• Korean Journal of Acupuncture
• /
• v.41 no.1
• /
• pp.27-32
• /
• 2024

Objectives : This study developed an AI-based patient chatbot and examined the usability and educational effectiveness of the chatbot in the context of Korean medicine education. Methods : The patient chatbot was developed using the AI chatbot builder 'Danbee', and a total of five experts were surveyed and interviewed to determine the usability, effectiveness, advantages, disadvantages, and improvement points of the chatbot. Results : The patient chatbot was found to have high usability and educational effectiveness. The advantages of the patient chatbot were 1) it provided students with practical experience in performing clinical skills, 2) it provided instructors with assessment materials while reducing their teaching burden, and 3) it could be effectively used for horizontal and vertical integration education. The disadvantages and improvements of the patient chatbot were 1) improving the accuracy of intention inference, 2) providing students with specific instructions for problem-solving activities, and 3) providing assessment results and feedback about students' activities. Conclusions : This study is significant in that it proposes a new training method to overcome the limitations of the existing doctor-patient simulation. It is hoped that this study will stimulate further research on the improvement of students' clinical skills using artificial intelligence.

• Journal of Information Technology Services
• /
• v.23 no.5
• /
• pp.59-72
• /
• 2024

Recent advancements in IT technology and infrastructure have led to the widespread application of AI chatbots across various fields, including education, where they have shown effectiveness in improving classroom focus and achievement [1][2]. This study analyzes the importance of quality-in-use for AI chatbots in elementary Korean language learning based on ISO/IEC 25000 Quality-in-use standards, aiming to provide quality evaluation criteria for future educational chatbot development. The research methodology involved a two-tier hierarchy of 5 main characteristics and 13 sub-characteristics of quality-in-use, with surveys conducted among industry professionals and instructors after preliminary investigations. Results showed that situational adaptability, effectiveness, and efficiency were prioritized in the main characteristics. In sub-characteristics, situational completeness, learning accuracy, and flexibility were top-ranked. Instructors emphasized the importance of risk mitigation, reflecting their concern for reducing private education costs and improving learning environments. Industry professionals prioritized completeness in chatbot outputs. These findings suggest that prioritizing instructor-valued features in subject-based learning chatbots can enhance their utility and effectiveness in educational settings. The study also highlights the potential for leveraging differences in quality evaluation priorities between industry professionals and instructors in developing learning chatbots

• The Mathematical Education
• /
• v.63 no.3
• /
• pp.467-484
• /
• 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.

• Journal of Medicine and Life Science
• /
• v.21 no.3
• /
• pp.92-101
• /
• 2024

This study investigated how the use of a conversational artificial intelligence (AI) chatbot improved medical students' patient-centered communication (PCC) skills and how it affected their motivation to learn using innovative interactive tools such as AI chatbots throughout their careers. This study adopted a one-group post-test-only design to investigate the impact of AI chatbot-based learning on medical students' PCC skills, their learning motivation with AI chatbots, and their perception towards the use of AI chatbots in their learning. After a series of classroom activities, including metaverse exploration, AI chatbot-based learning activities, and classroom discussions, 43 medical students completed three surveys that measured their motivation to learn using AI tools for medical education, their perception towards the use of AI chatbots in their learning, and their self-assessment of their PCC skills. Our findings revealed significant correlations among learning motivation, PCC scores, and perception variables. Notably, the perception towards AI chatbot-based learning and AI chatbot learning motivation showed a very strong positive correlation (r=0.72), indicating that motivated students were more likely to perceive chatbots as beneficial educational tools. Additionally, a moderate correlation between motivation and self-assessed PCC skills (r=0.54) indicated that students motivated to use AI chatbots tended to rate their PCC skills more favorably. Similarly, a positive relationship (r=0.68) between students' perceptions of chatbot usage and their self-assessed PCC skills indicated that enhancing students' perceptions of AI tools could lead to better educational outcomes.

