Student Learning Autonomy Transformation through the ‘Source-Grounded AI’ Feature of NotebookLM in the Basic Japanese Course I (A Case Study of International Relations Students at Universitas Jenderal Achmad Yani)
DOI:
https://doi.org/10.71435/738191Keywords:
Notebooklm, Source-Grounded AI, Learner AutonomyAbstract
The increasing use of artificial intelligence in higher education has raised concerns about misinformation, overdependence, and the weakening of learner agency. This qualitative case study explores how NotebookLM, as a source-grounded AI tool, supports learner autonomy in Basic Japanese learning among International Relations students at Universitas Jenderal Achmad Yani. The study was conducted in a Basic Japanese 1 course, where lecturer-curated materials were uploaded to the Learning Management System and used by students as the main knowledge base for NotebookLM-assisted learning. Data were collected through digital engagement observations, student reflective logs, and document analysis of Japanese curriculum vitae assignments. The findings show that NotebookLM supported students in verifying basic Japanese vocabulary, reviewing unfamiliar writing forms, revisiting course materials, and applying source-based expressions to personalized learning outputs. Evidence of learner autonomy appeared in students’ source consultation, independent vocabulary selection, self-monitoring, draft revision, and task-based decision-making. However, the study also found uneven patterns of AI use. Some students used NotebookLM critically as a learning support tool, while others tended to rely on it mainly for information retrieval. The findings suggest that NotebookLM does not automatically guarantee accuracy, eliminate AI hallucinations, or produce autonomous learners. Its pedagogical value depends on curated materials, clear task design, lecturer guidance, students’ digital literacy, and reflective learning practices. The study concludes that source-grounded AI can strengthen scaffolded autonomy when integrated responsibly into foreign language education, especially for beginner learners from non-language-major backgrounds. It offers a model for AI-assisted language learning that balances innovation, verification, and learner responsibility.
References
Qamar, M. T., Yasmeen, J., Pathak, S. K., Sohail, S. S., Madsen, D. Ø., & Rangarajan, M. (2024). Big claims, low outcomes: fact checking ChatGPT’s efficacy in handling linguistic creativity and ambiguity. Cogent arts & humanities, 11(1), 2353984. https://doi.org/10.1080/23311983.2024.2353984
Behzad, S. (2024). Language learning meets generative AI: Utilizing large language models for metalinguistic explanations. ProQuest LLC.
Torres-Martínez, S. (2025). Credence, attribution, and creativity in the construction of literary meaning with generative artificial intelligence. Language and Semiotic Studies, (0). https://doi.org/10.1515/lass-2024-0072
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., ... & Chen, E. (2025). A survey on knowledge-oriented retrieval-augmented generation. arXiv preprint arXiv:2503.10677.
Robison, E. (2025). Enhancing simulation prebriefing in nursing education using AI-generated podcast: An application of NotebookLM. Teaching and Learning in Nursing. https://doi.org/10.1016/j.teln.2025.10.014
Shoitan, R., Moussa, M. M., Tawfik, N., Cho, Y. I., & Abdallah, M. S. (2026). Exploring generative artificial intelligence: a comprehensive guide. PeerJ Computer Science, 12, e3276. https://doi.org/10.7717/peerj-cs.3276
Molina, E., & Medina, E. (2025). AI Revolution in Higher Education: What You Need to Know. Digital Innovations in Education. Brief N° 4. World Bank. https://doi.org/10.1596/43298
Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., ... & Chen, E. (2025). A survey on knowledge-oriented retrieval-augmented generation. arXiv preprint arXiv:2503.10677. https://doi.org/10.48550/arXiv.2503.10677
Abo El-Enen, M., Saad, S., & Nazmy, T. (2025). A survey on retrieval-augmentation generation (RAG) models for healthcare applications. Neural Computing and Applications, 37(33), 28191-28267. https://doi.org/10.1007/s00521-025-11666-9
Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R., & Nanayakkara, S. (2023). Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering. Transactions of the Association for Computational Linguistics, 11, 1-17. https://doi.org/10.1162/tacl_a_00530
Andriani, M., Udasmoro, W., Salsano, R., & Hardini, T. I. (2022). Stymie patterns: The case of French-language learning in Indonesian universities. Indonesian Journal of Applied Linguistics, 12(1), 180-189. https://doi.org/10.17509/ijal.v12i1.46548
Al-Jarf, R. (2026). A systematic self-review of studies on cultural learning, global issues, and pedagogical practices in second language contexts (2003–2025). International Journal of Cultural and Religious Studies, 6(3), 0932-0932. https://doi.org/10.32996/ijcrs.2026.6.3.2
Sakai, N. (2025). Natural Sciences or Humanities? A Case Study of Japanese University Students’ Awareness in Second Language Learning. Journal of English Language Teaching and Applied Linguistics, 7(2), 95-110. https://doi.org/10.32996/jeltal.2025.7.2.11
Haristiani, N., & Christinawati, D. (2024). Interlanguage Pragmatic Competence of University Students: An Error Analysis of Apology Speech Act Strategies in Japanese Learners. International Journal of Language Education, 8(1), 1-19. https://doi.org/10.26858/ijole.v8i1.60904
Mignon, C. (2022). Conception: The design and implementation of an online learning management system for secondary schools in Grenada. International Journal for Research in Applied Science and Engineering Technology.
