한국과학교육학회지 (Journal of The Korean Association For Science Education)

  • 제44권5호
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  • Pages.511-518
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  • 2024
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  • 1226-5187(pISSN)
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  • 2288-8489(eISSN)
한국과학교육학회 (The Korean Association for Science Education)
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AI 활용 교육에 대한 화학 교사의 인식 분석 -1급 정교사 자격 연수 참여자를 중심으로-

Analysis of Chemistry Teachers' Perceptions of AI Utilization in Education: Focusing on Participants in First-Grade Teacher Qualification Level Training

  • 김성기 (한국교육과정평가원)
  • Sungki Kim (Korea Institute for Curriculum and Evaluation)
  • 투고 : 2024.08.27
  • 심사 : 2024.10.07
  • 발행 : 2024.10.31
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초록

연구는 화학 교사를 대상으로 AI 활용 교육에 대한 인식을 관심도, 효과 기대, 실행을 방해하는 요인을 중심으로 알아보았다. 2024년 화학 1급 정교사 자격 연수에 참여한 79명의 화학 교사를 대상으로 설문 조사를 통해 데이터를 수집하였다. 관심도는 전반적인 관심도와 개별적 관심도를 분석하고, 배경 변인에 따른 관심도 차이를 Kruskal-Wallis H 검정으로 분석하였다. 효과 기대는 7가지 항목에서 측정하고, 이에 따른 차이를 반복측정 ANOVA와 Bonferroni 방법으로 분석하였다. 실행을 방해하는 요인은 내적 요인과 외적 요인을 중심으로 키워드 분석하였다. 연구 결과, 전반적인 관심도는 상대적으로 낮았으며, 특히 정보적 관심(1단계)과 무관심(0단계)이 각각 35.4%와 34.2%로 높게 나타났다. 현직 교사일 때 관련 연수 경험 유무에 따라 관심도에서 유의미한 차이를 보였다(p<.05). AI 활용 교육에 대한 효과 기대에서 7가지 항목의 효과 기대가 통계적으로 유의미한 차이가 나타났다(p<.05). 교사들은 '다양한 학습 경험 제공'을 가장 높은 효과로 평가했으며, 반면에 '과학 개념 이해 증진', '과학적 탐구 능력 증진', '과학적 소양 함양'은 상대적으로 낮게 평가하였다(p<.05). 실행을 방해하는 요인으로는 내적 요인의 비율이 외적 요인에 비해 높았으며, 내적 요인에 해당하는 키워드로 '변화에 대한 부담', '역량 부족', 'AI 활용 교육에 대한 교사의 부정적 인식'이 있었다. 이 결과를 바탕으로 AI 활용 교육의 실행을 위한 방안을 제안하였다.

This study investigated the perceptions of chemistry teachers regarding the use of AI in education, focusing on their stages of concern, expected effects, and factors impeding implementation. Data were collected through a survey of 79 chemistry teachers who participated in first-grade teacher qualification training in 2024. The stages of concern were analyzed both overall and individually, and differences in stages of concern based on background variables were examined using the Kruskal-Wallis H test. The expected effects were measured across seven aspects, with differences were analyzed using repeated measures ANOVA and the Bonferroni method. Factors impeding implementation were analyzed through keyword analysis, focusing on internal and external factors. The results showed that overall concern was relatively low, with informational concern (Stage 1) and unconcerned (Stage 0) being high at 35.4% and 34.2%, respectively. Among active teachers, significant differences in stages of concern were observed depending on whether they had training experience (p<.05). The expected effects of AI in education showed significant statistical differences across the seven aspects (p<.05). Teachers rated 'providing diverse learning experiences' as the highest effect, while 'enhancing understanding of scientific concepts', 'improving scientific inquiry skills', and 'cultivating scientific literacy' were rated relatively low (p<.05). Internal factors were found to impede implementation more than external factors, with key internal factors including 'resistance to change', 'lack of capability', and 'teachers' negative perceptions of AI in education'. Based on these findings, recommendations were made to enhance the implementation of AI in educational settings.

키워드

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