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All Journal Jurnal Riset dan Inovasi Pembelajaran
Made Susi Lissia Andayani
Informatics Engineering Education Study Program, Faculty of Engineering and Vocational Education, Universitas Pendidikan Ganesha, Bali, Indonesia
Chemistry Education Languange, Linguistic, Communication & Media Mathematics Other

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Generative AI Use Patterns and Ethical Dilemmas among Pre-Service Teachers Made Susi Lissia Andayani; Luh Putu Eka Damayanthi
Jurnal Riset dan Inovasi Pembelajaran Vol. 6 No. 2 (2026): May - August 2026
Publisher : Education and Talent Development Center Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/jrip.v6i2.5619

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.
Co-Authors Luh Putu Eka Damayanthi