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EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman

Year 2025, Volume: 37 Issue: 2, 196 - 205

Ayça Topallı

https://doi.org/10.7240/jeps.1553974

Abstract

Bu çalışmada üniversite öğrencilerine sanal danışman olarak yardımcı olabilecek, Türkçe destekli bir akıllı yazılım, EkoBot sunulmuştur. Bu yazılımla öğrencilerin sorularına doğru ve hızlı bir şekilde yanıt almaları hedeflenmiştir. Bunun için yapay zekâ destekli büyük dil modellerinden yararlanılmıştır. Büyük dil modelinin, eğitimi sırasında kullanılmamış, üniversite yönetmeliklerine dayalı yanıtlar verebilmesi için bu belgeler modele dışardan verilmiş ve “almayla artırılmış üretim” yöntemi kullanılmıştır. Önerilen sistemin performansını ölçmek için ucu açık ya da olumlu ve olumsuz yanıtlara sahip 100 adet soru üretilmiştir. Alma kısmında, soruya en çok benzeyen beş bağlam metni ile %100 başarım elde edilmiştir. Üretme kısmında, yanıt ile bağlam benzerlikleri 0,82 olarak bulunmuştur. Ayrıca önerilen çözümün bir Web sayfası olarak çalışan bir prototipi hazırlanmış ve öğrencilerin kullanımına sunulmuştur.

Keywords

Doğal dil işleme kosinüs benzerliği istem mühendisliği büyük dil modelleri yapay zekâ almayla artırılmış üretim

Thanks

Bu çalışma, OpenAI API Researcher Access Program tarafından API kredisi ile desteklenmiştir.

