• The Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.37-46
• /
• 2019

Humans have anxieties as well as expectations for artificial intelligence. This study attempted to identify the expectation and anxiety factors affecting the attitude toward artificial intelligence innovation and to ascertain how much influence they have on current artificial intelligence innovation. This study considered that attitudes toward artificial intelligence may be different for each generation sharing a similar technology change culture. Therefore, the researchers limited the research subjects to I generation, which is the main users of artificial intelligence in the future. As a main result, the factors of expectiation of 'performance gain', 'positive social impact', and the factor of anxiety of 'threat to human-oriented social value' were drawn, and these factors influenced weak and strong artificial intelligence respectively. The results of this study suggests that artificial intelligence should develop into a pleasant relationship with humankind.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.401-416
• /
• 2023

This study systematically examined the influence of preservice teachers' self-efficacy and AI anxiety, on the intention to use AI programs 'knock-knock! mathematics expedition' in mathematics lessons based on a technology acceptance model. The research model was established with variables including self-efficacy, AI anxiety, perceived ease of use, perceived usefulness, and intention of use from 254 pre-service teachers. The structural relationships and direct and indirect effects between these variables were examined through structural equation modeling. The results indicated that self-efficacy significantly affected perceived ease of use, perceived usefulness, and intention to use. In contrast, AI anxiety did not significantly influence perceived ease of use and perceived usefulness. Perceived ease of use significantly affected perceived usefulness and intention to use and perceived usefulness significantly affected intention to use. The findings offer insights and strategies for encouraging the use of 'knock-knock! mathematics expedition' by preservice teachers in mathematics lessons.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.49-57
• /
• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.

• Journal of Convergence for Information Technology
• /
• v.7 no.4
• /
• pp.53-60
• /
• 2017

The purpose of this study was to identify the factors affecting intention and attitude of artificial intelligence technology(AI) of university students in healthcare using UTAUT model. Participants were 278 college students and the data were collected through self-reported questionnaire from May 15 to June 14, 2016. The collected data were analyzed using PASW Statistics/AMOS 22.0. The results were as follows. The effect of expectation factor, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology Intention. Factor of expectation effect, social influence, usefulness of work, anxiety factor had a significant effect on use of AI technology. As a result of verifying the significance of the indirect effect, it can be seen that the direct effect of the anxiety factor on the attitude factor is partially mediated by the use intention factor and the intention to use was partially mediated in the direct effect of the usefulness factor of the task on the attitude factor. This result means that it is important to increase the expectation factors, social effects, and perceived usefulness through accurate information based on facts and to reduce vague anxiety in order to increase the positive intention and attitude of university students' use of AI technology.

• Journal of Oriental Neuropsychiatry
• /
• v.35 no.3
• /
• pp.231-246
• /
• 2024

Objectives: To build basic clinical data for developing an artificial intelligence algorithm for Korean herbal prescriptions for anxiety, depression, anger, and insomnia. Methods: Subjects were recruited among those who reported mild or more severe symptoms of anxiety, depression, anger, and insomnia (Anxiety: State-Trait Anxiety Inventory≥40, Depression: Beck Depression Inventory≥14, Anger: State-Trait Anxiety Inventory≥16, Insomnia: Insomnia Severity Index≥8). Clinical observation items including basic medical information and symptoms were collected from them. These data were then analyzed by experts in Hyungsang medicine, Sasang constitutional medicine, and Sanghan-Geumgwe medicine. Results and Conclusions: Experts of the three societies presented key clinical data and recommended prescriptions. Results of this study can be used as basic data for developing an artificial intelligence algorithm for Korean herbal prescriptions in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.2966-2986
• /
• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• International Journal of Advanced Culture Technology
• /
• v.12 no.1
• /
• pp.242-248
• /
• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Asia pacific journal of information systems
• /
• v.33 no.4
• /
• pp.1058-1092
• /
• 2023

This study investigates the perception and adoption of ChatGPT (a large language model (LLM)-based chatbot created by OpenAI) among Korean users and assesses its potential as the next disruptive innovation. Drawing on previous literature, the study proposes perceived intelligence and perceived anthropomorphism as key differentiating factors of ChatGPT from earlier AI-based chatbots. Four individual motives (i.e., perceived usefulness, ease of use, enjoyment, and trust) and two societal motives (social influence and AI anxiety) were identified as antecedents of ChatGPT acceptance. A survey was conducted within two Korean online communities related to artificial intelligence, the findings of which confirm that ChatGPT is being used for both utilitarian and hedonic purposes, and that perceived usefulness and enjoyment positively impact the behavioral intention to adopt the chatbot. However, unlike prior expectations, perceived ease-of-use was not shown to exert significant influence on behavioral intention. Moreover, trust was not found to be a significant influencer to behavioral intention, and while social influence played a substantial role in adoption intention and perceived usefulness, AI anxiety did not show a significant effect. The study confirmed that perceived intelligence and perceived anthropomorphism are constructs that influence the individual factors that influence behavioral intention to adopt and highlights the need for future research to deconstruct and explore the factors that make ChatGPT "enjoyable" and "easy to use" and to better understand its potential as a disruptive technology. Service developers and LLM providers are advised to design user-centric applications, focus on user-friendliness, acknowledge that building trust takes time, and recognize the role of social influence in adoption.

• Journal of Information Technology Services
• /
• v.20 no.3
• /
• pp.75-86
• /
• 2021

As artificial intelligence is actively studied, chatbot systems are being applied to various fields. In particular, many chatbot systems for psychological counseling have been studied that can comfort modern people. However, while most psychological counseling chatbots are studied as rule-base and deep learning-based chatbots, there are large limitations for each chatbot. To overcome the limitations of psychological counseling using such chatbots, we proposes a novel psychological counseling AI chatbot system. The proposed system consists of a GPT-2 model that generates output sentence for Korean input sentences and an Electra model that serves as sentiment analysis and anxiety cause classification, which can be provided with psychological tests and collective intelligence functions. At the same time as deep learning-based chatbots and conversations take place, sentiment analysis of input sentences simultaneously recognizes user's emotions and presents psychological tests and collective intelligence solutions to solve the limitations of psychological counseling that can only be done with chatbots. Since the role of sentiment analysis and anxiety cause classification, which are the links of each function, is important for the progression of the proposed system, we experiment the performance of those parts. We verify the novelty and accuracy of the proposed system. It also shows that the AI chatbot system can perform counseling excellently.

• The korean journal of orthodontics
• /
• v.52 no.4
• /
• pp.268-277
• /
• 2022

Objective: Poor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment. Methods: In total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs. Results: The predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments. Conclusions: The constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.