J Eng Teach Movie Media > Volume 24(2); 2023 > Article
Chang: The Effect of AI Chatbot-Based Tourism English Instruction on Intercultural Communicative Competence*

Abstract

The present study aimed at investigating AI chatbot-based Tourism English instruction for college students to develop their intercultural communicative competence. For the study, two distinctive models of AI based educational approach were proposed; learning with AI (LWAI) and learning about AI (LAAI). A total of 50 Tourism English 3 learners with the model of LWAI were assigned to conduct a conversation with the chatbot during the semester, while 20 Tourism English 6 learners with the model of LAAI were asked to build a hotel English chatbot. In LWAI, the collected chatbot conversation reports were analyzed in terms of task completion rates. It was found that the chatbot conversations mostly involved small talk rather than problem-solving tasks. In LAAI, Tourism English learners collected English for hotel reservations and coded data into the hotel English chatbot with the help of computer engineering students. As a result of a team project, the hotel English chatbot building was successful. Lastly, there was an increase in participants’ intercultural communicative competence in terms of respect for cultural differences and interaction attentiveness. In short, the present study showed that the AI chatbot-based Tourism English instruction had a positive effect on the development of intercultural communicative competence.

I. INTRODUCTION

Reflecting the needs of learners and society in the era of the Fourth Industrial Revolution, the 2015 revised National Curriculum in Korea was differentiated based on the 21st century core competencies. The 2015 revised English curriculum defines four curricular competencies: English communication competence, self-management competence, community competence, and knowledge information processing competence (Ministry of Education, 2017). In line with this, core educational competencies for each university are redefined and they are applied to every college course. In the case of the Tourism English course, although the basic goal of the course is to strengthen English communication skills in the field of tourism industry, it’s also encouraged for students to develop content related to knowledge information processing competence. Therefore, it is necessary to develop various teaching methods to develop knowledge information processing competence along with English communicative competence. Although various IT-based English teaching methods are used in the language classroom, the Generation Z generation, born between 1996 and 2012, who are called Digital Natives, are not only exposed to media such as the Internet, social media, and smartphones, but they are also comfortable participating in various social and personal activities based on big data and AI. A chatbot is an artificial intelligence program that provides a conversation similar to a human, recognizing text or voice commands to communicate with users (Shin, 2019). One of the characteristics of Gen-Z is that they are self-starters, they do not hesitate to learn about any topic online, and they are highly engaged in programs that can lead to certification in a short period of time (Schwieger & Ladwig, 2018). The students in this study are also Gen-Z, and in order to meet their needs, traditional teaching methods need to be changed. It is necessary to provide digital-based learning tools, not just paper-based textbooks. It’s also required to develop a flipped classroom teaching method that allows students to adjust the pace of the class, as they have a high need for self-directed learning (Lee, 2019).
According to Harding (2007), ESP learners have communication difficulties that require an active understanding of the target audience based on their knowledge of the specific field of study. Byram (1997) argued that the improvement of intercultural communicative competence can facilitate foreign language acquisition, and foreign language learners should develop the acquisition of knowledge, behavior, attitude, and skill in the target language in order to communicate interculturally. Reflecting this, foreign language instructors and researchers have conducted various studies on improving intercultural communication skills, but there are many problems in applying cultural teaching and learning to language teaching. This is because there are difficulties in defining cultural goals and methods related to language teaching (Zarate, 1986). Kramsch (1993) argued that language teachers should not only focus on linguistic codes, but also emphasize meaning. The acquisition of meaning is a process that considers language, culture, learning, and the relationship between these factors, and should involve learners in the active formation and interpretation of meaning. The American Council on the Teaching of Foreign Languages (ACTFL) introduces a variety of cultural teaching techniques, including lectures, videos of interviews with native informants and native speakers, videos of native speakers’ daily life, and reading materials. In this study, it’s focused on how to apply these various cultural teaching methods. The best way to improve intercultural communication is to live in the target language, but it is not possible for learners in all EFL contexts to acquire language while living in an Englishspeaking country. As an alternative, various teaching methods have been studied to provide Krashen’s Comprehensible Input, including various immersion class formats and classes in English. In EFL environments such as Korea, the potential of AI chatbots as an effective tool for learning and using English is highly anticipated, and it is believed that it will maximize the effectiveness of English education. Kim et al. (2019) presented the following characteristics of chatbots as an English learning tool. First, it is the closest interlocutor to practice communication in English. Second, conversations with chatbots are less burdensome. Third, the conversations with chatbot are recorded in text. Forth, chatbots are particularly strong in early conversations.
The participants of this study are Tourism majors and Tourism English course is a part of English for Specific Purposes (ESP) courses. In this study, in a course based on AI chatbot, students are encouraged to complete the tasks of small talk, commanding performance, asking information and problem solving with AI chatbot. In the process, they acquire language naturally through conversations with the chatbot and improve their intercultural communication competence. The research questions of this study are formed as follows.
1. What is the design of AI chatbot-based Tourism English course?
2. How is the chatbot conversation conducted by college students?
3. How is the effect of the AI chatbot-based Tourism English course on the development of intercultural communication?