References

  • Ayanouz, S., Abdelhakim, B. A., & Benhmed, M. (2020). A Smart Chatbot Architecture based NLP and Machine Learning for Health Care Assistance. Proceedings of the 3rd International Conference on Networking, Information Systems & Security, 1–6. https://doi.org/10.1145/3386723.3387897
  • Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS ’20), 9459–9474.
  • Lee, K., Chang, M.-W., & Toutanova, K. (2019). Latent Retrieval for Weakly Supervised Open Domain Question Answering. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 6086–6096. https://doi.org/10.18653/v1/P19-1612
  • Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). Retrieval-Augmented Language Model Pre-Training. Proceedings of Machine Learning Research, 3929–3938.
  • Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., & Zaharia, M. (2022). Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.
  • Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023). In-Context Retrieval-Augmented Language Models.
  • Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W. (2023). REPLUG: Retrieval-Augmented Black-Box Language Models.
  • İEÜ Ön Lisans ve Lisans Eğitim-Öğretim ve Sınav Yönetmeliği. (2024). https://www.ieu.edu.tr/tr/bylaws/type/read/id/72.
  • Ranoliya, B. R., Raghuwanshi, N., & Singh, S. (2017). Chatbot for university related FAQs. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1525–1530. https://doi.org/10.1109/ICACCI.2017.8126057
  • Krassmann, A. L., Flach, J. M., Grando, A. R. C. da S., Tarouco, L. M. R., & Bercht, M. (2019). A Process for Extracting Knowledge Base for Chatbots from Text Corpora. 2019 IEEE Global Engineering Education Conference (EDUCON), 322–329. https://doi.org/10.1109/EDUCON.2019.8725064
  • Nwankwo, W. (2018). Interactive Advising with Bots: Improving Academic Excellence in Educational Establishments. American Journal of Operations Management and Information Systems, 3(1), 6. https://doi.org/10.11648/j.ajomis.20180301.12
  • Chandra, Y. W., & Suyanto, S. (2019). Indonesian Chatbot of University Admission Using a Question Answering System Based on Sequence-to-Sequence Model. Procedia Computer Science, 157, 367–374. https://doi.org/10.1016/j.procs.2019.08.179
  • Tommy, L., Kirana, C., & Riska, L. (2020). The Combination of Natural Language Processing and Entity Extraction for Academic Chatbot. 2020 8th International Conference on Cyber and IT Service Management (CITSM), 1–6. https://doi.org/10.1109/CITSM50537.2020.9268851
  • Guvindan Raju, K. R., Adams, C., & Srinivas, R. (2018). Cognitive Virtual Admissions Counselor. SMU Data Science Review, 1(1), 1–13.
  • Gbenga, O., Okedigba, T., & Oluwatobi, H. (2020). An Improved Rapid Response Model for University Admission Enquiry System Using Chatbot. International Journal of Computer (IJC), 38(1), 123–131.
  • Priadko, A. O., Osadcha, K. P., Kruhlyk. Vladyslav S., & Rakovych, V. A. (2019). Development of a chatbot for informing students of the schedule. Proceedings of the 2nd Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2019), 128–137.
  • Shivam, K., Saud, K., Sharma, M., Vashishth, S., & Patil, S. (2018). Chatbot for College Website. International Journal of Computing and Technology, 5(6), 74–77.
  • Mendoza, S., Hernández-León, M., Sánchez-Adame, L. M., Rodríguez, J., Decouchant, D., & Meneses-Viveros, A. (2020). Supporting Student-Teacher Interaction Through a Chatbot. In Learning and Collaboration Technologies. Human and Technology Ecosystems (pp. 93–107). Springer International Publishing. https://doi.org/10.1007/978-3-030-50506-6_8
  • Nguyen, T. T., Le, A. D., Hoang, H. T., & Nguyen, T. (2021). NEU-chatbot: Chatbot for admission of National Economics University. Computers and Education: Artificial Intelligence, 2, 100036. https://doi.org/10.1016/j.caeai.2021.100036
  • Ula, M., Hardi, R., & Hipiny, I. (2023). An Improved Structure for Academic Information Services through AI Chatbots. Journal of Engineering Science and Technology Review, 16(5), 164–173. https://doi.org/10.25103/jestr.165.20
  • Verma, A., Kuntala, C., Khatri, P., . S., Kaur, S., Mohapatra, A. K., & Singhal, S. (2022). University Chatbot System Using NLP. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4255753
  • Oliveira, P. F., & Matos, P. (2023). Introducing a Chatbot to the Web Portal of a Higher Education Institution to Enhance Student Interaction. ASEC 2023, 128. https://doi.org/10.3390/ASEC2023-16621
  • Maryamah, M., Irfani, M. M., Tri Raharjo, E. B., Rahmi, N. A., Ghani, M., & Raharjana, I. K. (2024). Chatbots in Academia: A Retrieval-Augmented Generation Approach for Improved Efficient Information Access. 2024 16th International Conference on Knowledge and Smart Technology (KST), 259–264. https://doi.org/10.1109/KST61284.2024.10499652
  • Neupane, S., Hossain, E., Keith, J., Tripathi, H., Ghiasi, F., Golilarz, N. A., Amirlatifi, A., Mittal, S., & Rahimi, S. (2024). From Questions to Insightful Answers: Building an Informed Chatbot for University Resources. ArXiv.
  • Nguyen, H. D., Tran, T.-V., Pham, X.-T., Huynh, A. T., & Do, N. V. (2021). Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot. Sensors and Materials, 33(9), 3101. https://doi.org/10.18494/SAM.2021.3264
  • Ait Baha, T., 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
  • Aloqayli, A., & Abdelhafez, H. (2023). Intelligent Chatbot for Admission in Higher Education. International Journal of Information and Education Technology, 13(9), 1348–1357. https://doi.org/10.18178/ijiet.2023.13.9.1937
  • Alharethi, T. M. (2023). Autoresponder using Chatbot for Educational Services. 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), 1–5. https://doi.org/10.1109/ICAISC56366.2023.10084956
  • Lee, Y.-F., Hwang, G.-J., & Chen, P.-Y. (2022). Impacts of an AI-based chabot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational Technology Research and Development, 70(5), 1843–1865. https://doi.org/10.1007/s11423-022-10142-8
  • Martinez-Araneda, C., Gutiérrez, M., Maldonado, D., Gómez, P., Segura, A., & Vidal-Castro, C. (2024). Designing a chatbot to support problem-solving in a programming course. 966–975. https://doi.org/10.21125/inted.2024.0317
  • Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2023). RAGAS: Automated Evaluation of Retrieval Augmented Generation. ArXiv.
  • Lin, C.-Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. Text Summarization Branches Out, 74–81.
  • Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001). BLEU. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL ’02, 311. https://doi.org/10.3115/1073083.1073135
  • Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020, April 26). BERTScore: Evaluating Text Generation with BERT. 8th International Conference on Learning Representations.

EkoBot: Intelligent Virtual Academic Advisor with Turkish Support

Year 2025, Volume: 37 Issue: 2, 196 - 205

Ayça Topallı

https://doi.org/10.7240/jeps.1553974

Abstract

In this study, EkoBot, an AI-powered software with Turkish language support, designed to assist university students as a virtual advisor, is presented. The goal of this software is to provide students with accurate and prompt answers to their questions. For this purpose, large language models supported by artificial intelligence were utilized. To enable the large language model to give responses based on university regulations, which were not included in its training, these documents were provided to the model externally, and a “retrieval-augmented generation” method was employed. To evaluate the performance of the proposed system, 100 questions with open-ended or positive and negative responses were generated. In the retrieval phase, 100% success was achieved with the five context texts most similar to the question. In the generation phase, the similarity between the responses and the context was found to be 0.82. Additionally, a prototype of the proposed solution was developed as a Web page and made available for the students.

