Leveraging Large Language Models to Detect Academic Anxiety in Indonesian English for Specific Purposes Students through Reflective Writing
Keywords:
Academic anxiety; Indonesian ESP students; transformer models; reflective writing; deep learning; educational technologyAbstract
This study investigates the capacity of Large Language Models to identify academic anxiety in reflective writing produced by English for Specific Purposes students from Indonesia. It tackles two main issues: how well LLMs can identify anxiety from linguistic and environmental cues, and how anxiety-related language markers change depending on the type of activity and level of expertise. Employing a quantitative exploratory-correlational design, the study involved 600 undergraduate ESP students from Universitas Muhammadiyah Gresik. In addition to submitting two samples of reflective writing, each participant filled out a validated Academic Anxiety Inventory. To extract important language variables, such as lexical density, emotional Valence, modal usage, and syntactic complexity, transformer-based models (BERT, RoBERTa) were improved. Analytical reflections displayed greater lexical richness and syntactic complexity, but narrative reflections displayed more negative sentiment and hedging, according to MANOVA results, which demonstrated significant differences in anxiety markers. Higher-proficiency students demonstrated balanced rhetorical control and emotional tone, whereas lower-proficiency students exhibited greater signs of language anxiety. These results provide credence to the use of LLMs as non-invasive, scalable instruments for emotional diagnosis in ESP settings.
https://doi.org/10.26803/ijlter.24.12.17
References
Abdallah, D. M. M. S. (2024). Using Self-Regulated Learning Supported by Artificial Intelligence (AI) Chatbots to Develop EFL Student Teachers’ Self-Expression and Reflective Writing Skills. Academic Journal of Faculty of Education, Assiut University, 40(9), 1–66. https://doi.org/10.21608/mfes.2024.389892
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024). Perils and opportunities in using large language models in psychological research. PNAS Nexus, 3(7), 1–14. https://doi.org/10.1093/pnasnexus/pgae245
Agyeman, N. Y. B. (2024). Deep learning in high schools: exploring pedagogical approaches for transformative education. Humanika, 24(2), 111–126. https://doi.org/10.21831/hum.v24i2.71350
Alfirevi?, N., Renduli?, D., Fošner, M., & Fošner, A. (2024). Educational Roles and Scenarios for Large Language Models: An Ethnographic Research Study of Artificial Intelligence. Informatics, 11(4). https://doi.org/10.3390/informatics11040078
Andrés, C., Urzúa, C., Ranjan, R., Eduardo, E., Saavedra, M., Badilla-quintana, M. G., Lepe-martínez, N., & Philominraj, A. (2025). Effects of AI-Assisted Feedback via Generative Chat on Academic Writing in Higher Education Students : A Systematic Review of the Literature. Education Sciences, 15(10), 1–16.
Arindra, M. Y., & Ardi, P. (2020). The correlation between students’ writing anxiety and the use of writing assessment rubrics. LEARN Journal: Language Education and Acquisition Research Network, 13(1), 76–93.