II. Chatbot in Education

1. Types of AI Chatbot

AI chatbot conversations are conducted through text, but by fusing with other technologies, some applications have been developed to enable voice conversations (Bansal & Khan, 2018). As the technology level of chatbots has improved, they are increasingly applied to business services, such as call center consultation, e-commerce guidance, financial services, and news services, and the demand for them is rapidly expanding (Yang et al., 2019). It is also called an artificial intelligence speaker because it is released as a speaker type product, and starting from Amazon in 2014, Google, Naver, Kakao, KT, SK Telecom, LG U+, and other large companies have released smart speakers. Chatbots are implementing voice-based interactive interfaces. Some of the most popular English-based chatbots are Cleverbot, Mistuku, XiaoIce, and Lyra Virtual Assistant. Cleverbot is a chatbot developed by Rollo Carpenter that was launched on the Internet in 1997 and is still available on its website (www.cleverbot.com) as well as through an application on mobile. In 2011, it passed the turing test, proving that it can have a human-like conversation. In particular, when the users click on think about it, think for me at the bottom of the dialog, Cleverbot starts the conversation and the content is randomly generated. Mitsuku, a chatbot developed by Steve Wroswick in 2005, won the Löwner Prize in 2013, 2016, 2017, and 2018, and is considered one of the best performing chatbots in existence. A.L.I.C.E., a chatbot that uses natural language processing, uses information stored in the AIKL system to recognize and analyze keywords contained in the interlocutor’s speech and provide answers. In addition, if a user’s utterance contains multiple questions, it can handle them simultaneously. XiaoIce is an AI chatbot developed by Microsoft STCA in 2014 that can communicate using both voice and text. Developed as a social chatbot, XiaoIce has the ability to maintain a natural interaction with users by spontaneously bringing up new topics during conversations, unlike chatbots that only have task-oriented conversations. It can also understand the user’s emotions and give encouraging responses, making it like talking to a real friend. Developed by Artificial Solutions in 2013, Zo is a chatbot that can communicate via voice and text. It is a personal assistant bot that can manage schedules, alarms, and connect to Google, Wikipedia, etc. to search and provide information such as weather, maps, and news, as well as search and play music or videos. It can also act as a translator for over 70 languages, including Korean, Spanish, German, French, and more. Users can talk to it via text or voice and use it by installing the app on the smartphone. Shin (2019) explored the feasibility of using AI chatbots Mitsuku and Cleverbot in English writing activities. As more than 90% of the vocabulary spoken by both chatbots was within the top 3,000 words of the basic vocabulary of the curriculum, it was judged that there was no major problem for university students to understand them and that they were highly useful and valuable, and their respective characteristics were presented.