Keywords

Natural language processing cosine similarity prompt engineering large language models artificial intelligence retrieval augmented generation

References

  • Ayanouz, S., Abdelhakim, B. A., & Benhmed, M. (2020). A Smart Chatbot Architecture based NLP and Machine Learning for Health Care Assistance. Proceedings of the 3rd International Conference on Networking, Information Systems & Security, 1–6. https://doi.org/10.1145/3386723.3387897
  • Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS ’20), 9459–9474.
  • Lee, K., Chang, M.-W., & Toutanova, K. (2019). Latent Retrieval for Weakly Supervised Open Domain Question Answering. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 6086–6096. https://doi.org/10.18653/v1/P19-1612
  • Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). Retrieval-Augmented Language Model Pre-Training. Proceedings of Machine Learning Research, 3929–3938.
  • Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., & Zaharia, M. (2022). Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.
  • Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023). In-Context Retrieval-Augmented Language Models.
  • Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W. (2023). REPLUG: Retrieval-Augmented Black-Box Language Models.
  • İEÜ Ön Lisans ve Lisans Eğitim-Öğretim ve Sınav Yönetmeliği. (2024). https://www.ieu.edu.tr/tr/bylaws/type/read/id/72.
  • Ranoliya, B. R., Raghuwanshi, N., & Singh, S. (2017). Chatbot for university related FAQs. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1525–1530. https://doi.org/10.1109/ICACCI.2017.8126057
  • Krassmann, A. L., Flach, J. M., Grando, A. R. C. da S., Tarouco, L. M. R., & Bercht, M. (2019). A Process for Extracting Knowledge Base for Chatbots from Text Corpora. 2019 IEEE Global Engineering Education Conference (EDUCON), 322–329. https://doi.org/10.1109/EDUCON.2019.8725064
  • Nwankwo, W. (2018). Interactive Advising with Bots: Improving Academic Excellence in Educational Establishments. American Journal of Operations Management and Information Systems, 3(1), 6. https://doi.org/10.11648/j.ajomis.20180301.12
  • Chandra, Y. W., & Suyanto, S. (2019). Indonesian Chatbot of University Admission Using a Question Answering System Based on Sequence-to-Sequence Model. Procedia Computer Science, 157, 367–374. https://doi.org/10.1016/j.procs.2019.08.179
  • Tommy, L., Kirana, C., & Riska, L. (2020). The Combination of Natural Language Processing and Entity Extraction for Academic Chatbot. 2020 8th International Conference on Cyber and IT Service Management (CITSM), 1–6. https://doi.org/10.1109/CITSM50537.2020.9268851
  • Guvindan Raju, K. R., Adams, C., & Srinivas, R. (2018). Cognitive Virtual Admissions Counselor. SMU Data Science Review, 1(1), 1–13.
  • Gbenga, O., Okedigba, T., & Oluwatobi, H. (2020). An Improved Rapid Response Model for University Admission Enquiry System Using Chatbot. International Journal of Computer (IJC), 38(1), 123–131.
  • Priadko, A. O., Osadcha, K. P., Kruhlyk. Vladyslav S., & Rakovych, V. A. (2019). Development of a chatbot for informing students of the schedule. Proceedings of the 2nd Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2019), 128–137.
  • Shivam, K., Saud, K., Sharma, M., Vashishth, S., & Patil, S. (2018). Chatbot for College Website. International Journal of Computing and Technology, 5(6), 74–77.
  • Mendoza, S., Hernández-León, M., Sánchez-Adame, L. M., Rodríguez, J., Decouchant, D., & Meneses-Viveros, A. (2020). Supporting Student-Teacher Interaction Through a Chatbot. In Learning and Collaboration Technologies. Human and Technology Ecosystems (pp. 93–107). Springer International Publishing. https://doi.org/10.1007/978-3-030-50506-6_8
  • Nguyen, T. T., Le, A. D., Hoang, H. T., & Nguyen, T. (2021). NEU-chatbot: Chatbot for admission of National Economics University. Computers and Education: Artificial Intelligence, 2, 100036. https://doi.org/10.1016/j.caeai.2021.100036
  • Ula, M., Hardi, R., & Hipiny, I. (2023). An Improved Structure for Academic Information Services through AI Chatbots. Journal of Engineering Science and Technology Review, 16(5), 164–173. https://doi.org/10.25103/jestr.165.20
  • Verma, A., Kuntala, C., Khatri, P., . S., Kaur, S., Mohapatra, A. K., & Singhal, S. (2022). University Chatbot System Using NLP. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4255753
  • Oliveira, P. F., & Matos, P. (2023). Introducing a Chatbot to the Web Portal of a Higher Education Institution to Enhance Student Interaction. ASEC 2023, 128. https://doi.org/10.3390/ASEC2023-16621
  • Maryamah, M., Irfani, M. M., Tri Raharjo, E. B., Rahmi, N. A., Ghani, M., & Raharjana, I. K. (2024). Chatbots in Academia: A Retrieval-Augmented Generation Approach for Improved Efficient Information Access. 2024 16th International Conference on Knowledge and Smart Technology (KST), 259–264. https://doi.org/10.1109/KST61284.2024.10499652
  • Neupane, S., Hossain, E., Keith, J., Tripathi, H., Ghiasi, F., Golilarz, N. A., Amirlatifi, A., Mittal, S., & Rahimi, S. (2024). From Questions to Insightful Answers: Building an Informed Chatbot for University Resources. ArXiv.
  • Nguyen, H. D., Tran, T.-V., Pham, X.-T., Huynh, A. T., & Do, N. V. (2021). Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot. Sensors and Materials, 33(9), 3101. https://doi.org/10.18494/SAM.2021.3264
  • Ait Baha, T., 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
  • Aloqayli, A., & Abdelhafez, H. (2023). Intelligent Chatbot for Admission in Higher Education. International Journal of Information and Education Technology, 13(9), 1348–1357. https://doi.org/10.18178/ijiet.2023.13.9.1937
  • Alharethi, T. M. (2023). Autoresponder using Chatbot for Educational Services. 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), 1–5. https://doi.org/10.1109/ICAISC56366.2023.10084956
  • Lee, Y.-F., Hwang, G.-J., & Chen, P.-Y. (2022). Impacts of an AI-based chabot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational Technology Research and Development, 70(5), 1843–1865. https://doi.org/10.1007/s11423-022-10142-8
  • Martinez-Araneda, C., Gutiérrez, M., Maldonado, D., Gómez, P., Segura, A., & Vidal-Castro, C. (2024). Designing a chatbot to support problem-solving in a programming course. 966–975. https://doi.org/10.21125/inted.2024.0317
  • Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2023). RAGAS: Automated Evaluation of Retrieval Augmented Generation. ArXiv.
  • Lin, C.-Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. Text Summarization Branches Out, 74–81.
  • Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001). BLEU. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL ’02, 311. https://doi.org/10.3115/1073083.1073135
  • Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020, April 26). BERTScore: Evaluating Text Generation with BERT. 8th International Conference on Learning Representations.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Natural Language Processing
Journal Section Research Articles
Authors

