인공지능 기반 심리상담에 활용되는 딥러닝 기법 고찰

Abstract

Objectives This study aims to review and summarize deep learning techniques that have been developed to date and to discuss how they can be applied in practice to artificial intelligence-based psychology counseling robots.
 Methods To examine the latest deep learning techniques utilized in artificial intelligence psychological counseling, an extensive literature search was conducted for research papers and relevant books from both domestic and in-ternational sources in the fields of emotion analysis, facial expression generation, and text generation.. For do-mestic literature, databases such as RISS and KISS were utilized, and for international literature, research paper search sites including ERIC (ProQuest), JSTOR, and Google Scholar were employed.
 Results When applying artificial intelligence in psychological counseling, the prominent technology of interest in-volves recognizing emotions from the interlocutor's facial expressions and generating similar expressions, or rec-ognizing emotions from sentences spoken by the interlocutor and generating responsive sentences. The study in-troduced the concepts of machine learning and deep learning and explained the workings of deep learning techni-ques, such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), Transformer, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and GAN (Generative Adversarial Network). These techniques are founded on the princi-ples derived from human brain functioning. Particularly, emotion recognition programs through image analysis have predominantly relied on CNN, while research in the realm of language has advanced with the utilization of GPT-2, LSTM, and similar methods.
 Conclusions Among the existing deep learning techniques, the recognition of emotions from the interlocutor's fa-cial expressions and the generation of similar expressions, or the recognition of emotions from sentences spoken by the interlocutor and the generation of responsive sentences, are the technologies predominantly under scrutiny for their application in artificial intelligence-based psychological counseling. Nevertheless, rule-based chatbots continue to dominate the landscape. This paper explored the merits and demerits of AI-based counseling em-ploying such deep learning techniques. Looking ahead, there is a need for the advancement of technology that en-ables natural interactions, coupled with the essential verification of its effectiveness.