Enhancing student academic services through AI-driven virtual assistants using the RAG method at Universitas Terbuka
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The Indonesian Open University currently operates an information service called Hallo UT, which mainly provides academic and administrative information. To improve this service, this study develops a virtual assistant chatbot capable of delivering autonomous 24-hour customer support using a Retrieval-Augmented Generation (RAG) approach. RAG combines information retrieval techniques with large language model capabilities to generate accurate and contextually relevant responses. Data were collected from academic manuals containing frequently asked questions and questionnaires distributed to 76 students. The chatbot was evaluated based on accuracy, response time, and user satisfaction. Results show that the system achieved an average accuracy rate of 92% with an average response time of 5 seconds. In addition, 62% of students responded positively to the chatbot’s functionality. These findings demonstrate the chatbot’s potential to improve student engagement, reduce administrative workload, and enhance the overall learning experience. Future research should involve larger samples, multilingual support, and broader system integration.
Abrishami, M. S., Eshratifar, A. E., Eigen, D., Wang, Y., Nazarian, S., & Pedram, M. (2020). Efficient Training of deep convolutional neural networks by augmentation in embedding space. arXiv, 1-5. https://doi.org/10.48550/arXiv.2002.04776
Abu-Salih, B., & Alotaibi, S. (2024). A systematic literature review of knowledge graph construction and application in education. Heliyon, 10(3), 1-23. https://doi.org/10.1016/j.heliyon.2024.e25383
Baha, T. A., El Hajji, M., Es-Saady, Y., & Fadili, H. (2024). The impact of educational chatbot on student learning experience. Education and Information Technologies, 29(8), 10153-10176. https://doi.org/10.1007/s10639-023-12166-w
Barakat, B., & Huang, Q. (2023). Improving reliability of fine-tuning with block-wise optimisation. arXiv, 20(20), 1-10. https://doi.org/10.48550/arXiv.2301.06133
Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Using chatbots as AI conversational partners in language learning. Applied Sciences, 12(17), 1-16. https://doi.org/10.3390/app12178427
Berkat. (2024). Education transformation in Palangka Raya: Optimizing technology management through artificial intelligence towards quality improvement. JTP-Jurnal Teknologi Pendidikan, 26(1), 131-140. https://doi.org/10.21009/jtp.v26i1.43402
Castro, G. P. B., Chiappe, A., Becerra Rodriguez, D. F., & Sepulveda, F. G. (2024). Harnessing AI for education 4.0: Drivers of personalized learning. Electronic Journal of e-Learning, 22(5), 01-14. https://doi.org/10.34190/ejel.22.5.3467
Chheang, V., Sharmin, S., Márquez-Hernández, R., Patel, M., Rajasekaran, D., Caulfield, G., Kiafar, B., Li, J., Kullu, P., & Barmaki, R. L. (2024). Towards anatomy education with generative ai-based virtual assistants in immersive virtual reality environments. arXiv, 1-11. https://doi.org/10.48550/arXiv.2306.17278
Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 1-12. https://doi.org/10.3390/app13053056
Dongbo, M., Miniaoui, S., Fen, L., Althubiti, S. A., & Alsenani, T. R. (2023). Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms. Information Processing & Management, 60(5), 103440. https://doi.org/10.1016/j.ipm.2023.103440
Gill, S. S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., Fuller, S., Singh, M., Arora, P., Parlikad, A. K., Stankovski, V., Abraham, A., Ghosh, S. K., Lutfiyya, H., Kanhere, S. S., Bahsoon, R., Rana, O., Dustdar, S., Sakellariou, R., Uhlig, S., &
Buyya, R. (2024). Transformative effects of chatGPT on modern education: Emerging era of AI chatbots. Internet of Things and Cyber-Physical Systems, 4, 19-23. https://doi.org/10.1016/j.iotcps.2023.06.002
Goyal, S., Kumar, A., Garg, S., Kolter, Z., & Raghunathan, A. (2023). Finetune like you pretrain: Improved finetuning of zero-shot vision models. arXiv, 1-19. https://doi.org/10.48550/arXiv.2212.00638
Hajipour, V., Hekmat, S., & Amini, M. (2023). A value-oriented artificial intelligence-as-a-service business plan using integrated tools and services. Decision Analytics Journal, 8, 1-15. https://doi.org/10.1016/j.dajour.2023.100302
Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023). Active retrieval augmented generation. arXiv, 1-24. https://doi.org/10.48550/arXiv.2305.06983
Kadaruddin, K. (2023). Empowering education through generative AI: Innovative instructional strategies for tomorrow's learners. International Journal of Business, Law, and Education, 4(2), 618-625. https://doi.org/10.56442/ijble.v4i2.215