2. AI in Education

Learning with AI (LWAI) is categorized into system-facing AI, student-facing AI, and teacher-facing AI, and refers to AI as a role that supports the educational environment, teachers, and students (Hong et al., 2020). It is the use of AI and other technologies in education, encompassing approaches that utilize AI as a direct learning tool or learning environment, as a teaching tool, or as a learner monitoring, assessment, and grading tool. Learning about AI (LAAI) refers to AI as educational content, which is categorized into teaching AI to children and young people, technicians, and management-level personnel. This is an approach to teaching AI as educational content to cultivate the ability to design, develop, and utilize AI algorithms based on an understanding of AI. The purpose of AI education in elementary and secondary education is to prepare students for the future AI society, develop an understanding of AI, recognize the social impact of AI across various fields, and promote the ethical use of AI. AI education in elementary and secondary education covers AI basic skills and AI ethics, and based on this, education on AI and education on AI utilization should be expanded to different types of content (Holmes et al., 2019). Figure 1 shows the AI in education based on two terms; LWAI and LAAI.
Figure 2 shows an example of AI in Tourism English classes. It is a chatbot program for hotel English practice developed by inputting data collected based on big data, and can be used as a curriculum for class activities and evaluation. Although hotel English learners are not in the hotel practicum, this chatbot program allows them to practice with the customer as a hotelier. During the learning with hotel English chatbot, students learn not only English competence but also hotelier’s duties while they work for hotels. Fryer and Carpenter (2006) highlighted the advantages of using chatbots for foreign language education. Learners can feel more comfortable when talking to a chatbot than a human, that it is less boring than daily repetitive learning, and they can use texts or various speech modes to strengthen their communication skills.
In the model of LWAI, AI chatbot is used for a conversation practice, vocabulary practice, and performance evaluation in activities that perform problem-solving tasks such as flipped learning (FL), problem-based learning (PBL), and team-based learning (TBL). AI chatbot plays a role of a teacher. Traditionally, during the learner-teacher interaction, the teacher naturally leads the learner to progressively refine the utterance through negotiation of meaning. Negotiation of meaning is the process by linguistic effort to solve the problems that arise in understanding the exact content of a communication. The mechanisms of semantic negotiation include confirmation checks, clarification requests, and repetition requests (Pica & Doughty, 1985). According to Chu and Min (2019), integrating AI chatbot to English class facilitates the successful language production and it can be regarded as a tool in the progressive English learning.
In the model of LAAI, which is a combined class with the other department related to AI chatbot building, the process of learning is conducted by sharing materials between students through team teaching or operating joint projects with students from computer engineering. In this model, the chatbot, itself, is the goal of instruction, while learning and practicing Tourism English communication is regarded as a teaching medium or materials. As a team with computer engineering students, students in the Tourism English course learn the basic data collection method as a type of special lecture or they are encouraged to take the AI-related course as a prerequisite course. Figure 3 shows the data crawling code for AI chatbot. Participants in LAAI, even though they are not majoring in AI chatbot related studies, are requested to learn the basic concepts and procedure to build up AI chatbot. In 2020, the Ministry of Education in Korea announced that students will have IT based courses like coding and computer skills and smart classroom environment. Besides, the overcrowded ICT life styles and business models with 5G, Digital trading, AI, and Digital communication are facing learners and educators. The big challenge of the educational setting with AI is an inevitable change for learners, educators and researchers.

3. Intercultural Communicative Competence

As English is regarded as a global language and communication tool, the competence to effectively communicate across cultures has become increasingly vital. Developing intercultural communication competence not only enhances language proficiency but also promotes cultural understanding, empathy, and successful interactions in diverse contexts (Byram, 1997). Intercultural communication competence goes beyond language skills. It encompasses the ability to adapt language use, nonverbal communication, and cultural norms to specific cultural contexts (Hinkel, 1999). By developing this competence, English learners can effectively convey their intended messages and understand the underlying meanings within different cultural frameworks. Bennet (1993) defined that intercultural sensitivity is a world perspective that people interact with others who are from different culture. When people experience difference of culture, they follow several steps to become ethnorelativism, which means people try to deny, defense, minimize, accept, adapt and integrate with other cultures. By doing these reactions, they become a successful intercultural communicator, thus, it’s acceptable to measure intercultural communicative competence by the level of intercultural sensitivity. With advancements in communication technologies, people now have various digital platforms available to interact and express themselves. However, effective communication goes beyond technical proficiency; it requires a holistic understanding of communicative competence that encompasses linguistic, sociocultural, pragmatic, and strategic aspects (Throne et al., 2009). Communication occurs through various modes, such as text messages, emails, video calls, and social media platforms. Developing communicative competence requires the ability to navigate and utilize these multimodal communication channels effectively while considering the appropriate mode for different contexts.

