Development of student intent-based educational chatbot system with adaptive and attentive DTCN on symmetric convolution approach
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an intelligent educational chatbot that uses deep learning and a novel optimization algorithm to quickly and accurately answer student academic inquiries, saving time for students and administrators.
Area Of Science
- Artificial Intelligence
- Natural Language Processing
- Educational Technology
Background
- Accessing academic information on institutional websites is time-consuming for students.
- Current chatbot platforms often provide irrelevant outputs and inaccurate user intent prediction.
- There is a need for an automated, intelligent system to provide quick and precise academic assistance.
Purpose Of The Study
- To develop an automated, student intent-based educational chatbot.
- To integrate deep learning and advanced optimization algorithms for accurate intent recognition.
- To improve student engagement by providing timely and relevant academic information.
Main Methods
- Pre-processing student inquiries and generic chatbot data.
- Representing text data as vectors using BERT, TransformerNet, and Text CNN.
- Employing the Averaging-based Driving Training - Barnacles Mating Optimizer (ADT-BMO) for feature selection and optimization.
- Utilizing an adaptive deep neural network with symmetric convolution (AA-DTCN-SC) for intent recognition.
- Optimizing the AA-DTCN-SC model parameters using ADT-BMO.
Main Results
- The developed chatbot demonstrates superior performance compared to baseline models (DTCN, RNN, Bi-LSTM).
- Performance improvements were noted as 4.44% (DTCN), 3.3% (RNN), 10.59% (Bi-LSTM), and 11.9% (unspecified baseline).
- The ADT-BMO algorithm effectively optimizes feature weighting and model parameters for enhanced accuracy.
- The system automatically generates contextually relevant responses to student queries.
Conclusions
- The research presents a well-functioning, scalable, and time-saving educational chatbot.
- The chatbot enhances student engagement through quick, precise, and pertinent scholarly assistance.
- The novel AA-DTCN-SC model, optimized by ADT-BMO, effectively addresses limitations of existing chatbot platforms in educational settings.
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