• Title/Summary/Keyword: 대화형 튜터링 시스템

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Data Augmentation of English Reading Comprehension Tutoring Dialogs using ChatGPT (ChatGPT 를 이용한 독해 튜터링 대화 데이터 확장)

  • Hyunyou Kwon;Sung-Kwon Choi;Jinxia Huang;Oh-Woog Kwon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.05a
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    • pp.43-44
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    • 2023
  • 대화형 독해 튜터링 시스템을 위한 학생주도 대화 데이터셋 생성 및 확장에 ChatGPT 의 활용 가능성을 평가하였다. 단순히 수동으로만 구축한 기존의 데이터셋과 ChatGPT 에 의해 반자동으로 확장된 데이터셋을 비교한 결과, 구축량, 소요 시간, 비용 및 반복 작업 측면에서 ChatGPT 가 가진 유용성을 알 수 있었다. 그러나, 유형별 배분의 편중과, 부적절한 데이터 생성 등의 한계도 나타났다. Chat GPT 의 빠른 발전이 예상됨에 따라 대화형 튜터링 분야에 ChatGPT 에 의한 반자동 데이터 확장 방법이 널리 활용될 것으로 기대된다.

On the Development of Animated Tutoring Dialogue Agent for Elementary School Science Learning (초등과학 수업을 위한 애니메이션 기반 튜터링 다이얼로그 에이전트 개발)

  • Jeong, Sang-Mok;Han, Byeong-Rae;Song, Gi-Sang
    • Journal of The Korean Association of Information Education
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    • v.9 no.4
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    • pp.673-684
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    • 2005
  • In this research, we have developed a "computer tutor" that mimics the human tutor with animated tutoring dialog agent and the agent was integrated to teaching-learning material for elementary science subject. The developed system is a natural language based teaching-learning system using one-to-one dialogue. The developed pedagogical dialogue teaching-learning system analysis student's answer then provides appropriate answer or questions after comparing the student's answer with elementary school level achievement. When the agent gives either question or answer it uses the TTS(Text-to-Speech) function. Also the agent has an animated human tutor face for providing more human like feedback. The developed dialogue interface has been applied to 64 6th grade students. The test results show that the test group's average score is higher than the control group by 10.797. This shows that unlike conventional web courseware, our approach that "ask-answer" process and the animated character, which has human tutor's emotional expression, attracts students and helps to immerse to the courseware.

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Development of a customized GPTs-based chatbot for pre-service teacher education and analysis of its educational performance in mathematics (GPTs 기반 예비 교사 교육 맞춤형 챗봇 개발 및 수학교육적 성능 분석)

  • Misun Kwon
    • The Mathematical Education
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    • v.63 no.3
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    • pp.467-484
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    • 2024
  • The rapid advancement of generative AI has ushered in an era where anyone can create and freely utilize personalized chatbots without the need for programming expertise. This study aimed to develop a customized chatbot based on OpenAI's GPTs for the purpose of pre-service teacher education and to analyze its educational performance in mathematics as assessed by educators guiding pre-service teachers. Responses to identical questions from a general-purpose chatbot (ChatGPT), a customized GPTs-based chatbot, and an elementary mathematics education expert were compared. The expert's responses received an average score of 4.52, while the customized GPTs-based chatbot received an average score of 3.73, indicating that the latter's performance did not reach the expert level. However, the customized GPTs-based chatbot's score, which was close to "adequate" on a 5-point scale, suggests its potential educational utility. On the other hand, the general-purpose chatbot, ChatGPT, received a lower average score of 2.86, with feedback indicating that its responses were not systematic and remained at a general level, making it less suitable for use in mathematics education. Despite the proven educational effectiveness of conventional customized chatbots, the time and cost associated with their development have been significant barriers. However, with the advent of GPTs services, anyone can now easily create chatbots tailored to both educators and learners, with responses that achieve a certain level of mathematics educational validity, thereby offering effective utilization across various aspects of mathematics education.

Development and mathematical performance analysis of custom GPTs-Based chatbots (GPTs 기반 문제해결 맞춤형 챗봇 제작 및 수학적 성능 분석)

  • Kwon, Misun
    • Education of Primary School Mathematics
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    • v.27 no.3
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    • pp.303-320
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    • 2024
  • This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.

A Study on Success Strategies for Generative AI Services in Mobile Environments: Analyzing User Experience Using LDA Topic Modeling Approach (모바일 환경에서의 생성형 AI 서비스 성공 전략 연구: LDA 토픽모델링을 활용한 사용자 경험 분석)

  • Soyon Kim;Ji Yeon Cho;Sang-Yeol Park;Bong Gyou Lee
    • Journal of Internet Computing and Services
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    • v.25 no.4
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    • pp.109-119
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    • 2024
  • This study aims to contribute to the initial research on on-device AI in an environment where generative AI-based services on mobile and other on-device platforms are increasing. To derive success strategies for generative AI-based chatbot services in a mobile environment, over 200,000 actual user experience review data collected from the Google Play Store were analyzed using the LDA topic modeling technique. Interpreting the derived topics based on the Information System Success Model (ISSM), the topics such as tutoring, limitation of response, and hallucination and outdated informaiton were linked to information quality; multimodal service, quality of response, and issues of device interoperability were linked to system quality; inter-device compatibility, utility of the service, quality of premium services, and challenges in account were linked to service quality; and finally, creative collaboration was linked to net benefits. Humanization of generative AI emerged as a new experience factor not explained by the existing model. By explaining specific positive and negative experience dimensions from the user's perspective based on theory, this study suggests directions for future related research and provides strategic insights for companies to improve and supplement their services for successful business operations.