• The Mathematical Education
• /
• v.63 no.2
• /
• pp.139-163
• /
• 2024

The purpose of this study is to examine the capabilities of ChatGPT as a tool for supporting students in generating mathematical arguments that can be considered proofs. To examine this, we engaged students enrolled in a mathematics pathways course in evaluating and revising their original arguments using ChatGPT feedback. Students attempted to find and prove a method for the area of a triangle given its side lengths. Instead of directly asking students to prove a formula, we asked them to explore a method to find the area of a triangle given the lengths of its sides and justify why their methods work. Students completed these ChatGPT-embedded proving activities as class homework. To investigate the capabilities of ChatGPT as a proof tutor, we used these student homework responses as data for this study. We analyzed and compared original and revised arguments students constructed with and without ChatGPT assistance. We also analyzed student-written responses about their perspectives on mathematical proof and proving and their thoughts on using ChatGPT as a proof assistant. Our analysis shows that our participants' approaches to constructing, evaluating, and revising their arguments aligned with their perspectives on proof and proving. They saw ChatGPT's evaluations of their arguments as similar to how they usually evaluate arguments of themselves and others. Mostly, they agreed with ChatGPT's suggestions to make their original arguments more proof-like. They, therefore, revised their original arguments following ChatGPT's suggestions, focusing on improving clarity, providing additional justifications, and showing the generality of their arguments. Further investigation is needed to explore how ChatGPT can be effectively used as a tool in teaching and learning mathematical proof and proof-writing.

• Journal of Service Research and Studies
• /
• v.13 no.3
• /
• pp.172-189
• /
• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Journal of Internet Computing and Services
• /
• v.25 no.2
• /
• pp.79-92
• /
• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.21-41
• /
• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

• English Language & Literature Teaching
• /
• v.12 no.2
• /
• pp.87-105
• /
• 2006

Style is considered a pivotal construct in sociolinguistic variation studies. While previous studies have examined style in traditional forms of language such as speech, very little research has examined new and emerging styles such as computer-mediated discourse. Thus, the present study attempts to investigate style in the online communication mode of chat. In so doing, the study compares text-based online chat with speech and writing. Online chat has been previously described as a hybrid form of language that is close to speech. Here, the exact nature of online chat is elucidated by focusing on contraction use. Differential acquisition of stylistic variation is also examined according to English learning background. The empirical component consists of data from Korean speakers of English. Data is taken from a written summary, an oral interview, and a text-based online chat session. A multivariate analysis was conducted. Results indicate that online chat is indeed a hybrid form that is difficult to delineate from speech and writing. Text-based online chat shows a somewhat similar rate of contraction to speech, which confirms its hybridity.. Lastly, some implications of the study are given in terms of the learning and acquisition of style in general and in online contextual modes.

• Journal of Fashion Business
• /
• v.23 no.6
• /
• pp.16-26
• /
• 2019

The purpose of this study was to understand the effect of WeChat relational benefits on customer satisfaction and purchase intention. Due to the rapid development in the technological field, the use of WeChat has expanded drastically in recent years. In visual arts industry, WeChat business occupies a significant proportion of emerging industries. The recruited participants that were recruited via an online survey website were 335 (www.sojump.com). SPSS 22.0 statistical system was used to analyze the descriptive statistics, reliability, and validity. This study conceptualizes the positive relationship among relational benefits, customer satisfaction, and purchase intention of visual arts products. The findings indicated that WeChat users' relational benefits can be categorized into two distinct benefit types: confidence, and social benefits. The more relational benefits WeChat users perceived, the higher the level of satisfaction. In contrast, the positive influence of customer satisfaction on purchase intention is weaker in consumers with a higher level of perceived risk. The results of this study will help WeChat business to retain its customers, thus, gaining a long-term value for visual arts industry. Theoretical and managerial implication for WeChat business are provided.

• Journal of Distribution Science
• /
• v.21 no.8
• /
• pp.83-96
• /
• 2023

Purpose: This study examined the relationship between the factors (Credibility, Usability) and user Innovativeness of the ChatGPT on TRA (Theory of Reasoned Action; Subjective Norm, Attitude) and Switching Intention. TRA and Innovation Diffusion Theory (IDT) were used. Research design, data and methodology: From April 26 to 27, 2023, an online panel survey agency was commissioned to conduct a survey of GhatGPT users in their 20s and 40s in Korea, and a total of 210 people were used for the final analysis. Verification of the research model was performed using SPSS and AMOS. Results: First, ChatGPT factors (Credibility, Usability) were found to have positive effects on TRA (Subjective Norm, Attitude). Second, ChatGPT user Innovativeness was found to have a positive effect on TRA (Subjective Norm, Attitude). Third, ChatGPT users' TRA (Subjective Norm, Attitude) were found to have positive effects on Switching Intention. Conclusions: These results mean that the superior Usability and Credibility of ChatGPT and the Innovativeness of users have a significant effect on the Switching Intention from existing Portal Service (Naver, Google, Daum, etc.) to ChatGPT. Generative AI such as ChatGPT should strive to develop various services such as improving the convenience of functions so that innovative users can use them easily and conveniently in order to provide services that meet expectations.

