본 연구는 생성형 인공지능인 챗봇을 활용해서 핵심광물을 탐구하는 과정에서 나타나는 챗봇과 학생의 반응, 그 들 사이에서 일어나는 상호작용을 인식론적 측면에서 분석했다. 그 결과를 바탕으로 인공지능을 활용한 교수·학습 과정 에서 유의해야 할 문제들을 교사의 역할, 교육의 목표, 지식의 속성 측면에서 논의했다. 이 연구를 위해 고등학생 19명 을 대상으로 챗봇을 활용한 3차시 과학 교육 프로그램을 진행했고, 학생들이 작성한 보고서를 분석했다. 그 결과, 학생 의 질문은 형식적 측면에서 검색형 질문과 탈검색형 질문이 나타났고 내용적 측면에서는 대상에 대한 특성을 묻는 다 양한 질문 외에도 여러 자료를 종합해서 판단할 것을 요구하는 질문도 나타났다. 대체로 학생들은 지향해야할 것과 지 양해야 할 것을 구분한 질문 전략을 갖추고 있었다. 챗봇의 답변은 일정한 형태-서문, 본문, 결문 등의 3부분으로 이루 어져 있었고, 특히 결문에는 내용에 대한 의견을 곁들인 논평이나 의견 등이 포함되어 있어서 여기에는 가치 판단과 함께 과학의 본성이 나타났다. 챗봇과 학생의 상호작용은 학생이 챗봇의 답변에 대한 질문을 조직하는 과정에서 잘 드 러났다. 답변 근거 여부에 따라 독립형, 파생형 질문으로, 포괄성 수준의 변화에 따라 상위형, 하위형, 병렬형 질문이 나타나기도 했다. 학생들은 챗봇의 답변에 비판적 사고기술이 포함된 질문으로 반응하기도 했다. 이러한 결과를 바탕으 로 챗봇과 학생 사이에는 교사와 상호작용하는 일반적 수업과 달리 ‘제한된 상호작용’이라는 태생적 한계가 있음을 발 견하고 이를 보완할 교사의 역할을 논의했고, 아울러 AI를 활용한 학습의 목표 및 이들이 제공하는 지식의 속성을 함 께 논의했다.

This study aims to investigate the effects of using AI chatbots in Korean English education from a macro perspective. For this purpose, 19 experimental studies are selected to conduct a meta-analysis, synthesizing the results of 51 individual study cases. The results of this study are as follows: First, it is found that the overall effect size of using chatbots is more than medium size meaning that a chatbot is an effective tool to learn English. Second, in the aspects of linguistic competence and affective categories, each shows over medium sizes like the overall effect size. In details of the dependent variables, vocabulary and speaking in linguistic competence and motivation in affective categories, large effect sizes are shown. Third, the effect sizes are getting larger, as the younger the students are, the longer the experiment period lasts, and the more purpose-built the chatbot is. But the differences in the effect sizes in terms of these moderators (e.g., school level, experiment period, and chatbot type) are not significant. Lastly, it is suggested that follow-up studies are needed to collect a sufficient number of experimental study cases and subdivide the variables for performing a more detailed meta-anlysis.

We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).

We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).

The following is not a conversation with a bank clerk. " Instead, let me introduce you to customized credit loans," "Do you want me to connect you to the screen of using COVID-19 support funds and checking balance?" These are the contents of consultations with AI chatbots at financial institutions. Chatbot, which used to be an additional tool for adding convenience to life, is now at the center of our lives.

The use of AI chatbots in frontline customer service is beneficial as it can provide quick service responses, cost-saving on human employees and accelerate customers’ decision-making process. However, implementing chatbots can be a double-edged sword. On the one hand, companies benefit from the use of chatbots. On the other hand, it may hurt customer experience as customers perceive chatbots are less trustworthy and show less social presence. Service failures today have become more unpredictable with the increasing complexity of social environments. Aligning with the trends of online customer service, customers are most likely to encounter a chatbot when seeking online customer service to solve service failures. With most of the previous literature investigating customers’ perceptions of chatbots and chatbot-related service failures, little research has focused on the area where chatbots as service recovery agents and how customers perceive the use of chatbots handling their service requests after service failures.