• The Journal of Korean Medicine
• /
• v.45 no.1
• /
• pp.203-214
• /
• 2024

Objectives: In the context of competency-based education emphasized in Korean Medicine, this study aimed to develop a pilot version of a CPX (Clinical Performance Examination) Practicing Chatbot utilizing large language models with prompt engineering. Methods: A standardized patient scenario was acquired from the National Institute of Korean Medicine and transformed into text format. Prompt engineering was then conducted using role prompting and few-shot prompting techniques. The GPT-4 API was employed, and a web application was created using the gradio package. An internal evaluation criterion was established for the quantitative assessment of the chatbot's performance. Results: The chatbot was implemented and evaluated based on the internal evaluation criterion. It demonstrated relatively high correctness and compliance. However, there is a need for improvement in confidentiality and naturalness. Conclusions: This study successfully piloted the CPX Practicing Chatbot, revealing the potential for developing educational models using AI technology in the field of Korean Medicine. Additionally, it identified limitations and provided insights for future developmental directions.

• Journal of Korea Multimedia Society
• /
• v.22 no.8
• /
• pp.940-949
• /
• 2019

Classroom response systems (CRS) have been proven to have positive educational effects on student engagement and participation by allowing immediate feedback to both students and instructors. We explore the use of a chatbot and slide widget-based CRS to overcome some of the challenges of existing mobile-based CRSs while retaining their advantages. Our system uses widely available instant messaging services and operates web-based slide widgets that can be seamlessly integrated into instructors' slides to visualize student feedback in various formats. The student survey results indicate that our system is as effective as conventional CRSs in promoting student engagement and participation.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.139-145
• /
• 2020

In the era of the 4th Industrial Revolution, the use of information based on AI APIs has a great influence on industry and life. In particular, the use of artificial intelligence data in the medical field will have many changes and effects on society. This paper is to study the necessary components to implement the "Cognitive Health Advisor model (CHA model)" and to implement the "CHA model using chatbot" based on this. It uses the open Cognitive chatbot to analyze and analyze the health status of users changing in their daily lives. The user's health information analyzed by the biometric sensor and chatbot consultation delivers the information to the user through the chatbot. And it implements a cognitive health advisor model that provides educational information for users' health promotion. Through this implementation, it intends to confirm the possibility of future use and to suggest research directions.

• Journal of The Korean Association For Science Education
• /
• v.44 no.3
• /
• pp.231-248
• /
• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• Journal of Information Science Theory and Practice
• /
• v.12 no.3
• /
• pp.1-13
• /
• 2024

The purpose of this study is to investigate the possibility of using chatbots as a school library educational tool. In order to achieve the purpose of the study, 116 librarian teachers first investigated the types and contents of education conducted in the school library setting and the perception of chatbots there. In addition, 15 librarians (five elementary, five middle, and five high school) were asked to complete a structured questionnaire after using Google's Bard, Microsoft's Bing, and OpenAI's Nova to find out if it is possible to use chatbots in school library education. As a result, user and reading education chatbots were found to be common in school libraries, and 99% of librarians knew about them in some detail. However, the average chatbot performance by area was 2.9 out of 5 (2.6 points being the lowest). Nevertheless, chatbots are being developed utilizing deep learning methodologies and have excellent performance, and are very effective for content-based library education through problem-solving activities.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.44-52
• /
• 2022

With recent scientific advances and growing interest in AI technologies, AI-based chatbots have been viewed as a practical learning aid for English language development. The purpose of this study is to examine preservice teachers' perceptions on the potential benefits of employing AI chatbots in English instruction and its pedagogical aspects. 28 preservice teachers majoring in English education were asked to use Kuki chatbots for a week with a guidance of a researcher and then report on their perceptions of AI chatbots in terms of perceived usefulness after use, applicability, and educational benefits and drawbacks. Emerging codes and themes were identified and evaluated using Thematic Analysis(TA) based on qualitative data from surveys and interviews. The findings show that six emerging themes were identified, encompassing perspectives on teacher, learner, communication, linguistic, affective, and assessment. The overall findings of this study revealed that AI-based chatbots can play a significant role as learning tools for stimulating interactive communication in a target language. Most preservice primary teachers acknowledge that AI chatbots can be useful as teaching and learning aids for both teachers and students. Furthermore, when applying various learner data to chatbot technology, such as learner assessment and diagnosis, a guided approach is necessary to perform a conversation appropriate for the learner's level and characteristics. Finally, as chatbots have a variety of benefits in terms of affective aspects, they may improve EFL learners' confidence in speaking English and learning motivation.