiers in Education, 11, 1813306. https://doi.org/10.3389/feduc.2026.1813306

McPhee, S. W., & Jerowsky, M. (2025). Beyond technical skills: a pedagogical perspective on fostering critical engagement with generative AI in university classrooms. Frontiers in Education, 10, 1593278. https://doi.org/10.3389/feduc.2025.1593278

Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world's top-ranking universities. Computers and Education Open, 5, 100151. https://doi.org/10.1016/j.caeo.2023.100151

Mwakalinga, S. E., & Mabilika, F. A. (2025). Perceptions, pitfalls, and proposals for the ethical use of artificial intelligence in the classroom: a case study of students and educators (teachers’ and lecturers’). Cogent Education, 12(1), 2557611. https://doi.org/10.1080/2331186X.2025.2557611

Nartey, E. K. (2024). Guiding principles of generative AI for employability and learning in UK universities. Cogent Education, 11(1), 2357898. https://doi.org/10.1080/2331186X.2024.2357898

Nguyen, N. N., & Barbieri, W. (2026). Generative AI in work-integrated learning: Supporting pre-service teachers' emotional labour and self-management in Australian initial teacher education. British Journal of Educational Technology, 57(4), 1094–1114. https://doi.org/10.1111/bjet.70043

Ó Ceallaigh, T. J., & Murphy, S. (2026). Teaching the teachers: A systematic review of genAI-specific technological pedagogical knowledge (TPK) in teacher education. Computers and Education Open, 10, 100367. https://doi.org/10.1016/j.caeo.2026.100367

Prilop, C. N., Mah, D. K., Jacobsen, L. J., Hansen, R. R., Weber, K. E., & Hoya, F. (2025). Generative AI in teacher education: Educators’ perceptions of transformative potentials and the triadic nature of AI literacy explored through AI-enhanced methods. Computers and Education: Artificial Intelligence, 9, 100471. https://doi.org/10.1016/j.caeai.2025.100471

Seufert, S., Hartmann, P., & Spirgi, L. (2025). Fostering Intelligent-TPACK through AI-assistance: A multi-method study in pre-service teacher education. Computers and Education Open, 9, 100314. https://doi.org/10.1016/j.caeo.2025.100314

Silvola, A., Kajamaa, A., Merikko, J., & Muukkonen, H. (2025). AI-mediated sensemaking in higher education students’ learning processes: Tensions, sensemaking practices, and AI-assigned purposes. British Journal of Educational Technology, 56(5), 2001–2018. https://doi.org/10.1111/bjet.13606

Sofkova Hashemi, S. (2026). Students’ multimodal prompting practices as epistemic work in AI literacy development. Computers and Education: Artificial Intelligence, 11, 100635. https://doi.org/10.1016/j.caeai.2026.100635

Syamsuddin, S., Arniati, F., & Kaharuddin, A. (2025). Artificial intelligence and problem-based learning: Structural equation modeling evaluation of undergraduate critical thinking and academic performance. EduTransform: Multidisciplinary International Journal, 1(2), 25–31.

Tangkearung, S. S., Tulak, T., Kaharuddin, A., & SMBM, A. (2026). Exploring low reading interest and digital literacy among Indonesian pre-service teachers: A single-case study at a private university in South Sulawesi. Information Technology Education Journal, 15–32.

Tsao, J. (2025). Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers. Computers and Education: Artificial Intelligence, 9, 100496. https://doi.org/10.1016/j.caeai.2025.100496

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, 100326. https://doi.org/10.1016/j.caeai.2024.100326

Williams, R. T. (2023). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, 1331607. https://doi.org/10.3389/feduc.2023.1331607

Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 56(5), 1842–1863. https://doi.org/10.1111/bjet.13599

Yan, W., Nakajima, T., & Sawada, R. (2025). Beyond tool use: Tracking the evolution of generative AI literacy among university students through a process-oriented investigation. Computers and Education: Artificial Intelligence, 9, 100465. https://doi.org/10.1016/j.caeai.2025.100465

Downloads

Published

2026-08-20

How to Cite

Andayani, M. S. L., & Damayanthi, L. P. E. (2026). Generative AI Use Patterns and Ethical Dilemmas among Pre-Service Teachers. Jurnal Riset Dan Inovasi Pembelajaran, 6(2), 849–864. https://doi.org/10.51574/jrip.v6i2.5619