Ethical Awareness and Algorithmic Bias in AI-IDLE: Perspectives of Sharia Faculty Students at UIN Raden Intan Lampung
DOI:
https://doi.org/10.63324/lec.3v.1i.139Keywords:
AI-IDLE, algorithmic bias, critical AI literacy, ethical awarenessAbstract
Generative AI tools are increasingly used for AI-mediated Informal Digital Learning of English (AI-IDLE), yet research has paid limited attention to how learners negotiate ethical responsibility and algorithmic bias during everyday, out-of-class use, particularly in value-oriented faculties. This qualitative study examined ethical awareness and perceived algorithmic bias in AI-IDLE among Sharia Faculty students at UIN Raden Intan Lampung. Using a qualitative descriptive case study design, twelve undergraduates were recruited through purposive maximum-variation sampling. Data were generated from semi-structured interviews and a curated set of anonymized AI interaction artifacts (e.g., prompts and outputs for explanation, drafting, and translation), supported by brief artifact walkthroughs when feasible. Data were analyzed via reflexive thematic analysis with iterative coding, memoing, and cross-source comparison to strengthen interpretive transparency. Findings showed that ethical awareness operated as context-sensitive boundary-making: students differentiated AI as a learning scaffold versus a substitute for intellectual labor, tightened limits in assessment-adjacent tasks, and reclaimed authorship through substantive rewriting to preserve voice and stance. Ethical reasoning also included privacy stewardship through data minimization in prompts, though awareness varied. Regarding bias, students most often noted Western-centric framing in value-laden examples and language standardization that diluted pragmatic or culturally embedded meanings. Fluent outputs produced an authority effect, but many participants reported mitigation routines such as verification, requesting multiple viewpoints, and re-prompting for Indonesian/Islamic contextualization. The study implies that AI-IDLE guidance should integrate ethical boundary-setting, privacy-aware prompting, and critical AI literacy practices to support responsible and culturally responsive informal language learning.
References
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411. https://doi.org/10.1016/j.ijer.2024.102411
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage Publications.
Casal, J. E., & Kessler, M. (2023). Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing. Research Methods in Applied Linguistics, 2(3), 100068. https://doi.org/10.1016/j.rmal.2023.100068
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/J.CHB.2022.107468
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Guan, L., Li, S., & Gu, M. M. (2024). AI in informal digital English learning: A meta-analysis of its effectiveness on proficiency, motivation, and self-regulation. Computers and Education: Artificial Intelligence, 7, 100323. https://doi.org/10.1016/j.caeai.2024.100323
Guan, L., Zhang, E. Y., & Gu, M. M. (2025). Examining generative AI–mediated informal digital learning of English practices with social cognitive theory: a mixed-methods study. ReCALL, 37(3), 315–331. https://doi.org/10.1017/S0958344024000259
Guo, X. (2024). Facilitator or thinking inhibitor: understanding the role of ChatGPT-generated written corrective feedback in language learning. Interactive Learning Environments, 32, 1–19. https://doi.org/10.1080/10494820.2024.2445177
Hastomo, T., Widiati, U., Ivone, F. M., & Zen, E. L. (2025). EFL teachers’ self-efficacy in using AI tools: A comparative study in Indonesia. Advanced Education, 19(27), 103–123. https://doi.org/10.20535/2410-8286.335502
Hastomo, T., Widiati, U., Ivone, F. M., Zen, E. L., Hasbi, M., & Khulel, B. (2025). AI-powered conversational agents and intercultural learning: Insights from Indonesian EFL students. Intercultural Communication Education, 8(1), 103217. https://doi.org/10.29140/ice.v8n1.103127
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
Kundi, B., Morr, C. El, Gorman, R., & Dua, E. (2022). Artificial Intelligence and Bias: A Scoping Review. In AI and Society (1st ed.). Chapman and Hall/CRC.
Liu, G. L., Darvin, R., & Ma, C. (2024). Unpacking the role of motivation and enjoyment in AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation in the Chinese context. Computers in Human Behavior, 160, 108362. https://doi.org/10.1016/j.chb.2024.108362
Liu, G. L., Darvin, R., & Ma, C. (2025). Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer Assisted Language Learning, 38(7), 1632–1660. https://doi.org/10.1080/09588221.2024.2310288
Liu, G., & Ma, C. (2023). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2023.2240316
Long, D., Roberts, J., Magerko, B., Holstein, K., DiPaola, D., & Martin, F. (2023). AI literacy: Finding common threads between education, design, policy, and explainability. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3544549.3573808
Mandasari, B., Basthomi, Y., Hastomo, T., Afrianto, Hamzah, I., & Aminatun, D. (2025). The snapshots of Indonesian pre-service English teachers’ perspectives on integrating technology-based tools to rural schools. Voices of English Language Education Society, 9(1), 42–57. https://doi.org/10.29408/veles.v9i1.27965
Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2), 1–17. https://doi.org/10.1080/2331186X.2023.2236469
Rapanta, C. (2025). Postdigital citizenship education and argumentation: Can AI support students’ critical literacy? In J. Zajda & A. Rapoport (Eds.), Discourses of Globalisation and Citizenship Education. Globalisation, Comparative Education and Policy Research (Vol. 46, pp. 69–86). Springer. https://doi.org/10.1007/978-3-031-86145-1_5
Sawalha, G., Taj, I., & Shoufan, A. (2024). Analyzing student prompts and their effect on ChatGPT’s performance. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2397200
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9). https://doi.org/10.1093/pnasnexus/pgae346
Yin, R. K. (2018). Case study research and applications: Design and methods. Sage Publications.
Zadorozhnyy, A., & Lee, J. S. (2025). Comparing classroom and digital settings: the role of basic psychological needs on EFL students’ IDLE engagement. ReCALL, 1–18. https://doi.org/10.1017/S0958344025000059
