2026· International journal of research and innovation in social science· Vol 10, pp. 8426-8439· 0 citations
TL;DR
AskEPTbot, a machine-learning Telegram chatbot developed to act as a one-stop centre for EPT information that is accessible to both lecturers and students, is introduced.
Abstract
The English Placement Test (EPT) allows first-semester diploma students at Universiti Teknologi MARA (UiTM) to be exempted from the compulsory first-level English proficiency course if they sit and pass it. Communicating accurate and timely information about the test across the university's branch campuses has long been difficult, because there has been no single, centralised information hub, and important materials were often lost among the many messages exchanged between lecturers and students. This study introduces AskEPTbot, a machine-learning Telegram chatbot developed to act as a one-stop centre for EPT information that is accessible to both lecturers and students. Building the chatbot involved integrating EPT information into its knowledge database and enabling a machine-learning feature designed to return increasingly accurate responses as more users interact with it. The aim was to coordinate the EPT communication system and to ease the workload of the lecturers in charge (LICs) at 22 UiTM branches nationwide, who had previously answered hundreds of enquiries individually or through WhatsApp groups. A two-pronged quantitative design combined secondary data analysis of user analytics with a user satisfaction survey. Analytics from 5,067 users showed a high volume of repeat sessions and interactions per user, and the chatbot returned accurate responses in 94.61% of the sampled cases during a single evaluation week. Survey respondents also reported high satisfaction with the chatbot's usefulness, its ability to meet expectations, its response speed, the clarity of its replies, and its ease of use. AskEPTbot therefore functioned as a practical, well-used information hub and was designed to reduce the administrative burden on LICs.
This action research evaluated the use of Generative Artificial Intelligence (GenAI) tool, ChatGPT, to support students in note-taking practice. It was aimed to see if forty-nine students in an English course at a Melbourne-based institution (hereafter, referred to as MI) would independently use ChatGPT for note-taking and quiz generation after three GenAI lessons. Applying a mixed-method approach, data were collected through two surveys, three after-class polls, and three focus group interviews. The two main findings included a significant improvement in student engagement and the importance of explicit GenAI teaching in class. However, three key issues were identified: (1) the challenge for English language learners to construct effective task prompts, (2) the quality of questions generated by ChatGPT, and (3) the selection of appropriate audio materials. Our recommendations, therefore, include integrating GenAI lessons into the syllabus and providing ChatGPT prompt writing training for both teachers and students.
Unknown authors· The English Australia Journa...· 0 citations
Michael, a syllabus-aware AI teaching assistant designed to scaffold reasoning through structured, hint-first dialogue aligned with course progression, rather than providing direct solutions, is introduced, suggesting that curriculum-aligned constraints and hint-first scaffolding can support instructional integration without displacing pedagogical goals.
Or Peretz, Roei Zerahia· International Journal of Inf...· 0 citations
The study successfully validated that the intelligent chatbot efficiently bridges communication barriers, automates repetitive administrative inquiries, and improves service accessibility, confirming that the application is highly usable, practical, and effective as an inclusive, assistive communication tool.
Gil G. Dialogo, Hershey Alburo-Abugadie, Enrico C. Lucero· International journal of com...· 0 citations
Universities now use Large Language Models (LLMs) to transform their processes for managing student information. The paper introduces an upgraded chatbot system for Narasaraopeta Engineering College (NEC) which extends previous on-premise LLM chatbot research by providing four new functions. The system uses (1) Retrieval-Augmented Generation (RAG) to create citation-based responses through LlamaIndex and ChromaDB, (2) Context Memory which maintains conversation flow during multiple dialogue exchanges, (3) Voice Input through OpenAI Whisper Speech-to-Text (STT) technology, and (4) Multilingual Support which covers English and these seven languages: Hindi, Telugu, Tamil, Kannada, and Malayalam through IndicNLP. The system tested 60 benchmark questions across four academic categories which included regulations and examination policies and fee structures and multilingual queries and achieved 96.7% overall accuracy with sub-second text response times and 1.0–1.4 second voice response times. The system operates entirely on-premise through Docker which safeguards institutional data privacy while eliminating the need for recurring cloud API expenses. The upcoming development will create Emotion-Aware AI, FAQ Auto-Learning, Student Portal Integration, and a Mobile Application.
M. Yaswanth, Kopparapu Sai Amar Durgesh, Mogili Harsha Vardhan et al.· 2026 7th International Confe...· 0 citations
Structured human-computer interactions with higher education chatbots to explore whether these chatbots were programmed to provide financial counseling to college students found that many systems marked as AI chatbots fell short of adaptive, generative capabilities which are the essence of AI systems.
Richard Simonds, Z. Taylor, Sara Ray· Journal of Ethics and Emergi...· 0 citations
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