• 한국콘텐츠학회논문지
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• 제19권9호
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• pp.37-46
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• 2019

본 연구는 인공지능 혁신에 대한 태도에 영향을 미치는 기대와 불안요소들을 알아보고, 이들이 현재 대중들의 인식 속에서 어느 정도의 영향력을 가지는 지에 대해 확인해보고자 하였다. 본 연구는 비슷한 기술변화 문화를 공유한 세대별로 인공지능을 바라보는 태도가 다를 수 있음을 고려하여, 연구 대상을 미래 인공지능 주 소비층인 I-세대로 한정하였다. 본 연구의 결과, '업무 성과 향상', '사회 질적 향상'의 기대요인과 '인간의 사회적 가치 위협'의 불안요인을 도출하였고, 이들 요인이 각각 약한 인공지능과 강한 인공지능에 미치는 영향을 확인하였다. 또한 I-세대가 현재 약한 인공지능에는 업무 성과 향상에 대한 높은 기대와 함께 매우 긍정적인 태도를 가지는 한편, 강한 인공지능에는 약한 인공지능과 비교해 불안을 많이 느끼며 태도의 긍정성도 낮아짐을 확인하였다. 본 연구의 결과는 인공지능이 인류와 유쾌한 관계 속에서 발전하는 방향에 대한 시사점을 제시한다.

• 한국수학교육학회지시리즈A:수학교육
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• 제62권3호
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• pp.401-416
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• 2023

본 연구는 기술수용모델을 기반으로 예비교사의 자기효능감과 AI 불안이 수학 수업에서 '똑똑! 수학 탐험대'를 사용하려는 의도에 미치는 영향을 구조적으로 살펴보았다. 이를 위해 254명의 예비교사들의 자기효능감, AI 불안, 인지된 사용 용이성, 인지된 유용성, 사용 의도를 변인으로 연구모형을 설정하고 구조방정식으로 변인 간의 구조적 관계와 직·간접효과를 분석하였다. 분석 결과, 자기효능감은 인지된 사용 용이성, 인지된 유용성, 사용 의도에 유의미한 영향을 미쳤으며, AI 불안은 인지된 사용 용이성과 인지된 유용성에 유의미한 영향을 미치지 않았다. 인지된 사용 용이성은 인지된 유용성과 사용 의도에 유의미한 영향을 미쳤으며, 인지된 유용성은 사용 의도에 유의미한 영향을 미쳤다. 이러한 결과를 통해 수학수업에서 예비교사가 '똑똑! 수학 탐험대' 사용을 촉진하기 위한 시사점과 방안을 제안하였다.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.49-57
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• 2022

최근 인공지능의 다양한 활용은 기계학습의 딥 인공신경망 구조를 통해 가능해졌으며 인간과 같은 능력을 보여주고 있다. 불행하게도 딥 구조의 인공신경망은 아직 정확한 해석이 이루어지고 있지 못하고 있다. 이러한 부분은 인공지능에 대한 불안감과 거부감으로 작용하고 있다. 우리는 이러한 문제 중에서 인공신경망의 능력 부분을 해결한다. 인공신경망 구조의 크기를 계산하고, 그 인공신경망이 처리할 수 있는 데이터의 크기를 계산해 본다. 계산의 방법은 수학에서 쓰이는 군의 방법을 사용하여 데이터와 인공신경망의 크기를 군의 구조와 크기를 알 수 있는 Order를 이용하여 계산한다. 이를 통하여 인공신경망의 능력을 알 수 있으며, 인공지능에 대한 불안감을 해소할 수 있다. 수치적 실험을 통하여 데이터의 크기와 딥 인공신경망을 계산하고 이를 검증한다.

• 융합정보논문지
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• 제7권4호
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• pp.53-60
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• 2017

본 연구는 UTAUT 모델을 이용하여 보건의료분야 대학생들의 인공지능기술(Artificial Intelligence Technology, AI)의 사용 의도와 태도에 영향을 미치는 요인들을 규명하기 위해 시행되었다. 연구대상은 278명의 대학생으로, 2016년 5월 15일부터 6월 14일까지 자기기입식 설문지를 통하여 자료를 수집하였다. 연구결과 성과기대, 사회적 영향, 업무의 유용성, 불안요인이 사용 의도에 유의미한 영향을 미치는 것으로 나타났다. 그리고 성과기대, 업무의 유용성, 불안요인은 태도에 유의미한 영향을 미치는 것으로 나타났으며, 사용 의도는 태도에 영향을 미치는 것으로 나타났다. 불안요인과 업무의 유용성이 태도에 미치는 직접 효과가 사용 의도에 의해 부분 매개하는 것으로 나타났다. 대학생들의 AI 기술에 대한 긍정적인 사용 의도와 태도를 높이기 위해서는 사실에 근거한 정확한 정보전달과 막연한 불안감을 줄이면서 성과기대, 사회적 영향, 인지된 유용성을 향상시키는 것이 중요한 것으로 나타났다.

• 동의신경정신과학회지
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• 제35권3호
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• pp.231-246
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• 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)
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• 제17권11호
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• pp.2966-2986
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• 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
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• 제12권1호
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• pp.242-248
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• 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
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• 제33권4호
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• pp.1058-1092
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• 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.

• 한국IT서비스학회지
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• 제20권3호
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• pp.75-86
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• 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.

• 대한치과교정학회지
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• 제52권4호
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• pp.268-277
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• 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.