Kashive, N., Powale, L., & Kashive, K. (2021). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/IJILT-05-2020-0090
Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., Lu, G., Li, S., Liu, T., & Zhai, X. (2024). AGI: Artificial general Intelligence for education. arXiv, 1-19. https://doi.org/10.48550/arXiv.2304.12479
Lee, G.-G., Latif, E., Wu, X., Liu, N., & Zhai, X. (2024). Applying large language models and chain-of-thought for automatic scoring. Computers and Education: Artificial Intelligence, 6, 1-15. https://doi.org/10.1016/j.caeai.2024.100213
Lin, M. P.-C., Chang, D. H., & Winne, P. H. (2025). A proposed methodology for investigating student-chatbot interaction patterns in giving peer feedback. Educational Technology Research and Development, 73(1), 353-386. https://doi.org/10.1007/s11423-024-10408-3
Liu, X. (2021). Model optimization techniques for embedded artificial intelligence. 2021 2nd International Conference on Computing and Data Science (CDS), pp. 1-6. https://doi.org/10.1109/CDS52072.2021.00008
Mahajan, C., Krishnan, A., Nambiar, M., & Singhal, R. (2022). Hetero-rec: Optimal deployment of embeddings for high-speed recommendations. AIMLSystems '22: Proceedings of the Second International Conference on AI-ML Systems. 11, 1-9. https://doi.org/10.1145/3564121.3564134
Muhammad Rahaji, J., & Sudianto, S. (2023). Implementation of chatbot for merdeka belajar kampus merdeka program using long short-term memory. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 12(2), 253-262. https://doi.org/10.23887/janapati.v12i2.58794
Nasa-Ngium, P., Nuankaew, W. S., & Nuankaew, P. (2023). Analyzing and tracking student educational program interests on social media with chatbots platform and text analytics. International Journal of Interactive Mobile
Technologies (iJIM), 17(05), 4-21. https://doi.org/10.3991/ijim.v17i05.31593
Nguyen, B.-S., Le, D. T., Vu, H. M., Nguyen, T. A. D., Nguyen, M.-T., & Le, H. (2022). Improving document image understanding with reinforcement finetuning. arXiv. http://arxiv.org/abs/2209.12561
Popescu, R.-I., Sabie, O. M., & Trușcă, M. I. (2023). The contribution of artificial intelligence to stimulating the innovation of educational services and university programs in public administration. Transylvanian Review of Administrative Sciences, 19(70), 85-108. https://doi.org/10.24193/tras.70E.5
Purchase, S., Zhao, A., & Mullins, R. D. (2022). Revisiting embeddings for graph neural networks. arXiv. http://arxiv.org/abs/2209.09338
Ramakrishnan, R., Thangamuthu, P., Nguyen, A., & Gao, J. (2024). Revolutionizing campus communication: NLP-powered university chatbots. International Journal of Advanced Computer Science and Applications, 15(6), 1-12. https://dx.doi.org/10.14569/IJACSA.2024.0150606
Ranjit, M., Ganapathy, G., Manuel, R., & Ganu, T. (2023). Retrieval augmented chest X-Ray Report generation using openAI GPT models. arXiv. http://arxiv.org/abs/2305.03660
Rasheed, Z., Ghwanmeh, S., & Abualkishik, A. Z. (2023). Harnessing Artificial intelligence for personalized learning: A systematic review. Data and Metadata, 2(146), 1-15. https://doi.org/10.56294/dm2023146
Rios-Campos, C., Cánova, E. S. M., Zaquinaula, I. R. A., Zaquinaula, H. E. A., Vargas, D. J. C., Peña, W. S., Idrogo, C. E. T., & Arteaga, R. M. Y. (2023). Artificial intelligence and education. South Florida Journal of Development, 4(2), 641-655. https://doi.org/10.46932/sfjdv4n2-001
Saetia, C., & Vateekul, P. (2018). Enhance accuracy of hierarchical text categorization based on deep learning network using embedding strategies. 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1-6. http://dx.doi.org/10.1109/jcsse.2018.8457376
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 1-23. https://doi.org/10.3390/info15100596
Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2023). Platform-independent and curriculum-oriented intelligent assistant for higher education. International Journal of Educational Technology in Higher Education, 20(42), 1-24. https://doi.org/10.1186/s41239-023-00412-7
Santos, G. A., de Andrade, G. G., Silva, G. R. S., Duarte, F. C. M., Costa, J. P. J. D., & de Sousa, R. T. (2022). A conversation-driven approach for chatbot management. IEEE Access, 10, 8474-8486. https://doi.org/10.1109/ACCESS.2022.3143323
Seraquive, J. E. G., Álvarez-Muñoz, P., Palacios-Zamora, K., Gamboa, D. A. P., & Faytong-Haro, M. (2024). Unveiling the core constructs: A statistical approach to evaluating user experience with chatbots in higher education (A case study from a university in Ecuador). Journal of Infrastructure Policy and Development 2024, 8(10), 1-14.
