• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.12-29
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• 2019

The purpose of this study is to develop the scenario for implementation of the rule-based counseling chatbot and to verify its usefulness. For this purpose, counseling scenario for exploring problems triggering depression and establishing early counseling relationships was built and user evaluations for the rule-base counseling chatbot were conducted on 10 counseling psychologists. As a result, clear counseling structuring, appropriate use of selective and narrative responses, and active listening were identified as important components of counseling chatbot. To the next step, the usefulness of counseling chatbot developed in the previous step was examined with 78 college students. As a result, active listening was found to be an important factor in improving user satisfaction and positive mood change. In addition, through qualitative analysis of user evaluation, the unique advantages of counseling chatbot were identified and the limitations of counseling chatbot that should be overcome were identified. Based on the results of the study, the implications of the study and suggestions for future research were discussed.

• Journal of Information Technology Services
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• v.20 no.3
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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.