중학교 수학 개념 학습을 위한 시나리오 기반 챗봇 설계 연구
Designing a scenario-based AI chatbot for concept learning in middle school mathematics
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본 연구는 중학교 수학 개념 학습을 지원하기 위해 시나리오 기반 AI 챗봇 수리봇(SuriBot)을 개발하고 그 교육적 활용 가능성을 탐색하는 데 목적이 있다. 개념 기반 탐구 학습, 인지유연성, 사회정서학습 이론을 반영하여 372개 블록, 1,379개 대화 세트, 88개 태그, 50개 이미지를 포함한 시나리오형 콘텐츠를 설계하고, NAVER Clova Chatbot Builder와 TalkTalk 플랫폼을 활용해 구현하였다. 시나리오 기반 피드백은 개념 탐구와 재도전을 유도하며, 텍스트・이미지・퀴즈・외부 링크등 멀티모달 응답은 인지적 이해와 정서적 참여를 촉진하였다. 본 연구는 AI 챗봇이 교육과정 목표와 학습자 요구를 연결하는 효과적인 교수・학습 도구가 될 수 있음을 시사한다.
Abstract
An abstract of the manuscript, 200–300 words. The rapid acceleration of digital transformation has heightened interest in integrating artificial intelligence (AI) into mathematics education to provide personalized feedback, foster self-directed learning, and support affective engagement. This study presents the design and implementation of SuriBot, a scenario-based AI chatbot developed to enhance concept learning in middle school mathematics. Grounded in concept-based learning, cognitive flexibility, and social-emotional learningtheories, SuriBot was built using NAVER Cloud’s Clova Chatbot Builder and deployed via the Naver TalkTalk platform to ensure accessibility. Our five-phase research procedure involved: (1) theoretical and prior research analysis to extract core design principles; (2) content structuring to align chatbot dialogue with the revised 2022 mathematics curriculum; (3) scenario design to map concept hierarchies and embed affective feedback; (4) chatbot implementation, creating 1,379 dialogue sets, 50 supporting images, and 88 dialogue tags organized into 372 scenario blocks; and (5) iterative testing and refinement through internal and pilot evaluations. SuriBot delivers multimodal responses—including text definitions, visual aids, interactive quizzes, and external learning links—and offers scaffolded feedback and retries tailored to learners’ performance and emotional state. Preliminary analysis indicates that the scenario-based approach effectively guides students through progressive concept exploration while maintaining engagement and reducing math anxiety through positive reinforcement. The integration of structured feedback loops and varied response types supports both cognitive understanding and emotional resilience. This research contributes a practical model for AI-enhanced mathematics instruction, demonstrating how carefully crafted dialogue scenarios can bridge curriculum goals with learner needs.