Ayça Topallı 0000-0001-7712-5790

Early Pub Date June 16, 2025
Publication Date
Submission Date September 21, 2024
Acceptance Date May 21, 2025
Published in Issue Year 2025 Volume: 37 Issue: 2

Cite

APA Topallı, A. (2025). EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman. International Journal of Advances in Engineering and Pure Sciences, 37(2), 196-205. https://doi.org/10.7240/jeps.1553974
AMA Topallı A. EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman. JEPS. June 2025;37(2):196-205. doi:10.7240/jeps.1553974
Chicago Topallı, Ayça. “EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman”. International Journal of Advances in Engineering and Pure Sciences 37, no. 2 (June 2025): 196-205. https://doi.org/10.7240/jeps.1553974.
EndNote Topallı A (June 1, 2025) EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman. International Journal of Advances in Engineering and Pure Sciences 37 2 196–205.
IEEE A. Topallı, “EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman”, JEPS, vol. 37, no. 2, pp. 196–205, 2025, doi: 10.7240/jeps.1553974.
ISNAD Topallı, Ayça. “EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman”. International Journal of Advances in Engineering and Pure Sciences 37/2 (June 2025), 196-205. https://doi.org/10.7240/jeps.1553974.
JAMA Topallı A. EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman. JEPS. 2025;37:196–205.
MLA Topallı, Ayça. “EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman”. International Journal of Advances in Engineering and Pure Sciences, vol. 37, no. 2, 2025, pp. 196-05, doi:10.7240/jeps.1553974.
Vancouver Topallı A. EkoBot: Türkçe Destekli Akıllı Sanal Akademik Danışman. JEPS. 2025;37(2):196-205.
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