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    협동학습 지원을 위한 AI 기반 그룹 채팅 시스템 설계 및 개발 연구 = A Study on the Design and Development of an AI Based Group Chat System for Collaborative Learning

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    https://www.riss.kr/link?id=A110102528

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    Advances in generative artificial intelligence(AI) present new possibilities for education; however, most AI tutoring systems remain centered on one-to-one individualized learning, failing to capture the social-interaction context of real classrooms. This study aims to design and develop a system in which an AI teaching assistant promotes and supports meaningful peer interaction within a group-chat environment. Grounded in educational theories of cooperative learning, the pedagogical role of AI assistants, and the instructional impact of feedback, we established three core design principles-teacher-AI collaboration, differentiated feedback, and process-oriented assessment. In the proposed system, the AI teaching assistant interprets conversational context based on prompts configured by the teacher, offers tailored feedback to students, and facilitates collaborative interaction. By doing so, this research redefines and extends the educational role of AI from a personal tutor to a facilitator of collaborative knowledge construction, and presents a concrete architectural model for an instructional system in which humans and AI work in concert.
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    Advances in generative artificial intelligence(AI) present new possibilities for education; however, most AI tutoring systems remain centered on one-to-one individualized learning, failing to capture the social-interaction context of real classrooms. ...

    Advances in generative artificial intelligence(AI) present new possibilities for education; however, most AI tutoring systems remain centered on one-to-one individualized learning, failing to capture the social-interaction context of real classrooms. This study aims to design and develop a system in which an AI teaching assistant promotes and supports meaningful peer interaction within a group-chat environment. Grounded in educational theories of cooperative learning, the pedagogical role of AI assistants, and the instructional impact of feedback, we established three core design principles-teacher-AI collaboration, differentiated feedback, and process-oriented assessment. In the proposed system, the AI teaching assistant interprets conversational context based on prompts configured by the teacher, offers tailored feedback to students, and facilitates collaborative interaction. By doing so, this research redefines and extends the educational role of AI from a personal tutor to a facilitator of collaborative knowledge construction, and presents a concrete architectural model for an instructional system in which humans and AI work in concert.

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