In response to the increasing deployment of brand chatbots in the service sector, this study developed a conceptual framework to examine the psychological processes through which brand chatbots contribute to relationship building efforts. A 2 (interactivity: high vs. low) X 2 (anthropomorphic conversation style: warm vs. competent) between-subject design was conducted in the context of the coffee service business. The levels of interactivity in the chatbots were operationalized by altering the subdimensions of interactivity: modality (i.e., media richness, response latency), message (i.e., interdependency in message exchanges), and source (i.e., customization of content flow). Different linguistic elements (e.g., terms of address, vocabulary, punctuation, emoticons) were used to construct two sets of scripts that emphasized either warmth or competence of the chatbots. Based on the results of the pretests, four brand chatbots via Facebook Messenger were developed using the platform provided by GoSky AI Inc.

Purpose - The purpose of this study was to explore the impact of chatbots’ innovation attributes on the innovation acceptance for consumers who have used chatbots to purchase fashion products that account for a large share of transactions in mobile shopping. Research design, data, and methodology – Data were collected from Korean consumers aged 20 to 49 who had experience using chatbots when purchasing fashion-related products via mobile circumstances. After a pilot survey of 31 customers, pre-questionnaire was revised for the final test, and the final questionnaire was distributed to 1,500 subjects. Out of these, 244 were retrieved. After excluding 48 inappropriate responses, 196 were used for statistical analysis. Frequency analysis, exploratory factor analysis, one-way ANOVA, regression analysis and independent t-test using SPSS 23.0 were employed for data analyses. Results - First, four factors of chatbots’ attributes were extracted: relative advantages and compatibility, complexity, sensibility, and diversity. Second, two factors were extracted for fashion leadership: fashion opinion leadership and fashion innovativeness. Two groups based on the fashion leadership were identified: active innovation adopters and passive innovation adopters. Third, relative advantages and compatibility, diversity, sensibility of innovation attributes were found to have effects on the innovation acceptance in order. Fourth, significant differences were found in sensibility of innovation attributes and innovation acceptance in groups by marital status and age. The married in their 30s and 40s perceived sensibility as a more important attribute of chatbots than the unmarried in their twenties. Among the groups of different income levels, meaningful differences were found in diversity of innovation attributes and innovation acceptance. Fifth, there were significant differences found in relative advantages and compatibility, sensibility of innovation attributes, and acceptance of Innovation among the groups by fashion leadership. Active innovation adopters were found to be more aware of the importance of relative advantages and compatibility, and sensibility of innovation attributes, and innovation acceptance. Conclusions – The present study provides chatbots’ marketing strategies for fashion items need to be modified by demographic characteristics and fashion leadership. Particularly, fashion leadership was found to be an important factor in determining the perception of innovation attribute as well as innovation acceptance.

본 연구는 최근 주목을 받고 있는 AI 기기를 영어 교육에 활용하는 방안의 하나로 대표적인 AI 챗봇(chatbot)인 Mitsuku와 Cleverbot의 활용 가능성을 탐색하였다. 이를 위해 7단계로 구성된 다양한 과업을 제시하고 27명의 대학생을 대상으로 이들 챗봇과 채팅을 수행하도록 하였다. 그 결과, 먼저 두 챗봇 모두 대화에서 구사하는 어휘의 90% 이상이 상위 3,000단어 이내에 포함되어 대화 시 학습자들이 챗봇의 표현을 이해하는데 큰 문제가 없었다. Mitsuku와 Cleverbot의 기능 비교에서는 실제 대화의 양상과 설문 조사를 바탕으로 볼 때 Mitsuku에 대한 평가가 훨씬 긍정적이었다. Mitsuku는 맥락 파악에 다소 한계를 보이기는 했지만 대화 구사에 큰 무리가 없었고 Mitsuku 자체의 지식 데이터베이스를 뛰어넘는 내용에 대해서는 웹검색을 바탕으로 정보를 제공할 수 있다는 점에서 Cleverbot에 비해 높은 만족도를 보였다. 본 연구에서 나타난 챗봇의 한계에도 불구하고 실험에 참여한 대부분의 학습자들은 영어 구사의 기회와 영어 입력을 제공한다는 점, 그리고 무엇 보다도 인간 대화와는 달리 부담 없이 어떠한 민감한 대화 주제도 꺼낼 수 있고 채팅 시 나타날 수 있는 영어 오류에 대해 신경이 덜 쓰인다는 점을 챗봇 활용의 장점으로 꼽았다. 끝으로 본 연구에서는 그 밖의 시사점과 후속 연구에 대한 제언을 제시하였다.