Jul 2026· International Scientific Journal of Engineering and Management· 0 citations
TL;DR
The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications.
Abstract
The rapid evolution of Large Language Models (LLMs) has opened new possibilities for building intelligent, human-like conversational systems capable of understanding and responding to open-domain queries in real time. Traditional chatbot architectures, whether rule-based or narrow-domain trained, are constrained by limited contextual understanding, poor conversational continuity, and an inability to generate fluent, human-like responses across varied topics. This paper presents the design, development, and evaluation of an AI-Powered Conversational Web Assistant that integrates Google's Gemini 1.5 Flash large language model with a lightweight Python Flask web application to deliver real-time, context-aware conversational support. The system accepts free-form natural-language input through a responsive browser-based chat interface, forwards the query to the Gemini API through secure RESTful calls, and renders the generated response asynchronously without page reload. A Flask backend manages session handling, prompt construction, and API communication, while a SQLite-backed logging layer stores conversation history for auditing and future personalization. The architecture is deliberately modular, separating the presentation layer, the dialogue-management logic, and the external LLM service, which allows any of these layers to be replaced or extended independently. Experimental use of the deployed prototype across a range of factual, technical, and conversational queries demonstrated that the assistant produces fluent, contextually relevant, and largely accurate responses while maintaining low perceived latency. The study also documents the practical challenges encountered during development, including prompt-engineering trade-offs, API rate limits, session-memory handling, and safe-response filtering, together with the mitigation strategies adopted. The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications, with a clear pathway toward voice interaction, sentiment-aware responses, multilingual support, and persistent contextual memory in future iterations.
Index Terms: - Artificial Intelligence • Natural Language Processing • Large Language Models • Google Gemini API • Conversational AI • Chatbot • Flask Framework • Prompt Engineering • Intent Recognition • Dialogue Management • Natural Language Generation • SQLite • Human-Computer Interaction • Web-Based Assistant.
In today’s data-driven environment, the ability to explore and manipulate datasets is essential, however, many existing data analysis tools require technical expertise in platforms such as Excel, Python, or Power BI, creating accessibility barriers for non-technical users. This project presents Quiksight, a web-based conversational data analysis system powered by Large Language Models (LLMs) that enables users to interact with datasets using natural language. The objective of this work was to develop an intuitive system that simplifies data analysis by allowing users to upload Excel or CSV files and perform operations such as filtering, sorting, summarization, column modification, missing-value handling, and data export without writing code. The system was implemented using Google Gemini as the LLM, FastAPI for backend processing, and HTML, JavaScript, and Tailwind CSS for the frontend interface.Evaluation showed that Quiksight successfully interpreted and executed a wide range of natural language data manipulation queries with high accuracy. Testing recorded an average query response time of approximately 2 seconds, while dataset uploads up to 30MB remained below 20 seconds under suitable network conditions. Compared with existing platforms such as Julius.ai and PowerDrill.ai, Quiksight demonstrated advantages through its lightweight architecture, simplified interaction model, and focus on everyday users. The project demonstrates the feasibility of LLM-powered conversational interfaces for democratizing data analysis. Future improvements include database integration, support for larger datasets, and advanced analytical capabilities such as statistical modelling.
Wumi Ajayi, Ihuanyachi O. OGBONNA, Daniel I. OGHINAN· Global Journal of Engineerin...· 0 citations
Large language model (LLM)-based conversational assistants are designed for general-purpose conversation tasks and are primarily optimized for high-resource languages. Although these systems support some low-resource languages (LRLs), their responses often fall short of user expectations. Consequently, speakers of LRLs remain marginalized and unable to fully benefit from advances in LLMs. These challenges underscore the need for targeted, language-specific solutions that can effectively serve underrepresented language communities. This study presents Labadain Chat, a conversational agent for Tetun, a low-resource language spoken by over 932,000 people in Timor-Leste. We adapt existing LLMs to Tetun using language-specific prompting strategies and report on the system's architecture, features, applications, and utility for the Tetun-speaking community. Results from the user study show a high task success rate for Labadain Chat (91%, with substantial inter-annotator agreement, Cohen's κ=0.67) and high user satisfaction (4.30 out of 5, with Cohen's weighted κ=0.75), demonstrating the effectiveness of language-specific LLM customization for Tetun. Overall, this study provides a practical pathway toward promoting equitable access to AI-powered information services for the Tetun-speaking community and suggests an adaptable methodology that can be applied to other under-resourced languages in similar contexts. The system is publicly available at https://www.labadain.com, with mobile applications for both iOS and Android.
