[논문]텍스트 기반 생성형 인공지능의 이해와 과학교육에서의 활용에 대한 논의

조헌국

doi:10.14697/jkase.2023.43.3.307

텍스트 기반 생성형 인공지능의 이해와 과학교육에서의 활용에 대한 논의
Understanding of Generative Artificial Intelligence Based on Textual Data and Discussion for Its Application in Science Education 원문보기

한국과학교육학회지 = Journal of the Korean association for science education, v.43 no.3, 2023년, pp.307 - 319

초록
AI-Helper

본 연구는 최근 주목받고 있는 텍스트 기반 생성형 인공지능에 대해 관심과 활용이 증가함에 따라 과학교육적 측면에서의 활용을 위해 생성형 인공지능의 주요 개념과 원리를 설명하고, 이를 효과적으로 활용할 수 있는 방안과 그 한계를 지적하며 이를 토대로 과학교육의 실행과 연구의 측면에서 시사점을 제공하는 것을 목적으로 한다. 최근 들어 증가하고 있는 생성형 인공지능은 대체로 인코더와 디코더로 이뤄진 트랜스포머 모델을 기반으로 하고 있으며, 인간의 피드백을 활용한 강화학습과 보상 모델에 대한 최적화, 문맥에 대한 이해 등을 통해 놀라운 발전을 이루고 있다. 특히, 다양한 사용자의 질문이나 의도를 이해하는 능력과 이를 바탕으로 한 글쓰기, 요약, 제시어 추출, 평가와 피드백 등 다양한 기능을 수행할 수 있다. 또한 교수자가 제시하는 예를 토대로 주어진 응답을 평가하거나 질문과 적절한 답변을 생성하는 등 학습자에 대한 진단과 실질적 교육내용의 구성 등 많은 유용성을 가지고 있다. 그러나 생성형 인공지능이 가지고 있는 한계로 인해 정확한 사실이나 지식에 대한 잘못된 전달, 과도한 확신으로 인한 편향, 사용자의 태도나 감정 등에 미칠 영향의 불확실성 등에 대한 문제 등에 대해 해가 없는지 검토가 필요하다. 특히, 생성형 인공지능이 제공하는 응답은 많은 사람들의 응답 데이터를 기반으로 한 확률적 접근이므로 매우 거리가 멀거나 새로운 관점을 제시하는 통찰적 사고나 혁신적 사고를 제한할 우려도 있다. 이에 따라 본 연구는 과학교수학습을 위해 인공지능의 긍정적 활용을 위한 여러 실천적 제언을 제시하였다.

Abstract ▼ AI-Helper

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

주제어

표/그림 (12)

그림 Figure 1. Comparison between discriminative and generative models (Ravichandiran, 2019)
그림 Figure 2. A process of chat bot services
그림 Figure 3. General process of natural language processing
그림 Figure 4. A comparison between perceptron and deep neural network
그림 Figure 5. A structure of the transformer model consisting of encoders and decoders (Vaswani et al., 2017)
표 Table 1. Large language models released since 2020 (Foster, 2023)
그림 Figure 6. Methods to train ChatGPT models (https://openai.com/research/instruction-following)
그림 Figure 7. A diagram of enhancement of proximal policy optimization by human feedback
표 Table 2. Examples of answers from generative AI
표 Table 3. An example of a discussion on the issue of electric vehicle subsidies with generative AI
표 Table 4. An example of a keyword extraction and feedback generation based on student's response
표 Table 5. An example of automatic scoring and feedback from students’ reponses and scientific texts

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