학술논문
생성형 인공지능 챗봇을 활용한 수학과 인공지능 융합 수업 모형 개발
Development of a math-AI convergence instructional model using a generative AI chatbot
Development of a math-AI convergence instructional model using a generative AI chatbot
Document Type
Article
Source
교육공학연구, 40(1), pp.1-40 Mar, 2024
Subject
Language
한국어
ISSN
1225-424X
Abstract
인공지능의 교육적 활용을 넘어 미래인재의 창의적 문제해결역량 향상을 위한 인공지능 융합 교육의 필요성이 강조되고 있다. 하지만 교과-인공지능 융합 수업의 사례와 전략에 대한 연구는 여전히 충분하지 않다. 이에 본 연구는 수학 교과에서 생성형 인공지능을 활용한 인공지능 융합 교육의 가능성을 탐색하고 이를 수업 모형의 형태로 개발하였다. 이를 위해 설계개발연구 방법론에 따라서 문헌 고찰 및 교사 인터뷰를 통해 초기모형을 개발하고, 인공지능 기반 수학교육 전문가 4인을 대상으로 2회의 전문가 타당화를 통해 수업 모형을 수정․보완하였다. 이에 따른 최종 수업 모형의 외적 타당성 확보를 위해 수원의 S 고등학교 1학년 8개 반 총 204명을 대상으로 모형을 적용하여 수업을 실행하고 설문 및 면담을 실시하였다. 연구결과, 학습자의 인공지능 리터러시 수준과 수학적 문제해결력에서 유의한 변화가 관찰되었으며, 학습자의 긍정적 반응이 확인되었다. 본 연구를 통해 수학과 인공지능 융합 수업의 특징, 생성형 인공지능의 AI 융합 교육 활용 가능성 및 한계, 교원의 인공지능 융합 교육역량의 중요성에 대한 시사점이 제시되었다.
Emphasis on the importance of AI convergence education for enhancing the creative problem-solving abilities of young learners is increasing, in addition to its educational applications. However, studies examining the practical implementation and strategies of AI convergence education remain scarce. Consequently, this study aims to investigate the feasibility of incorporating AI convergence education, particularly utilizing generative AI, within mathematics subjects. The study seeks to develop an instructional model and implementation strategy for this purpose. An initial model was formulated through a review of pertinent literature and interviews with practicing teachers following the design and development research methodology. Subsequently, the model underwent two rounds of expert validation involving four experts specializing in AI-based mathematics education. To assess the external validity of the final instructional model, 204 students across 8 first-grade classes at S High School in Suwon participated in tests, surveys, and interviews following the implementation of the model. The findings revealed significant improvements in learners’ AI literacy levels and mathematical problem-solving skills, accompanied by positive feedback from the students. Through this study, insights into the characteristics of mathematics and AI convergence education, the potential and constraints of employing generative AI for AI convergence education, and the significance of educators’ capabilities in AI convergence education were elucidated.
Emphasis on the importance of AI convergence education for enhancing the creative problem-solving abilities of young learners is increasing, in addition to its educational applications. However, studies examining the practical implementation and strategies of AI convergence education remain scarce. Consequently, this study aims to investigate the feasibility of incorporating AI convergence education, particularly utilizing generative AI, within mathematics subjects. The study seeks to develop an instructional model and implementation strategy for this purpose. An initial model was formulated through a review of pertinent literature and interviews with practicing teachers following the design and development research methodology. Subsequently, the model underwent two rounds of expert validation involving four experts specializing in AI-based mathematics education. To assess the external validity of the final instructional model, 204 students across 8 first-grade classes at S High School in Suwon participated in tests, surveys, and interviews following the implementation of the model. The findings revealed significant improvements in learners’ AI literacy levels and mathematical problem-solving skills, accompanied by positive feedback from the students. Through this study, insights into the characteristics of mathematics and AI convergence education, the potential and constraints of employing generative AI for AI convergence education, and the significance of educators’ capabilities in AI convergence education were elucidated.