III. METHOD

1. Participants

This study focuses on 70 students taking the course of Tourism English 3 (n = 50) and Tourism English 6 courses (n = 20) respectively. The students in Tourism English 3 are the third-year Tourism majors, majoring in only Tourism or double majoring in Tourism, and have taken Tourism English 1 and 2 as prerequisite courses. The total of 50 students were asked to participate in LWAI chatbot and the course has a goal to develop students’ English communicative competence in the overall tourism industry. The total of 20 students in Tourism English 6 who have taken Tourism English 1 to 5 as prerequisite courses are the fourth-year Tourism majors and the course has a goal to develop learners’ English communicative competence as a hotelier. They participated in LAAI chatbot and 20 students are divided into 5 groups. To assist students with the AI chatbot knowledge, each of the team leaders (n = 5) was asked to participate in the lab meeting of the Computer Engineering department to do a collaborative work to build the hotel English chatbot during the semester.

2. Research Procedure

According to Holmes et al. (2019), AI can be used as a tool and medium of learning, while AI itself can be a goal of instruction. In this, study, both models of LWAI and LAAI were conducted to confirm the effect of AI chatbot based Tourism English instruction. The research procedure is described in Figure 4. Firstly, AI chatbot related literatures were reviewed to find the models of AI education and teaching techniques. The proposed AI chatbot based Tourism English instruction were conducted for a semester. Then, participants’ chatbot reports, a hotel English chatbot and the increase of intercultural communicative competence were analyzed for the study.

3. Data Collection and Analysis

This study is formed with three research questions. The first one is to design the course of Tourism English learning with AI chatbot and the second one is to design the course of Tourism English learning about AI chatbot. Lastly, the increase of intercultural communitive competence is measured to investigate the effect of AI chatbot based Tourism English course. For doing this, the literature reviews of course design and AI in education were conducted and proposed the two instructional models; LWAI and LAAI. To make a conversation with AI chatbot, Cleverbot(www.cleverbot.com) was recommended to facilitate accessibility for learners. It allows learners to access the homepage on their mobile phones. A chatbot conversation was provided with several tasks during the semester, which are chatting, giving instructions, requesting information, and solving problems (Kim et al., 2019). The collected chatbot conversation reports were counted as one achievement when students complete the conversation by given task. From a total of 50 participants’ reports, 200 reports (50 per task) were counted by the number of successful conversations. The successful conversation is defined as a form of conversation which includes at least one turntaking. For research question 3, the effect of AI chatbot-based Tourism English course was verified in accordance with the intercultural sensitivity scale (ISS; Chen & Starosa, 2000). It includes 24 items and five sub-categories which are interaction engagement, respect for cultural difference, interaction confidence, interaction enjoyment and interaction attentiveness. The data was collected as a pre-and post-form of survey and t-test was conducted. Out of 50 students, 42 students were participated in both of pre-and post ISS tests. The result of the reliability test is as follows.

IV. FINDINGS AND DISCUSSIONS

1. A Design of AI Chatbot-Based Tourism English Class

The model of AI chatbot-based Tourism English course is described in Table 2. Model 1 is LWAI chatbot. Model 2 is LAAI, the interdisciplinary course of Tourism and Computer Engineering majors.
In Model 1, a chatbot is used for a conversation practice, word practice, and performance evaluation in activities that perform problem-solving tasks such as FL, PBL, and TBL. In Model 2, the interdisciplinary course of Tourism and AI chatbot-related major, computer engineering is conducted as a joint project, building up a hotel English chatbot. The following Table 3 shows the course design of Model 1, the Tourism English LWAI. In this model, AI chatbot is used as learning assistance to facilitate flipped learning of tourism English learning. In task 1, before the class, students conducted a conversation with Cleverbot, and during the class, they were asked to complete a dialog as a group work. In task 2, students tried to find the meaning of “Great Wall” and “Imperialism” by asking Cleverbot before the class, then they shared the information of China’s tourist attractions with group members during the class. In task 3, students collected the information about travel package on Cleverbot before the class, then they brought the information to share with group members during the class. In task 4, students tried to find the solution of problem before the class, and their findings were shared with group members during the class. Although students were interacting with a chatbot individually, they might have difficulty communicating in English, so the sample sentences were provided by the instructor. According to Chu and Min (2019), AI chatbot in English class is used as a tool in the progressive English learning. It plays a role as a teacher before the class.
The following Table 4 shows the course design of Model 2, the Tourism English LAAI chatbot. During the instruction, students in Tourism English 6 learned about hotel English expressions and situations in accordance with PBL. Students faced problem scenarios in the hotel reservation, food and beverage service, and housekeeping. During the period of 4th to 5th week, the hotel English learning module was provided as a type of problem-solving tasks along with LAAI chatbot. AI chatbot learning is assisted by students of computer engineering department by having a regular team project meeting. Group leaders of the Tourism English class (i.e., 5 students from each group) participated in the project meeting (i.e., a total of 4 meetings), where computer engineering students were building a hotel English chatbot. During the meeting, tourism students provided the contents of the hotel English conversation for AI chatbot building and engineering students showed the process of chatbot building to tourism students. In addition, as shown in Table 4, supplemental instructions about the chatbot building program were provided by the instructor during the Tourism English instruction. For doing this, the instructor took R-analysis course and chatbot building training program.