• The Journal of Information Systems
• /
• v.33 no.1
• /
• pp.91-114
• /
• 2024

Purpose The purpose of this study is to empirically investigate the factors that influence users' continuous intention to use ChatGPT based on the Expectation Confirmation Model(ECM). Drawing from the literature, this study identifies anthropomorphism and trust as key characteristics of generative AI and ChatGPT. Design/methodology/approach The research model was developed based on ECM and literature research to investigate the impacts of anthropomorphism and trust on continuous intention of using ChatGPT. In order to test the hypothese, a total of 193 questionnaires were collected and analyzed for the structural equation modeling with SmartPLS 4.0. Findings The study's findings show that all proposed hypotheses were supported, suggesting that the ECM is a valid framework for examining continuous intention of using ChatGPT. Moreover, the study stressed the crucial role of anthropomorphism in the model, showing the positive impact on expectation confirmation, perceived usefulness, and trust in ChatGPT. Also, trust positively affects perceived usefulness. These findings provide valuable insights for enhancing user satisfaction and continuous usage intention, serving as a foundation for development strategies for ChatGPT and similar AI-based systems.

• Communications of Mathematical Education
• /
• v.37 no.2
• /
• pp.233-256
• /
• 2023

This study conducted foundational research to derive ways to use ChatGPT in mathematics education by analyzing ChatGPT's responses to questions from the National Assessment of Educational Achievement (NAEA) and the College Scholastic Ability Test (CSAT). ChatGPT, a generative artificial intelligence model, has gained attention in various fields, and there is a growing demand for its use in education as the number of users rapidly increases. To the best of our knowledge, there are very few reported cases of educational studies utilizing ChatGPT. In this study, we analyzed ChatGPT 3.5 responses to questions from the three-year National Assessment of Educational Achievement and the College Scholastic Ability Test, categorizing them based on the percentage of correct answers, the accuracy of the solution process, and types of errors. The correct answer rates for ChatGPT in the National Assessment of Educational Achievement and the College Scholastic Ability Test questions were 37.1% and 15.97%, respectively. The accuracy of ChatGPT's solution process was calculated as 3.44 for the National Assessment of Educational Achievement and 2.49 for the College Scholastic Ability Test. Errors in solving math problems with ChatGPT were classified into procedural and functional errors. Procedural errors referred to mistakes in connecting expressions to the next step or in calculations, while functional errors were related to how ChatGPT recognized, judged, and outputted text. This analysis suggests that relying solely on the percentage of correct answers should not be the criterion for assessing ChatGPT's mathematical performance, but rather a combination of the accuracy of the solution process and types of errors should be considered.

• Therapeutic Science for Rehabilitation
• /
• v.13 no.1
• /
• pp.65-74
• /
• 2024

Objective : This study assessed ChatGPT, an artificial intelligence system based on a large language model, for its ability to pass the National Korean Occupational Therapy Licensure Examination (NKOTLE). Methods : Using NKOTLE questions from 2018 to 2022, provided by the Korea Health and Medical Personnel Examination Institute, this study employed English prompts to determine the accuracy of ChatGPT in providing correct answers. Two researchers independently conducted the entire process, and the average accuracy of both researchers was used to determine whether ChatGPT passed over the 5-year period. The degree of agreement between ChatGPT answers of the two researchers was assessed. Results : ChatGPT passed the 2020 examination but failed to pass the other 4 years' examination. Specifically, its accuracy in questions related to medical regulations ranged from 25% to 57%, whereas its accuracy in other questions exceeded 60%. ChatGPT exhibited a strong agreement between researchers, except for medical regulation questions, and this agreement was significantly correlated with accuracy. Conclusion : There are still limitations to the application of ChatGPT to answer questions influenced by language or culture. Future studies should explore its potential as an educational tool for students majoring in occupational therapy through optimized prompts and continuous learning from the data.