Rule-Based Adaptive Chatbot on WhatsApp for Visual, Auditory, and Kinesthetic Learning Style Detection

Muhammad Rahulil; Yuni Yamasari; Ricky Eka Putra; I made Suartana; Anita  Qoiriah

doi:10.35882/jeeemi.v8i1.1215

Muhammad Rahulil Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia. https://orcid.org/0009-0000-4394-9572
Yuni Yamasari Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia https://orcid.org/0000-0001-9719-3491
Ricky Eka Putra Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia. https://orcid.org/0000-0002-5515-7967
I made Suartana Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia. https://orcid.org/0000-0002-2877-5778
Anita Qoiriah Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia. https://orcid.org/0000-0002-7227-6642

DOI: https://doi.org/10.35882/jeeemi.v8i1.1215

Keywords: chatbot, whatsapp, learning styles, Expert Systems, Personalized Learning

Abstract

Adapting learning methods to individual learning styles remains a major challenge in digital education due to the static nature of traditional questionnaires and the absence of adaptive feedback mechanisms. This study aimed to develop a rule-based adaptive WhatsApp chatbot capable of automatically identifying users’ learning styles, visual, auditory, and kinesthetic, through a weighted questionnaire enhanced with probabilistic refinement. The proposed system introduces an adaptive decision framework that dynamically manages conversation flow using score dominance evaluation, early termination, and selective question expansion. Bayesian posterior probability estimation is employed to strengthen decision confidence in borderline cases, ensuring consistent and interpretable results even when user responses are ambiguous. The chatbot was implemented using WhatsApp-web.js and MongoDB, supported by session validation and activity log monitoring to ensure operational reliability and data integrity. System validation involved white-box testing using Cyclomatic Complexity to verify logical accuracy and 20-fold cross-validation using a Support Vector Machine (SVM) to evaluate classification performance. The adaptive model achieved an accuracy of 80.2% and an AUC of 0.902, supported by a balanced precision (0.738), recall (0.662), and F1-score (0.698). These results demonstrate stable discriminative capability and confirm that the adaptive scoring mechanism effectively reduces redundant questioning, lowers cognitive load, and improves interaction efficiency without compromising reliability. In conclusion, the study successfully achieved its objective of developing an adaptive, efficient, and mathematically transparent learning style detection system. The findings confirm that adaptive rule-based logic reinforced by probabilistic reasoning can significantly enhance the efficiency and reliability of digital learning assessments. Future research will extend this framework by incorporating multimodal behavioral indicators and personalized learning content to further strengthen adaptive learning support

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