Avram, L., Cium?geanu, M. D., & Sava, F. A. (2024). Written Language: A Promising Gateway to Anxiety Disorders Assessment. SAGE Open, 14(2), 1–9. https://doi.org/10.1177/21582440241241420
Ben-Zion, Z., Witte, K., Jagadish, A. K., Duek, O., Harpaz-Rotem, I., Khorsandian, M. C., Burrer, A., Seifritz, E., Homan, P., Schulz, E., & Spiller, T. R. (2025). Assessing and alleviating state anxiety in large language models. Npj Digital Medicine, 8(1), 1–6. https://doi.org/10.1038/s41746-025-01512-6
Benu, N. N., Beeh, N., & Nenotek, S. A. (2025). Implementing Deep Learning in The Efl Classroom: Strategies For Fostering Mindful, Meaningful. Journal on Language, Education, Literature and Culture, 3(1), 70–85. https://doi.org/10.33323/l.v3i1.64
Bouwer, R., van Braak, M., & van der Veen, C. (2024). Dialogic writing in the upper grades of primary school: How to support peer feedback conversations that promote meaningful revisions. Learning and Instruction, 93(July), 101965. https://doi.org/10.1016/j.learninstruc.2024.101965
Chaudhari, Y. S., Chaudhari, M. Y., & Pawar, H. A. (2025). Generative AI in Scholarly Writing: Opportunities and Ethical Dilemmas. Trends in Scholarly Publishing, 4(1), 22–28. https://doi.org/10.21124/tsp.2025.22.28
Cheng, A., Calhoun, A., & Reedy, G. (2025). Artificial intelligence-assisted academic writing: recommendations for ethical use. Advances in Simulation, 10(1), 1–9. https://doi.org/10.1186/s41077-025-00350-6
Córdova-Esparza, D. M. (2025). AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges. Information (Switzerland), 16(6). https://doi.org/10.3390/info16060469
Deng, C., Duan, Y., Jin, X., Chang, H., Tian, Y., Liu, H., Wang, Y., Gao, K., Zou, H. P., Jin, Y., Xiao, Y., Wu, S., Xie, Z., Lyu, W., He, S., Cheng, L., Wang, H., & Zhuang, J. (2024). Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey. CsCL, 1–52. http://arxiv.org/abs/2406.05392
Joshy, A., & Sundar, S. (2022). Analyzing the Performance of Sentiment Analysis using BERT, DistilBERT, and RoBERTa. 2022 IEEE International Power and Renewable Energy Conference, IPRECON 2022, December 2022, 1–6. https://doi.org/10.1109/IPRECON55716.2022.10059542
Ke, L., Tong, S., Cheng, P., & Peng, K. (2025). Exploring the frontiers of LLMs in psychological applications: a comprehensive review. Artificial Intelligence Review, 58(10). https://doi.org/10.1007/s10462-025-11297-5
Madeamin, R. (2025). Transformation of Indonesian language learning : Integration of Desfi Yenti, & Roza Susanti. (2025). Students’ Writing Anxiety in Academic Writing Practice. ELP (Journal of English Language Pedagogy), 10(1), 36–44. https://doi.org/10.36665/elp.v10i1.988
Di Pardo Léon-Henri, D. (2024). Multimodal reflective journals and life writing: A didactic approach to enhanced learning. Innovative Language Teaching Practices in Higher Education in a Post-Covid Era, 49–72. https://doi.org/10.5334/bdd.d
Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00425-2
Fullan, M., & Langworthy, M. (2014). New Pedagogies for Deep Learning Getting Started Guide 2023. Education in Motion (New Pedagogies for Deep Learning. https://deep-learning.global/wp-content/uploads/2023/05/New-Pedagogies-for-Deep-Learning-Getting-Started-Guide-2023.pdf
Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., & Alshemaimri, B. (2025). BERT applications in natural language processing: a review. Artificial Intelligence Review, 58(6). https://doi.org/10.1007/s10462-025-11162-5
Guizani, S., Mazhar, T., Shahzad, T., Ahmad, W., Bibi, A., & Hamam, H. (2025). A systematic literature review to implement large language model in higher education: issues and solutions. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00424-7
Jacobsen, L. J., & Weber, K. E. (2025). The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback. AI (Switzerland), 6(2). https://doi.org/10.3390/ai6020035
deep learning strategy with character education in the era of independent learning. IJNI: International Journal of Nusantara Islam, 13(2), 229–244. https://doi.org/10.15575/ijni.v13i2.46155