Ivanova, T. (2026). Heterogeneous Ontology Repository for Intelligent E-Learning. Applied Sciences, 16(9), 4379.
Paraschiva, M. (2025). Developing learner autonomy in primary education: strategies and classroom practices. Journal of Romanian Literary Studies, (43), 850-855.
Little, D. (2022). Language learner autonomy: Rethinking language teaching. Language Teaching, 55(1), 64-73. https://doi.org/10.1017/S0261444820000488
Sawaguchi, R. (2025). Developing a CEFR-based diagnostic test to assess Japanese university students’ productive knowledge of lexical bundles. Language Testing in Asia, 15(1), 27. https://doi.org/10.1186/s40468-025-00361-0
Sato, T. (2022). Assessing critical thinking through L2 argumentative essays: an investigation of relevant and salient criteria from raters’ perspectives. Language Testing in Asia, 12(1), 9. https://doi.org/10.1186/s40468-022-00159-4
Sawaki, Y., Ishii, Y., Yamada, H., & Tokunaga, T. (2024). Developing and validating an online module for formative assessment of summary writing with automated content feedback for EFL academic writing instruction. Language Testing in Asia, 14(1), 50. https://doi.org/10.1186/s40468-024-00325-w
Nakrowi, Z. S., & Lumettu, A. (2025). Two Decades of Academic Writing Assessment in Higher Education: A Bibliometric and Technological Trend Analysis of Scopus (2000-2025). International Journal of Learning, Teaching and Educational Research, 24(10), 279-306. https://doi.org/10.26803/ijlter.24.10.13
Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025). Assessing student perceptions and use of instructor versus AI‐generated feedback. British Journal of Educational Technology, 56(3), 1074-1091. https://doi.org/10.1111/bjet.13558
Ziqi, C., Xinhua, Z., Qi, L., & Wei, W. (2026). L2 students’ barriers in engaging with form and content-focused AI-generated feedback in revising their compositions. Computer Assisted Language Learning, 39(3), 715-735. https://doi.org/10.1080/09588221.2024.2422478
Fleckenstein, J., Meyer, J., Jansen, T., Keller, S. D., Köller, O., & Möller, J. (2024). Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays. Computers and Education: Artificial Intelligence, 6, 100209. https://doi.org/10.1016/j.caeai.2024.100209
George, A. S. (2023). The potential of generative AI to reform graduate education. Partners Universal International Research Journal, 2(4), 36-50. https://doi.org/10.5281/zenodo.10421475
Hou, I., Mettille, S., Man, O., Li, Z., Zastudil, C., & MacNeil, S. (2024, January). The effects of generative AI on computing students’ help-seeking preferences. In Proceedings of the 26th australasian computing education conference (pp. 39-48). https://doi.org/10.1145/3636243.3636248
Burke, C. M. (2025). AI-assisted exam variant generation: A human-in-the-loop framework for automatic item creation. Education Sciences, 15(8), 1029. https://doi.org/10.3390/educsci15081029
Ho, W. Y. J. (2022). The construction of translanguaging space through digital multimodal composing: A case study of students' creation of instructional videos. Journal of English for Academic Purposes, 58, 101134. https://doi.org/10.1016/j.jeap.2022.101134
Kramer, A. W., Schaaf, J. V., & Huizenga, H. M. (2023). How much do you want to learn? High-school students' willingness to invest effort in valenced feedback-learning tasks. Learning and Individual Differences, 108, 102375. https://doi.org/10.1016/j.lindif.2023.102375
Al Mamun, M. A., & Lawrie, G. (2023). Student-content interactions: Exploring behavioural engagement with self-regulated inquiry-based online learning modules. Smart learning environments, 10(1), 1. https://doi.org/10.1186/s40561-022-00221-x
Jayaron, B. (2024). Educators' academic insights on artificial intelligence: Challenges and opportunities. Electronic Journal of eLearning. https://doi.org/10.34190/ejel.21.5.3272
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(102642), 1-63. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Motlagh, N. Y., Khajavi, M., Sharifi, A., & Ahmadi, M. (2023). The impact of artificial intelligence on the evolution of digital education: A comparative study of openAI text generation tools including ChatGPT, Bing Chat, Bard, and Ernie. arXiv preprint arXiv:2309.02029.
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j.tbench.2023.100115
Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. Technology, knowledge and learning, 1-31. https://doi.org/10.48550/arXiv.2312.09548
Creely, E., & Carabott, K. (2025). Teaching and learning with AI: An integrated AI-oriented pedagogical model. The Australian Educational Researcher, 52(6), 4633-4654. https://doi.org/10.1007/s13384-025-00913-6
Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160-1173. https://doi.org/10.1111/bjet.13337
Anyichie, A. C., & Butler, D. L. (2023, February). Examining culturally diverse learners’ motivation and engagement processes as situated in the context of a complex task. In Frontiers in Education (Vol. 8, p. 1041946). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1041946
Jayanti, E. D. (2023, November). English Teacher's Strategies to Foster Learner Autonomy in Online Learning Environment. In Proceedings of the 2023 7th International Conference on Education and E-Learning (pp. 73-78). https://doi.org/10.1145/3637989.3637994
Talaver, O. V., & Vakaliuk, T. A. (2024, May). A model for improving the accuracy of educational content created by generative AI. In AREdu (pp. 149-158).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Educia Journal

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


