Design and Implementation of a Voice-Enabled Intelligent Educational Chatbot using Natural Language Processing
Year: 2026
Type: conference-paper
Abstract: The rapid expansion of online education has exposed significant gaps in traditional "one-size-fits-all" delivery models, which often lack the personalized, real-time intervention of human tutors. This research addresses these limitations by designing and implementing a Voice-Enabled Intelligent Educational Chatbot that integrates Natural Language Processing (NLP) with Bayesian Knowledge Tracing (BKT) to create an adaptive learning environment. The system utilizes a microservices architecture combining Automatic Speech Recognition (ASR) with transformer-based intent classification to facilitate natural spoken interaction. Methods involving fine-tuning DistilBERT for educational intent recognition and the deployment of a real-time mastery estimation engine were evaluated. Results from a controlled study with 150 undergraduate students demonstrate that the voice-enabled adaptive interface leads to a 34% improvement in learning outcomes and a 53% increase in student engagement compared to static text platforms. These findings suggest that multimodal conversational interfaces effectively reduce cognitive load and enhance accessibility in digital education, providing a scalable solution for personalized instruction.
Institution: Mohan Babu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166705779