Mah, C., Tan, M., Phalen, L., Sparks, A., & Demszky, D. (2025). From Sentence-Corrections to Deeper Dialogue: Qualitative Insights from LLM and Teacher Feedback on Student Writing. EdWorkingPaper No. 25-1193. Annenberg Institute for School Reform at Brown University, 25. https://www.proquest.com/reports/sentence-corrections-deeper-dialogue-qualitative/docview/3237401553/se-2?accountid=10113%0Ahttps://resolver.ebscohost.com/openurl?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&rfr_id=info:sid/ProQ%3Aeric&rft_val_fmt=info:
Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers and Education: Artificial Intelligence, 6(January 2024), 100199. https://doi.org/10.1016/j.caeai.2023.100199
Mohammed, S. J., & Khalid, M. W. (2025). Under the world of AI-generated feedback on writing: mirroring motivation, foreign language peace of mind, trait emotional intelligence, and writing development. Language Testing in Asia, 15(1). https://doi.org/10.1186/s40468-025-00343-2
Molinari, A., & Molinari, E. (2024). the Added Value of Academic Writing Instruction in the Age of Large Language Models: a Critical Analysis. Iadis International Journal on Www/Internet, 22(1), 44–58. https://doi.org/10.33965/ijwi_2024220104
Peláez-Sánchez, I. C., Velarde-Camaqui, D., & Glasserman-Morales, L. D. (2024). The impact of large language models on higher education: exploring the connection between AI and Education 4.0. Frontiers in Education, 9(June). https://doi.org/10.3389/feduc.2024.1392091
Praveen, B., & Abhishek, N. (2024). A Systematic Review on Indian Education System & NEP 2020. International Journal of Research Publication and Reviews, 5(1), 1858–1865. https://doi.org/10.55248/gengpi.5.0124.0228
Prihandoko, L. A., Djatmika, D., Nurkamto, J., Morganna, R., Amalia, S. N., & Ardi, P. (2024). Paving the Road to Writing Success: Self-efficacy, Metacognition, a Growth Mindset, and Academic Writing Performance among Indonesian EFL Students. Pasaa, 68(June), 165–202. https://doi.org/10.58837/chula.pasaa.68.1.5
Rahman, M. M., Shiplu, A. I., Watanobe, Y., & Alam, M. A. (2025). RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis. IEEE Transactions on Emerging Topics in Computational Intelligence, 14(8), 1–18. https://doi.org/10.1109/TETCI.2025.3572150
Reddy, G. S. L., Kankhara, M. H., Maheshwari, M., Bansal, S., Kapoor, R., M, H. R., & Kumar, B. (2025). Simulating Emotional Intelligence in LLMs through Behavioral Conditioning and Analogical Retrieval. Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II), 81–91. https://doi.org/10.18653/v1/2025.analogyangle-1.7
Šafranj, J., Gak, D., & Bulatovi?, V. (2022). Anxiety and self-efficacy in ESP learning: Effects on academic success. Research in Pedagogy, 12(2), 447–460. https://doi.org/10.5937/istrped2202447s
Sun, J., Motevalli, S., Chan, N. N., & Khodi, A. (2024). Implementation of Self-regulated Learning Writing Module: Amplifying Motivation and Mitigating Anxiety among EFL Learners. Forum for Linguistic Studies, 6(3), 89–109. https://doi.org/10.30564/fls.v6i3.6600
Topalov, J., Kneževi?, L., & Halupka-Rešetar, S. (2023). How Anxious Are Online Esp Learners? Exploring Students’ Anxiety in Video, Audio and Text-Based Communication in an Online Classroom. ESP Today, 11(2), 395–416. https://doi.org/10.18485/ESPTODAY.2023.11.2.10
Wang, Q. (2024). EFL learners’ motivation and acceptance of using large language models in English academic writing: an extension of the UTAUT model. Frontiers in Psychology, 15(January), 1–12. https://doi.org/10.3389/fpsyg.2024.1514545
Weise, T., Demir, K. C., Pérez-Toro, P. A., Arias-Vergara, T., Maier, A., Nöth, E., Schuster, M., Heismann, B., & Yang, S. H. (2025). Towards End-to-End Speech Articulation and Spoken Language Analysis Using Deep Learning. Human-Centric Intelligent Systems, 5(1), 103–122. https://doi.org/10.1007/s44230-025-00094-6
Xu, Q. A., Chang, V., & Jayne, C. (2022). A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3(June), 100073. https://doi.org/10.1016/j.dajour.2022.100073
Yu, T. (2025). How to Use LLMs Ethically in Academic Writing? Education Insights, 2(5), 25–39. https://doi.org/10.70088/50wkze06
Yuhua, D. (2024). Integrating Deep Learning into English Language Teaching Within the Digital Cultural Framework. Computer-Aided Design and Applications, 21(S16), 71–84. https://doi.org/10.14733/cadaps.2024.S16.71-84
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