Jul 2026· 2026 IEEE International Conference on Innovation, Ethics & Emerging Tech in Engineering and Computing Education (IE2C)· pp. 1-6· 0 citations· 15 references
Abstract
Large Language Models (LLMs) are increasingly used in AI-assisted engineering education, enterprise learning, technical knowledge support, and software-development workflows. However, static retrieval-augmented generation (RAG) pipelines retrieve external context for every query, which can increase latency, token cost, and irrelevant-context exposure, while direct generation can produce unsupported or stale answers for policy-sensitive, documentation-dependent, or multisource questions. This paper presents a production-oriented adaptive retrieval framework that treats retrieval as a conditional reliability decision rather than a default execution step. The framework combines query embeddings, similarity-based query memory, feedback-confirmed routing labels, comparison/fallback handling, and observability logs to decide whether a query should be answered directly, routed through retrieval, or escalated for evidence comparison. The system was evaluated in a production-similar controlled test bed using a 1,300-scenario validation workload and public benchmark validation using HotpotQA and RAGTruth. In the 1,300-scenario evaluation, the proposed method achieved 91.31% routing accuracy, 85.00% grounded-answer rate, 85.38% unsupported-answer detection, and 79.85% fallback correctness, while reducing mean latency compared with always-on Static RAG. Paired statistical testing showed significant improvements over LLM-only, Static RAG, and Threshold RAG baselines for routing, grounding, unsupported-answer detection, fallback behavior, and latency. The paper also discusses deployment considerations including query-memory growth, stale no-retrieval labels, knowledge-base version tracking, privacy-preserving redaction, and instructor oversight for responsible educational use.
This paper presents the design and implementation of intelligent chatbots using Retrieval-Augmented Generation architecture to derive accurate, context-based responses from domain-specific knowledge sources such as PDFs and CSV files. The proposed system integrates information retrieval mechanisms with large language models to overcome crucial limitations of traditional chatbots: hallucination, knowledge cut-off, and inability to access private organizational data. The proposed architecture uses a six-stage pipeline consisting of document ingestion, text splitting, and embedding generation with sentence transformers, semantic retrieval, and response generation via Large Language Models (LLMs). By preprocessing the PDF and CSV documents using a RecursiveCharacterTextSplitter and converting them into 384-dimensional semantic embeddings, the system works via a similarity-based retrieval of document chunks. For implementation, the LlamaIndex framework was used for data indexing and query orchestration, and HuggingFace Transformers for language modeling. Experimental validation conducted on the smart campus chatbot system demonstrated significant improvements in the accuracy, relevance and transparency of responses across education, student services, and administrative support domains compared with a standalone language model. Because of its modular design, it supports a wide range of domain-specific topics without the combinatorial costs of re-training. This is another important application for systems that require regular updates and accurate information. Overall, the proposed framework provides a simple and practical approach for easily implementing domain-aware chatbots.
Varsha Spoorthi Shree Narravula, V. K, Yashaswini B. Suresh et al.· 2026 7th International Confe...· 0 citations
The results show that the proposed RAG agent substantially improves the lightweight base model and produces transparent, syllabus-grounded answers that experts rated as correct and concise, while GPT-4.5 retains an edge on longer, multistep, and topology-intensive tasks.
André Rocha, Paulo Oliveira, João Ferreira et al.· Applied Sciences· 0 citations
Open language models are increasingly considered for institutional decision-support tasks in higher education, including policy interpretation, academic advising, administrative summarization, and quality-assurance workflows. However, their reliable deployment requires more than model availability: it depends on cloud-native orchestration, retrieval quality, evidence grounding, refusal behavior, monitoring, and governance controls. Following a design-science research approach, this paper presents an architectural artifact for deploying open language models in higher education decision support. The artifact operationalizes institutional reliability as a multidimensional construct composed of contextual accuracy, answer faithfulness, retrieval quality, refusal adequacy, latency compliance, auditability, and human-review compatibility, and aggregates these into an institutional reliability index. It proposes a reliability-aware retrieval-augmented generation pipeline that integrates governed document ingestion, embedding generation, hybrid retrieval, reranking, evidence-aware generation, confidence-based refusal, human review, audit logging, and post-deployment monitoring. To support reproducibility, the paper compares four deployment configurations and provides an illustrative worked example of the reliability index. The contribution is a conceptual yet technically grounded deployment artifact that connects cloud computing, data science, and higher education governance; the architecture has not yet been empirically validated, and a protocol for future institutional pilots is specified.
I. García-López, Nicia Guillén-Yparrea· Computers· 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
This work compares a baseline Academic Supervision Assistant, a GPT-5 chatbot with no scaffolding, against a multi-module system, Academic Supervision System (ASuS) that wraps the much smaller GPT-4o-mini in a LangGraph harness, and argues that where reliability, traceability, and institutional consistency matter more than open-ended fluency, harness engineering challenges the prevailing'bigger model is better'intuition.
E EduGuard, a safe retrieval-augmented generation (RAG) tutoring framework for introductory programming, is presented and compared against strong baselines, suggesting safe GenAI tutoring requires not only retrieval or strong prompting, but explicit pedagogical control, evidence verification, and deployment safeguards.
S. M. Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha et al.· arXiv.org· 0 citations
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