An Integrating AI Chatbot into AR Board Game DINASTI for Elementary History Learning

Authors

  • Burhan Hidayatulloh Universitas Negeri Surabaya
  • I Kadek Dwi Nuryana Universitas Negeri Surabaya

DOI:

https://doi.org/10.26740/jeisbi.v7i2.83185

Keywords:

AI Chatbot, Augmented Reality, Board Game, Learning Media, ADDIE Model, QR Scanner

Abstract

Learning the history of the Indonesian archipelago in Social and Natural Sciences (IPAS) at the elementary school

level often faces challenges as the material is considered abstract, monotonous, and teacher-centered, leading to

low student motivation and an average history test score of 67.5 (below the mastery criteria of 75) at SDN 163

Gresik. This study aims to develop and evaluate the effectiveness of the "DINASTI" board game as an innovative

learning media that integrates physical mechanics with QR Scanner technology, Augmented Reality (AR) 3D

visualization, and an AI Chatbot acting as an adaptive virtual tutor. The research method employed was Research

and Development (R&D) using the ADDIE model, involving 21 fourth-grade students as trial subjects, with data

gathered through learning outcome tests, expert validation questionnaires, and System Usability Scale (SUS)

surveys. The results indicated that quantitative validation by media experts declared the product Highly

Feasible/Highly Valid with a cumulative average score of 4.67 out of 5.00. Furthermore, the usability test via SUS

yielded an average score of 78.81, placing it in the Acceptable category with a grade B+ and a Good predicate. In

conclusion, the DINASTI learning media proved significantly effective in improving students' learning outcomes

while successfully maintaining face-to-face social interaction in the classroom.

Downloads

Download data is not yet available.

Downloads

Published

2026-07-28

How to Cite

Hidayatulloh, B., & Nuryana, I. K. D. (2026). An Integrating AI Chatbot into AR Board Game DINASTI for Elementary History Learning. Journal of Emerging Information Systems and Business Intelligence (JEISBI), 7(2), 353–362. https://doi.org/10.26740/jeisbi.v7i2.83185
Abstract views: 2 , PDF Downloads: 1