2. The Effect of Chatbot-Based Tourism English Instructions

1) Findings of Chatbot Conversations

The weekly chatbot conversation reports were collected from 50 students of Tourism English 3, which focuses on LWAI, and they were categorized into four tasks; small talk, commanding performance, asking for information and problem solving. The small talk to ask questions about the favorite food, acquaintance and hobbies to Cleverbot was successfully completed by 47 students (97%). The success of a chatbot conversation is counted when students and Cleverbot complete at least one turn-taking in conversations and the topics to bring up the conversations are successfully delivered. As shown in Figure 5, when user says “Good morning,” Cleverbot answers “It’s night time,” then this conversation is counted as a success since it has a turn-taking and the flow of conversation has a pragmatic point as well. Figure 5 shows the extract of chatbot conversation reports about small talk.
The next task was doing a conversation with Cleverbot by commanding performance. In the results, 31 out of 50 students (62%) complete the conversation successfully after having an instruction on how to make commanding sentences in English. Also, the instructor suggested the conversation prompts like ‘make the sentence to search for holidays with a verb.’ Most students failed to create sentences for commanding performance in every turn of conversations, thus the success of this task was counted when the first sentence was completed by the commanding words and the flow of conversation follows to commanding performance (see Figure 6).
The task to ask for information to Cleverbot was successfully completed by 18 students out of 50 (36%). Although a direct question like asking the travel package was successfully asked, the conversation flow was not meaningful and misunderstood between a student and Cleverbot. It’s accepted as a successful conversation in this study, because the task to ask for information is for students to have more chances to use words and sentences related to ask for information. The successfully counted chatbot conversations were described in Figure 7.
Finally, the task for problem-solving was successfully completed by only 4 students (8%). Figure 8 shows the extracts of problem-solving conversations with Cleverbot. In this case, the success of the conversation was counted when students make the recast questions to continue the conversation with Cleverbot. Although the flow of conversations was not naturally settled, students’ active participation in the conversation is regarded as a successful count for the problem-solving task.
In short, students’ success rates for chatbot conversations were decreased as the task was changed. Students conducted appropriate conversations of small talks rather than the problem-solving task. According to Choi and Lee (2010), factors that affect the difficulty of the task include the difficulty of the material in the task and the difficulty of the task itself, learner factors, the amount of time given to the task, and pressure to complete the task. The conversation with AI chabot is not the exception for the task difficulty. Although, students participated in a non-faced conversation as well as a non-human to human conversation, the difficulty to complete the task was similar to the faced and human to human conversations.

