Jul 2026· Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S O Makarova· Vol 18, pp. 538-557· 0 citations· 11 references
TL;DR
This paper presents the development of an intelligent conversational assistant designed to support users in obtaining and clarifying information about university class schedules, and implements adaptive selection of a generative model depending on the type of user query.
Abstract
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.
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.
G. Vamsi, Vinay Kumar Male· International Scientific Jou...· 0 citations
A novel approach to Intelligent Tutoring Systems (ITS) is presented by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
The proposed WILO-BiLSTM model can perform superior to the conventional approaches and its performance results in terms of METEOR, BLEU, ROUGE, and SPICE score at training data 90% is 0.28, 0.50, 0.56, and 26.93 for the SquAD dataset, respectively.
Pallavi Yevale, Nilesh Uke· Journal of Intelligent Decis...· 0 citations
Writing remains one of the most challenging skills for English as a Second Language (ESL) learners because it requires the coordinated application of grammar, vocabulary, and written discourse conventions. This study proposes an Intelligent Writing Tutor that integrates corpus-informed error analysis, natural language processing (NLP), and rule-based reasoning to generate individualized and explainable writing feedback for ESL learners. Guided by a mixed-methods Design Science Research approach, weekly journal entries produced by Ilocano-speaking English language majors at the Kalinga State University served as the learner corpus for analysis. Manual expert annotation based on Corder's Error Analysis framework identified lexical, morphological, syntactic, and mechanical errors that informed the development of an interpretable rule-based feedback engine. The analysis revealed that morphological errors were the most frequent, followed by mechanical, lexical, and syntactic errors, with verb tense misuse emerging as the dominant writing difficulty. Qualitative findings further indicated that many of the observed errors reflected first-language interference, particularly in tense marking, subject–verb agreement, preposition usage, and lexical choice. The proposed framework operationalizes learner texts through preprocessing, NLP-assisted error detection, rule-based error classification, feedback generation, and recommendation modules to produce individualized feedback reports. The study demonstrates the feasibility of integrating explainable NLP techniques and second language acquisition principles into an educational writing support system. Because the framework was developed using journal writings from Ilocano-speaking learners in a single institution, its applicability to other learner populations requires further investigation.
R. L. Ladwingon· International Journal of Adv...· 0 citations
Recent developments in digital libraries increasingly favor conversational and natural language access to information through Retrieval-Augmented Generation (RAG). Although these approaches are effective for extractive tasks grounded in individual records, they remain limited in their ability to interpret document collections holistically and to incorporate expert knowledge dynamically. In this article, we present a document analysis system designed for the management of historical digital libraries that supports on-the-fly knowledge modeling. The system is equipped with the capability to store facts produced either by expert archivists or derived from document retrieval processes within a graph-based structure. Through continuous professional interaction, the system can retrieve information not only from primary sources such as documents, but also from previously modeled knowledge, with the graph-based index acting as a memory for the language model to access. This enables increasingly complex queries involving long-term dependencies across documents, link discovery, and the integration of expert knowledge that may not be explicitly present in the original sources. As a result, the proposed approach facilitates the generation of richer and more comprehensive information.
Paula Font Sola, Adria Molina Rodr'iguez, J. Lladós· 0 citations
To address the difficulty faced by university faculty and students in obtaining useful information from massive campus data, this paper proposes an intelligent campus question-and-answer (Q&A) system based on dynamic retrieval-augmented generation (RAG) technology, using campus administrative knowledge as the data source. The system integrates large language models (LLMs) with domain-specific professional knowledge, leveraging the Campus All-in-One project as a foundation. It constructs a campus knowledge base that includes administrative guides, frequently asked questions, and regulatory documents as an external data corpus. By applying the Infinity database, designed specifically for dynamic RAG applications, and employing prompt engineering, the model’s ability to generate accurate and context-aware answers is enhanced. Through this dynamic RAG-based approach tailored for the education domain, the system provides users with interactive access to a wide range of campus administrative information, helping to resolve common issues, simplify inquiry processes for teachers and students, and reduce the workload of campus management.
Charan Thumma, Abhignan Srivatsava Sribhashyam, Chaitanya Tumma et al.· 2026 International Conferenc...· 0 citations
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