Gabriel de Jesus, Sérgio Nunes· Annual International ACM SIG...· 0 citations
Large language models (LLMs) have demonstrated remarkable capabilities across different domains, yet two critical challenges limit their reliability: opaquely generated responses and role drift in extended interactions. Moreover, the possibility of a multi-layered dialogue, shaping LLM behavior both asynchronously across sessions and, optionally, in real time, remains largely unexplored. This paper presents XBot, a conversational agent designed to address these challenges. Built on the GPT-4o API, XBot requires no access to internal weights or activation space, making it portable across different LLM-based systems. It analyzes the user message and decomposes it into chunks, each annotated with topic and sentiment. It then selects a domain validated strategy from a curated expert-defined set, based on the assigned role, spanning multiple levels of granularity from general response types down to topic- and sentiment-specific guidance. Each response is therefore accompanied by explicit justifications accessible to non-technical users, supporting a continuous three-way dialogue among users, domain experts and XBot for ongoing validation and iterative refinement. Domain experts can inspect, modify and extend the strategy set at any time, ensuring alignment with professional standards and ethical requirements. Experimental comparisons with GPT-4o vanilla across three roles, evaluated through an ablation study and a multi-evaluator panel combining LLM-based and human judges, consistently rank XBot as the best performing system across all dimensions, demonstrating superior empathy, role stability and conversational depth, while GPT-4o vanilla exhibits pervasive persona drift across all experimental scenarios.
Luciano Caroprese, Ester Zumpano, M. Aracne et al.· Discover Artificial Intellig...· 0 citations
Data catalogs are essential tools for managing and discovering organizational data, but traditional navigation methods can be cumbersome, especially as data volumes grow. This paper explores the integration of generative AI-based conversational interfaces to enable intuitive and efficient navigation of unified data catalogs. By leveraging advanced natural language processing (NLP) models, users can interact with data catalogs through human-like dialogues, eliminating the need for complex query languages and manual search processes. We discuss the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries. Through a case study and usability testing, we demonstrate how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes. Finally, we address the challenges and future directions of deploying generative AI in data catalog systems, emphasizing the need for scalable and secure solutions.
Steven Young, Rebecca Green· International Journal of Dat...· 0 citations
This paper presents the development of an intelligent conversational assistant designed to support users in obtaining and clarifying information about university class schedules. The evolution of dialogue systems is considered, from early rule-based text dialogue programs based on pattern matching of input utterances to modern virtual assistants implemented using neural transformer architectures and large language models. The relevance of the study is determined by the use of large language models in combination with Retrieval-Augmented Generation (RAG), which makes it possible to generate responses based on information retrieved from external sources. An architecture of an intelligent conversational assistant has been developed that integrates a RAG mechanism, dynamic routing between several neural language models, and automatic updating of the knowledge base through parsing Admiral F. F. Ushakov State Maritime University web resources. The proposed approach ensures robust processing of variable user queries and response generation based on the retrieved context. Unlike existing solutions that rely on a fixed language model, the proposed architecture implements adaptive selection of a generative model depending on the type of user query. A method for domain-oriented context construction for processing dynamically updated tabular schedule data has been developed, including specialized web page parsing, semantic structuring of the extracted information, and prompt engineering rules. To improve the factual reliability of responses, a mechanism is proposed that constrains generation to the retrieved data and applies terminological filtering. An ETL pipeline for updating the knowledge base has been implemented, enabling automatic updating of information about university class schedules. A comparative study of the effectiveness of the Qwen, GigaChat, and DeepSeek language models in processing user queries within the considered subject domain has been conducted. The obtained results demonstrate improved response accuracy and confirm the effectiveness of the proposed conversational assistant architecture.
N. A. Polkovnikova· Vestnik Gosudarstvennogo uni...· 0 citations
Large language models (LLMs) have sparked renewed interest in Conversational Information Retrieval (CIR). Within this shift, Conversational Dataset Retrieval (CDR) is emerging as a new subfield that focuses on using natural, context-aware dialogue to discover structured and semi-structured datasets. We present the first integrative review of this rapidly evolving landscape. We synthesise insights from 44 publications spanning user studies, conversational system design choices, dataset representation and access, and evaluation methods. We introduce a novel, layered conceptual framework that organizes research in CDR across four key dimensions: User Layer, System Layer, Data Layer, and Evaluation Layer. This framework is used to highlight recurring design patterns, technical advances, and persistent gaps. Our analysis identifies core challenges in CDR, including the lack of standardized evaluation benchmarks and limited support for ambiguous or evolving user intent. The aim of this survey is to provide a structured foundation for future research on CDR and guide the development of more interactive, intelligent, and user-centric dataset retrieval systems.
Lisa-Yao Gan, Johanna Walker, E. Simperl et al.· Information Systems Frontier...· 0 citations