Research Article
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Year 2024, Volume: 5 Issue: 2, 56 - 62, 30.12.2024
https://doi.org/10.46572/naturengs.1587879

Abstract

References

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  • Gunawan, J. (2023). Exploring the future of nursing: Insights from the ChatGPT model. Belitung Nursing Journal, 9(1), 1.
  • Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 2940.
  • King, M. R. (2023). The future of AI in medicine: a perspective from a Chatbot. Annals of Biomedical Engineering, 51(2), 291-295.
  • Li, Y., Cao, J., Xu, Y., Zhu, L., & Dong, Z. Y. (2024). Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance. Renewable and Sustainable Energy Reviews, 189, 113913.
  • Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
  • Ardic, O., Ozturk, M. U., Demirtas, I., & Arslan, S. (2024, May). Information Extraction from Sustainability Reports in Turkish through RAG Approach. In 2024 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
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  • Fukizi, K. Y. (2023, August). Collaborative Decision-Making Assistant for Healthcare Professionals: A Human-Centered AI Prototype Powered by Azure Open AI. In Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (pp. 118-119).

AI-Powered GPT-Based University-Specific Chat Assistant: UniROBO

Year 2024, Volume: 5 Issue: 2, 56 - 62, 30.12.2024
https://doi.org/10.46572/naturengs.1587879

Abstract

AI-powered chat applications are innovative solutions that facilitate user interaction and information access. These applications improve user experience by providing personalized and context-sensitive responses thanks to large language models and natural language processing techniques. This study examined the design and development process of UniRobo, an AI-powered chat application developed for Malatya Turgut Ozal University students and staff. UniRobo is an application that provides instant information on topics such as education, food menus, and campus events and offers personalized responses using large language models and natural language processing techniques. In the development process, based on user needs analysis, the mobile application was created with React Native, the back-end was created with Python and FastAPI, and the MongoDB database was integrated. Artificial intelligence capabilities were supported by OpenAI API and fine-tuning, thus adapting to university-specific content. Retrieval-Augmented Generation (RAG) architecture and Azure AI Search technology increased user satisfaction by providing more accurate and faster responses. As a result, UniRobo has made university life more accessible, providing users with access to fast and accurate information, and has demonstrated the potential of artificial intelligence-based solutions in the education sector.

References

  • Arcas, B. A. (2022). Do large language models understand us?. Daedalus, 151(2), 183-197.
  • Zhang, J., Krishna, R., Awadallah, A. H., & Wang, C. (2023). Ecoassistant: Using llm assistant more affordably and accurately. arXiv preprint arXiv:2310.03046.
  • Wu, C., Lin, Z., Fang, W., & Huang, Y. (2023, October). A medical diagnostic assistant based on llm. In China Health Information Processing Conference (pp. 135-147). Singapore: Springer Nature Singapore.
  • Guan, Y., Wang, D., Chu, Z., Wang, S., Ni, F., Song, R., & Zhuang, C. (2024, August). Intelligent agents with llm-based process automation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5018-5027).
  • Luo, B., Lau, R. Y., Li, C., & Si, Y. W. (2022). A critical review of state‐of‐the‐art chatbot designs and applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(1), e1434.
  • Wei, R., Li, K., & Lan, J. (2024, March). Improving Collaborative Learning Performance Based on LLM Virtual Assistant. In 2024 13th International Conference on Educational and Information Technology (ICEIT) (pp. 1-6). IEEE.
  • Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In IFIP international conference on artificial intelligence applications and innovations (pp. 373-383). Springer, Cham.
  • Kim, J. K., Chua, M., Rickard, M., & Lorenzo, A. (2023). ChatGPT and large language model (LLM) chatbots: The current state of acceptability and a proposal for guidelines on utilization in academic medicine. Journal of Pediatric Urology, 19(5), 598-604.
  • Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing.
  • Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66.
  • Wang, Y., Sun, Y., Fu, Y., Zhu, D., & Tian, Z. (2024). Spectrum-BERT: pre-training of deep bidirectional transformers for spectral classification of Chinese liquors. IEEE Transactions on Instrumentation and Measurement.
  • Çöltekin, Ç., Doğruöz, A. S., & Çetinoğlu, Ö. (2023). Resources for Turkish natural language processing: A critical survey. Language Resources and Evaluation, 57(1), 449-488.
  • Schweter, S. (2020). BERTurk-BERT models for Turkish. Zenodo, 2020, 3770924.
  • Koçak, S., İç, Y. T., Sert, M., & Dengiz, B. (2023). Ar-Ge projelerinin sınıflandırılması için doğal Türkçe dil işleme tabanlı yöntem. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(3), 1375-1388.
  • Cao, L. (2023). Diaggpt: An llm-based chatbot with automatic topic management for task-oriented dialogue. arXiv preprint arXiv:2308.08043.
  • Gunawan, J. (2023). Exploring the future of nursing: Insights from the ChatGPT model. Belitung Nursing Journal, 9(1), 1.
  • Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 2940.
  • King, M. R. (2023). The future of AI in medicine: a perspective from a Chatbot. Annals of Biomedical Engineering, 51(2), 291-295.
  • Li, Y., Cao, J., Xu, Y., Zhu, L., & Dong, Z. Y. (2024). Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance. Renewable and Sustainable Energy Reviews, 189, 113913.
  • Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
  • Ardic, O., Ozturk, M. U., Demirtas, I., & Arslan, S. (2024, May). Information Extraction from Sustainability Reports in Turkish through RAG Approach. In 2024 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • Wiratunga, N., Abeyratne, R., Jayawardena, L., Martin, K., Massie, S., Nkisi-Orji, I., ... & Fleisch, B. (2024, June). CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. In International Conference on Case-Based Reasoning (pp. 445-460). Cham: Springer Nature Switzerland.
  • Fukizi, K. Y. (2023, August). Collaborative Decision-Making Assistant for Healthcare Professionals: A Human-Centered AI Prototype Powered by Azure Open AI. In Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (pp. 118-119).
There are 23 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Serdar Budak 0009-0009-8636-369X

Serpil Aslan 0000-0001-8009-063X

Publication Date December 30, 2024
Submission Date November 19, 2024
Acceptance Date December 24, 2024
Published in Issue Year 2024 Volume: 5 Issue: 2

Cite

APA Budak, S., & Aslan, S. (2024). AI-Powered GPT-Based University-Specific Chat Assistant: UniROBO. NATURENGS, 5(2), 56-62. https://doi.org/10.46572/naturengs.1587879