2) Findings of AI Chatbot Building

The course of English Tourism 6 for hotel English learning was designed to build up AI chatbot for a hotel English conversation and provided three modules; hotel reservation, food and beverage service and housekeeping for learners to practice English by solving problems to face as a hotelier. In addition, the course provided the project meeting with computer engineering students to the group leader of the class and supplemented the AI chatbot building program learning for all students. In this chatbot building project, students from the tourism department developed content for the hotel English chatbot, and students from the computer engineering department worked on the natural language analysis and inputting instructions, so that the actual chatbot could be implemented. The first module about hotel reservations was successfully programed in the Hotel English chatbot, and the other modules, food and beverage service and housekeeping were not programmed in Hotel English chatbot. It’s because students had to proceed all steps again to fill different data on chatbot building program. The success of the first module, the hotel reservation, was regarded enough to learn about AI chatbot. Figure 9 showed the Hotel English chatbot.
At the last step, students of Tourism English experienced the hotel English chatbot and conducted a conversation with it. Compared to the professional chatbot, the developed one is not sufficiently providing the various responses, but as shown in the dialog box in Figure 9, the chatbot conversation based on the saved data was successfully conducted.

3) A Development of Intercultural Communicative Competence

The effect of AI chatbot-based Tourism English instruction was verified by the Intercultural Sensitivity Scale (ISS). Although the statistically significant increase of overall mean score was not reported, the mean score of pre-tests of ISS was 62.5, while the score of post-tests was 63.6. It implies that the further comparison of subcategories is requested. Table 5 shows the details of the paired comparison of ISS. In ISS, there are five subcategories; interaction engagement, respect for cultural differences, interaction confidence, interaction enjoyment, and interaction attentiveness items. According to Table 5, the paired comparison of 2, 8, 14 and 19 shows the significant increase. The items of 2 and 8 are included in the category of respect for cultural differences and the items of 14 and 19 are included in the category of interaction attentiveness items.
The findings from the result of ISS indicate that Tourism English LWAI chatbot is effective in the development of learners’ respect for cultural differences and interaction attentiveness. According to Fryer and Carpenter (2006), a chatbot conversation has several benefits in foreign language education. They provide learners feel more comfortable, but in this study, students’ engagements items were not significantly increased after the chatbot-based Tourism English instruction for one semester. The result implies that learner motivation trumps learning medium and materials. It seems that empirical research is needed on how to use it in actual classroom-based regular English classes, how to utilize local chatbots created for learning purposes, or how to effectively utilize global and local chatbots. Also, interaction engagement, confidence and enjoyment are items that fit better with human interaction rather than AI chatbot. However, the recognition of cultural difference and interaction attentiveness are more matched with the speakers themselves and they assist them to be ready for the interaction.

V. CONCLUSION

The present study aims to investigate the AI chatbot-based Tourism English instruction in terms of intercultural communicative competence development. At first, the literature review of AI in education proposed two models of AI education, learning with LWAI and LAAI. During the semester, the Tourism English students participated in Cleverbot conversations for LWAI and a chatbot building project for LAAI respectively. For the study, students of LWAI were encouraged to complete four tasks; small talk, commanding performance, asking for information and problem-solving. As like a human to human conversation, the task difficulty has an effect on the task completion, thus the students showed the high success rates in small talks with AI chatbot, Cleverbot than problem-solving task. In the instruction of LAAI, Tourism English learners participated in the AI chatbot building project and showed the success to build the hotel English chatbot which has a conversation about a hotel reservation. Then the intercultural communicative competence of participants of LWAI was reported as a positive effect of AI education in the Tourism English instruction. It fosters human resources needed in the tourism field by strengthening intercultural communication competence and the design of competency-oriented subjects. The study suggests an alternative teaching approach by utilizing AI, which guarantees that learners increase their knowledge information processing capabilities to the field of English education. Through the design of AI-based classes for Gen-Z learners, the study proposes a model for fostering global talents through classes aimed at improving communication competence, selfmanagement competence, and information processing competence.
There are three distinctive points to be discussed. The first one is about the learner factor. Gen-Z learners in this study have characteristics that are quite different from those of instructors and many cases of heterogeneity are occurring in the classroom. In order to realize learner-centered education and cultivate human resources for the global era, the transformation of teaching, considering the characteristics of learners is regarded as a meaningful study to accept social needs in education. However, in this study, as a digital native, Gen-Z failed to complete the difficult level of task with a help of AI chatbot. It implies that transforming the teaching approach with the geek technology sounds tempting for instructors, but the more delicate approach to control the technology-based learning to be considered. The second point is about the role of a teacher. As mentioned by Chu and Min (2019), Shin (2019), and Kim et al. (2019), AI chatbot plays a role of a progressive tool to facilitate English learning, but it’s also regarded as an English teacher when the individual student works on at home. In and out of the classroom, AI chatbot can be the substitute of an English teacher to provide not only the knowledge but also the interaction with a student. Unlike the traditional multimedia-based learning material, AI chatbot can be a partner of a conversation and a facilitator of the study. Thus, in this era, the role of a teacher, a knowledge carrier, a learning facilitator could be reconsidered for the future study. Lastly, it’s necessary to set the standard to choose the rapidly changed technology-based learning materials. AI chatbot used in this study was Cleverbot, which has upgraded its capability within a year. A new version of AI chatbot is released in every minute. Thus, in order to apply AI technology to classroom-based regular English classes, it is essential to develop an AI assisted language learning model, verify its validity, and apply it. Based on this, for the further research, it is necessary to collect the empirical data from foreign language classrooms to explore the possibility of generalization and search how to effectively utilize AI technology through classroom-based action research.