https://doi.org/10.24294/jipd.v8i10.6381
Shams, G., Kim, K. K., & Kim, K. (2024). Enhancing service recovery satisfaction with chatbots: The role of humor and informal language. International Journal of Hospitality Management, 120, 103782. https://doi.org/10.1016/j.ijhm.2024.103782
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
Siriwardhana, S., Weerasekera, R., Wen, E., & Nanayakkara, S. (2021). Fine-tune the entire RAG architecture (including DPR retriever) for question-answering. arXiv. http://arxiv.org/abs/2106.11517
Sivaraman, V., Wu, Y., & Perer, A. (2022). Emblaze: Illuminating machine learning representations through interactive comparison of embedding spaces. The 27th International Conference on Intelligent User Interfaces, pp. 418-432. https://doi.org/10.1145/3490099.3511137
Su , J., & Yang , W. (2023). Unlocking the power of chatgpt: A framework for applying generative ai in education. ECNU Review of Education, 6(3), 355-366. https://doi.org/10.1177/20965311231168423
Sugianto, S., Abidin, Z., Putra, A., & Budiman, K. (2021). Knowledge management system in a higher education institution: Development of an expertise search system. Journal of Physics: Conference Series, 1918, 1-5. https://doi.org/10.1088/1742-6596/1918/4/042021
Suryanto, T. L. M., Wibawa, A. P., Hariyono, H., & Nafalski, A. (2023). Evolving conversations: A review of chatbots and implications in natural language processing for cultural heritage ecosystems. IJRCS: International Journal of Robotics and Control Systems, 3(4), 955-1006. https://doi.org/10.31763/ijrcs.v3i4.1195
TonbuloĞLu, B. (2023). An evaluation of the use of artificial intelligence applications in online education. Journal of Educational Technology and Online Learning, 6(4), 866-884. https://doi.org/10.31681/jetol.1335906
Tu, S., Zhang, Z., Yu, J., Li, C., Zhang, S., Yao, Z., Hou, L., & Li, J. (2023). LittleMu: Deploying an online virtual teaching assistant via heterogeneous sources integration and chain of teach prompts. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 4843-4849.
https://doi.org/10.1145/3583780.3615484
Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(33), 1-20. https://doi.org/10.1186/s41239-021-00269-8
Walker, N. T., Ultes, S., & Lison, P. (2023). Retrieval-augmented neural response generation using logical reasoning and relevance scoring. arXiv. http://arxiv.org/abs/2310.13566
Wang, K., Ramos, J., & Lawrence, R. (2023). ChatEd: A chatbot leveraging chatgpt for an enhanced learning experience in higher education. arXiv. http://arxiv.org/abs/2401.00052
Zhang, L., & Hu, Y. (2021). A fine-tuning approach research of pre-trained model with two stage. 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), pp. 905-908. https://doi.org/10.1109/ICPECA51329.2021.9362566
Zhang, X., Sun, M., Liu, J., & Li, X. (2021). Optimal embedding calibration for symbolic music similarity. arXiv. http://arxiv.org/abs/2103.07656
Zhang, Z., Fang, M., & Chen, L. (2024). RetrievalQA: Assessing adaptive retrieval-augmented generation for short-form open-domain question answering. arXiv. http://arxiv.org/abs/2402.16457
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