Publications

@inproceedings{anvitha_edubot_2025,

title = {EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval},

author = {K. Anvitha and T. Durjay and K. Sathvika and G. Gnanendra and S. Annamalai and S. K. Natarajan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013615976&doi=10.1109%2FGINOTECH63460.2025.11077097&partnerID=40&md5=b08377283f2ea2ee406d38d1d23f1e42},

doi = {10.1109/GINOTECH63460.2025.11077097},

isbn = {9798331507756 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bao_musegesture_2024,

title = {MuseGesture: A Framework for Gesture Synthesis by Virtual Agents in VR Museum Guides},

author = {Y. Bao and N. Gao and D. Weng and J. Chen and Z. Tian},

editor = {U. Eck and M. Sra and J. Stefanucci and M. Sugimoto and M. Tatzgern and I. Williams},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214385900&doi=10.1109%2FISMAR-Adjunct64951.2024.00079&partnerID=40&md5=16a1b740663a051f3611cb201211620a},

doi = {10.1109/ISMAR-Adjunct64951.2024.00079},

isbn = {9798331506919 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},

pages = {337–338},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper presents an innovative framework named MuseGesture, designed to generate contextually adaptive gestures for virtual agents in Virtual Reality (VR) museums. The framework leverages the robust language understanding and generation capabilities of Large Language Models (LLMs) to parse tour narration texts and generate corresponding explanatory gestures. Through reinforcement learning and adversarial skill embeddings, the framework also generates guiding gestures tailored to the virtual museum environment, integrating both gesture types using conditional motion interpolation methods. Experimental results and user studies demonstrate that this approach effectively enables voice-command-controlled virtual guide gestures, offering a novel intelligent guiding system solution that enhances the interactive experience in VR museum environments. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Adversarial machine learning, Embeddings, Gesture Generation, Intelligent Agents, Intelligent systems, Intelligent virtual agents, Language generation, Language Model, Large language model, large language models, Museum guide, Reinforcement Learning, Reinforcement learnings, Robust language understanding, Virtual agent, Virtual Agents, Virtual environments, Virtual reality museum guide, VR Museum Guides},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{fuchs_immersive_2023,

title = {Immersive Spaces for Creativity: Smart Working Environments},

author = {A. Fuchs and S. Appel and P. Grimm},

editor = {A. A. Yunanto and A. D. Ramadhani and Y. R. Prayogi and P. A. M. Putra and M. Ruswiansari and M. Ridwan and F. Gamar and W. M. Rahmawati and M. R. Rusli and F. M. Humaira and A. F. Adila},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173627291&doi=10.1109%2FIES59143.2023.10242458&partnerID=40&md5=897696f2c0255ecc4fb2f859581c7619},

doi = {10.1109/IES59143.2023.10242458},

isbn = {9798350314731 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IES - Int. Electron. Symp.: Unlocking Potential Immersive Technol. Live Better Life, Proceeding},

pages = {610–617},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper presents an innovative approach to designing an immersive space that dynamically supports users (inter-)action based on users' behavior, voice, and mood, providing a personalized experience. The objective of this research is to explore how a space can communicate with users in a seamless, engaging, and interactive environment. Therefore, it integrates natural language processing (NLP), generative artificial intelligence applications and human computer interaction that utilizes a combination of sensors, microphones, and cameras to collect real-time data on users' behavior, voice, and mood. This data is then processed and analyzed by an intelligent system that employs machine learning algorithms to identify patterns and adapt the environment accordingly. The adaptive features include changes in lighting, sound, and visual elements to facilitate creativity, focus, relaxation, or socialization, depending on the user's topics and emotional state. The paper discusses the technical aspects of implementing such a system. Additionally, it highlights the potential applications of this technology in various domains such as education, entertainment, and workplace settings. In conclusion, the immersive creative space represents a paradigm shift in human-environment interaction, offering a dynamic and personalized space that caters to the diverse needs of users. The research findings suggest that this innovative approach holds great promise for enhancing user experiences, fostering creativity, and promoting overall well-being. © 2023 Elsevier B.V., All rights reserved.},

keywords = {Artificial intelligence, Generative AI, Human computer interaction, Immersive, Innovative approaches, Intelligent systems, Interactive Environments, Language Model, Language processing, Large language model, large language models, Learning algorithms, machine learning, Natural language processing systems, Natural languages, User behaviors, User interfaces, Virtual Reality, Working environment},

pubstate = {published},

tppubtype = {inproceedings}

}