AI 디지털교과서 도입에 따른 초등영어 읽기 학습 진단 및 맞춤형 학습 방안 탐색
Enhancing Diagnostic and Personalized Reading Instruction in Elementary School English Through AI-Powered Digital Textbooks
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참고문헌
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초록
본 연구는 2025학년도 초등영어 AI 디지털교과서 도입에 따른 학생들의 읽기 학습 진단 및 맞춤형학습 방안을 탐색하였다. AI 디지털교과서는 이전의 디지털교과서와 마찬가지로 각종 멀티미디어 자료와 그림 사전 등을 포함하고 있지만, 클라우드 방식의 인공지능형 학습 플랫폼으로 개발되어 학생들의 읽기 학습 과정과 결과를 다각적으로 분석ㆍ진단한 결과를 제공하고, 이에 따른 맞춤형 읽기 학습을 제공할 것으로 기대된다. 먼저 읽기 학습의 진단 방안을 탐색하고자, 본 연구는 기존에 개발된교육과정 표준체계의 내용 요소와 읽기 진단 선행 연구의 영역을 비교하였으며, 이들 내용 요소는 학습자의 읽기 능력을 진단하는 데 대체로 적절한 것으로 분석되었다. 다만 개별 학습자의 성취 수준이나 발달 단계를 세밀하게 포착하기 위해 내용 요소 간의 위계 및 연계를 보여주는 학습맵을 좀 더 세분화할 필요가 있었다. 그리고 세분화된 내용 요소와 연계된 문항 데이터베이스 구축이 필요하며, 음성 인식 엔진 등의 기능을 활용한 다양한 평가 방법이 필요한 것으로 파악되었다. 아울러 데이터 마이닝을 활용한 학습분석학 연구를 참고하여 학습자들의 읽기 패턴이나 수행 양상, 참여 정도를 다각적으로 수집하여 분석할 것을 제안하였다. 맞춤형 읽기 학습과 관련하여, 미국 사례와 우리나라 교육과정에 대한 비교 분석을 바탕으로 알파벳 체계, 유창성, 이해의 영역별, 난이도별 학습 콘텐츠 개발이필요함을 제안하였으며, 이를 위해 기존에 개발된 학습 콘텐츠의 분류 및 활용 방안을 탐색하였다. 그리고 학습자 특성에 기반한 코스웨어 설계 방안으로서, 그림과 텍스트 배치, 단일 화면 텍스트의 길이및 구성, TTS 기능의 적정한 활용 방안을 살펴보았다. 마지막으로 AI 디지털교과서의 발전 및 활용도를 높이기 위한 후속 연구 방향을 제시하였다.
Abstract
This study explores ways to assess students’ reading skills and provide individualized learning through AI digital English textbooks for elementary school students, scheduled for implementation in the 2025 academic year. Similar to previous digital textbooks, these AI versions will include various multimedia resources and picture dictionaries; however, they will also function as a cloud-based AI learning platform. This will allow for a comprehensive analysis of students’ reading skills and learning outcomes, thereby facilitating personalized reading instruction. To investigate diagnostic approaches, this study examined the suitability of the curriculum content elements for diagnostic purposes by comparing them with areas identified in previous reading research. Overall, the curriculum elements were found to be appropriate for the detailed assessment of students’ reading skills. However, to better capture individual achievement levels and developmental stages, a more detailed learning map illustrating the sub-elements was deemed necessary. A rich database of test items aligned with these sub-elements, along with various assessment methods, was also identified as essential. Drawing upon learning analytics research and data mining, this study further suggests a multifaceted analysis of students’ reading patterns, performance, and engagement. For individualized reading instruction, it proposes the development of level-differentiated learning content focusing on alphabet principles, fluency, and comprehension, based on a comparative analysis of instructional areas from the U.S., the National Reading Panel, and Korea’s curriculum. It also suggests ways to utilize existing learning content for individualized learning. To support effective courseware design, the study examined the optimal arrangement of images and text, text length, interactive on-screen features, and the appropriate use of text-to-speech (TTS) functions. Finally, the study concludes with recommendations for further research to enhance the development and application of AI digital textbooks.
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