과학교육 전문가의 ChatGPT 활용 SSI 교육 개념틀 개발 과정 분석
An Analysis of Science Education Professionals' Use of ChatGPT in Developing a Conceptual Framework for Socio-Scientific Issues (SSI)
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초록
이 연구는 과학교육 전문가들이 ChatGPT를 활용하여 SSI(사회적 과학적 이슈) 교육 개념틀을 개발하는 과정을 심층적으로 분석하고, 생성형 인공지능이 문제해결을 지원할 수 있는지에 대한 통찰을 제공하는 것을 목표로 한다. 연구는 과학교육 전문가 8명을 대상으로, ChatGPT를 활용하여 SSI 교육 개념틀을 개발하는 과제를 수행하도록 하였다. 연구 결과, 첫째, SSI 교육 개념틀 개발 과정에서 비판적 사고, 윤리적 판단, 학제 간 통합, 사회적 책임 등 핵심 요소가 도출되었으며, 이는 SSI 교육이 다양한 관점을 반영하며, 복합적인 문제해결 능력을 길러주는 데 중점을 둔다는 것을 의미한다. 또한, 전문가들은 학습자들이 실생활에서 과학적 지식을 적용하고, 사회적 참여를 통해 협력적 학습을 강조할 필요가 있다고 지적했다. 둘째, 과학교육 전문가들의 생성형 인공지능을 활용한 문제해결 과정은 과제 정의, 산출물 최적화, 산출물 평가 및 적용, 종결의 4단계로 구성되었으며, 프롬프트 입력 패턴에서 각 전문가가 집중한 단계가 서로 달랐으며, 이는 과제 수행 과정에서 전문가마다 중점적으로 다루는 세부 활동이 다름을 보여준다. 셋째, 이러한 과제 수행 과정을 기반으로, ChatGPT 활용 과제를 추상화하여 시각적이고 일반화된 4단계의 구조적 과제 수행 모델을 제안하였다. 이 연구는 생성형 인공지능과 인간의 협력을 기반으로 한 혁신적인 교수학습 전략 개발에 중요한 기초 자료로 활용될 수 있을 것이다.
Abstract
The purpose of this study was to investigate how generative AI could support problem-solving by analyzing how science education experts developed a conceptual framework for Socio-Scientific Issues (SSI) education using ChatGPT. Eight science education experts participated in the study and were tasked with utilizing ChatGPT to construct an SSI education framework. The findings revealed three key insights. First, critical thinking, ethical judgment, interdisciplinary integration, and social responsibility were identified as essential components of the SSI framework. These elements highlight the role of SSI education in fostering complex problem-solving skills through diverse perspectives. Experts also emphasized the need for learners to apply scientific knowledge to real-world contexts and engage in collaborative learning through social participation. Second, the problem-solving process with generative AI was categorized into four stages: task definition, output optimization, output evaluation and application, and conclusion. The varying prompt input patterns observed among the experts indicated differences in the focus of specific activities across the stages, reflecting individualized approaches to task execution. Third, based on the observed task execution process, a visualized and generalized four-stage structural task execution model using ChatGPT was proposed. This model emphasizes a nonlinear and flexible structure, enabling diverse problem-solving pathways. This study provides a foundational resource for developing innovative teaching and learning strategies that leverage the collaboration between generative AI and human problem-solving, offering promising directions for enhancing educational practices.
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