BERT Enabled AI Chatbot for Enhanced Voice Interaction

Abstract

Introduction

In the digital era, educational institutions face challenges in efficiently serving students, faculty, and other stakeholders. AI-powered chatbots have emerged as essential tools to provide personalized assistance, streamline communication, and support the growing demand for responsive student services, especially with increased online learning.

Chatbots utilize Artificial Intelligence (AI) and Natural Language Processing (NLP) to understand and respond to user queries. They are mainly categorized into three types:

• Rule-based (simple, predefined responses),

• Retrieval-based (select responses from a set of options based on context), and

• Generative-based (create dynamic responses using advanced models like transformers).

Research on chatbots for technical universities highlights the use of advanced NLP models such as BERT and GPT, showing their superior performance in understanding and generating accurate, context-aware responses. Various methodologies include machine learning and lexicon-based techniques, with evaluations based on accuracy, user satisfaction, and real-time testing.

Literature comparisons reveal that transformer-based models and neural networks outperform traditional approaches, with GPT-4 noted for excelling in educational contexts like calculus and statistics. Chatbots also vary in effectiveness depending on their design and application domain.

Key trends include the adoption of transformer models, reinforcement learning for continuous improvement, and a focus on ethical, fair, and transparent chatbot interactions. However, challenges remain, such as inconsistent university information, slow response times, data security, and limited voice interaction support.

Implementation typically involves a pipeline of input collection (text/voice), data preprocessing, feature extraction (e.g., BERT embeddings), model training and fine-tuning, query validation, and response generation (static or dynamic). Text-to-Speech (TTS) technology enhances accessibility by converting chatbot replies to audio.

Conclusion

In conclusion, the exploration of advanced NLP models for technical university information chatbots reveals a promising pathway toward enhancing student engagement and decision-making processes. The comparative analysis of various NLP architectures, such as BERT and GPT, underscores the strengths and limitations of each model in understanding and generating contextually relevant responses. While transformer-based models demonstrate significant improvements in accuracy and user experience, the integration of user feedback and engagement metrics is crucial for developing effective chatbot solutions. Additionally, addressing challenges such as data security and the need for voice interaction capabilities remains essential for creating more inclusive and user-friendly systems. The findings of this survey highlight the necessity for a holistic approach that combines technical performance with ethical considerations, ensuring fairness and transparency in chatbot interactions. As universities continue to face challenges related to inconsistent information and delayed responses, the implementation of advanced NLP-driven chatbots could bridge these gaps, providing students with reliable support and information. Future research should focus on refining these models and incorporating adaptive learning mechanisms to enhance their responsiveness over time. Ultimately, the advancement of NLP technologies in educational chatbots holds the potential to significantly improve the overall student experience, fostering informed decision-making and engagement in the academic journey.

References

[1] Girijaattigeri , (member, ieee), ankitagrawal, and sucheta v. Kolekar , (member, ieee) “advanced nlp models for technical universityinformationchatbots:development and comparative analysis” ,department of information and communication technology, digital object identifier 10.1109/access.2024.3368382,volume12,2024,https://creativecommons.org/licenses/bync-nd/4.0/ [2] Sumitpandey, srishtisharma “a comparative study of retrieval-based and generative-based chatbots using deep learning and machine learning”.the north cap university school of engineering & technology, gurugram, haryana, 122017,india ,https://doi.org/10.1016/j.health.2023.100198 [3] Davidsantandreucalonge, lindasmail, firuzkamalov,”enough of the chitchat: a comparative analysis of four ai chatbots for calculus and statistics”, vol.6 no.2(2023), issn : 2591-801x http://journals.sfu.ca/jalt/index.php/jalt/index [4] Nachiketkapure “comparative analysis of various chatbot frameworks”, department of computer science & engineering, birla college of arts, science & commerce, kalyan, india.volume:04/issue:09/september-2022, e-issn: 2582-5208, www.irjmets.com [5] Karthikkonar. Mca, nmimsmukeshpatel“a comparative study on chatbot based on machine learning and lexicon basedtechnique”,school of technology management & engineering, vileparle(west) mumbai. Volume 5, issue 5, may – 2020,issnno:-2456-2165. Www.ijisrt.com [6] Oleksiichalyi, “an evaluation of generalpurpose ai chatbots:acomprehensive comparative analysis”,faculty of informatics, vytautas magnus university, kaunas, 44404, lithuani, https://www.isjtrend.com/ [7] Shivangverma, lakshaysahni, moolchandsharma, ”comparative analysis of chatbots”, department of computer science and engineering, maharaja agrasen institute of technology, ggsipu, delhi, india, department of electrical and electronics engineering, delhi technological university,delhi,india(icicc2020), https://ssrn.com/abstract=3563674 [8] Jitendrachaudhary,vaibhavjoshi,atharvkhare,rahulgawali,asmita manna, “a comparative study of medical chatbots”, volume: 08 issue: 02 | feb2021 , e-issn: 2395-0056, www.irjet.net [9] A. Chandan, m. Chattopadhyay, and s. Sahoo, ‘‘implementing chat-bot in educational institutes,’’ ijrar j., vol. 6, no. 2, pp. 44–47, 2019. [10] T.lalwani,s.bhalotia, a.pal, v.rathod, and s.bisen, ‘‘implementation of a chatbot system using ai and nlp,’’ int. J. Innov. Res. Comput. Sci. Technol. (ijircst), vol. 6, no. 3, pp. 26–30, 2018. [11] J. Thukrul, A. Srivastava, and G. Thakkar, ‘‘Doctorbot—An informative and interactive chatbot for COVID-19,’’ Int. Res. J. Eng. Technol. (IRJET), vol. 7, no. 7, pp. 3033–3036, 2020. [12] S. Maher, ‘‘Chatbots & its techniques using AI: A review,’’ Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 12, pp. 503–508, Dec. 2020. [13] M. Aleedy, H. Shaiba, and M. Bezbradica, ‘‘Generating and analyzing chatbot responses using natural language processing,’’ Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 9, 2019. [14] P. Qi, Y. Zhang, Y. Zhang, J. Bolton, and C. D. Manning, ‘‘Stanza: A Python naturallanguageprocessingtoolkit for many human languages,’’ 2020, arXiv:2003.07082. [15] M. M. H. Dihyat and J. Hough, ‘‘Can rule-based chatbots outperform neural models without pre-training in small data situations? A preliminary comparison of AIML and Seq2Seq,’’ in Proc. 25th Workshop Semantics Pragmatics Dialogue, 2021, pp. 22–26.

Copyright

Copyright © 2025 Mrs. Elakia K, Dr. R. G. Suresh Kumar, Ms. Akshara Pavithran, Ms. Sreenandana T M , Ms. Vismaya A K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.