FIGURE 1

AI in Education (Hong et al., 2020, p. 7)

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FIGURE 2

AI in Tourism English Classes (BCS Technology International, 2021)

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FIGURE 3

Data Crawling for R-Analysis

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FIGURE 4

Research Procedure

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FIGURE 5

Small Talk

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FIGURE 6

Commanding Performance

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FIGURE 7

Asking for Information

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FIGURE 8

Problem Solving

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FIGURE 9

Hotel English Chatbot

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TABLE 1
The Results of Reliability Test
Cronbach’s alpha
Pre Post
Intercultural sensitivity scale (ISS) .855 .831
TABLE 2
The Instructional Model of AI Chatbot-Based Learning
Model 1 (LWAI) Model 2 (LAAI)
Teaching approach Flipped learning
Problem-based learning
Team-based learning
Task-based learning
Self-directed learning
Team-based learning
Problem-based learning
Capstone design
Learning activities Do a conversation with AI chatbot weekly
Do vocabulary learning with chatbot apps
Process learning assessment with AI chatbot
Learning text mining
Learning big data algorithm
Learning how to process data
TABLE 3
A Course Design for Tourism English LWAI Chatbot
Week Task Individual task: Chatbot prompts Group task: Flipped learning
1 Task 1: Small talk Make a conversation with chatbot, What’s your full name, hometown, favorites, hobbies, best travels… Course orientation, introducing a chatbot conversation, diagnostic test, and assigning students to the group
2 Make a conversation with group members asking about their name, hometown, family, favorites, and hobbies
3 Complete a dialog about what travelers like to do most at their destination
4 Complete conversations about what travelers prefer to stay in a destination
Complete a dialog about what travelers like to eat at a destination
5 Task 2: Commanding performance Find the meaning of “Great wall.”
Find the meaning of “Imperialism.”
Find the Malaysia’s religions.
Read an English-language passage about China and summarize the content
6 Read an English-language passage about Japan and summarize the content
7 Read an English passage about Vietnam and summarize the content
Read an English passage about Malaysia/Indonesia and summarize the content
8 Midterm exam
9 Task 3: Asking in formation Search the tourist attractions in China, Japan and Vietnam. Research to develop a China travel package
10 Research to develop a Japan travel package
11 Research to develop a Vietnam travel package
12 Task 4: Problem solving Compare the package program for China, Japan and Vietnam.
Find the ways to advertise travel package.
Market a China travel package
13 Market a Japan travel package
14 Market a Vietnam travel package
15 Final exam
TABLE 4
A Course Design for Tourism English LAAI Chatbot
Week Module of hotel English learning Step for AI chatbot learning Problem based learning activities
1 Module 1: Hotel reservation Step 1: Program installing Orientation: Introducing hotel English chatbot by learning hotel English and chatbot building program (special lecture of professional of chatbot building)
2 Problem based learning:
1. Collect the data for a hotel reservation conversation in English
2. Present hotel reservation English
3
4 1. R tools: www.cran.r-project.org
2. Java jdk: https://www.oracle.com/java/technologies/javase-jdk14-downloads.html
3. R Studio: https://rstudio.com/products/rstudio/
4. KoNLP
5 Module 2: Food and beverage service Step 2: Crawling hotel English conversation data Problem based learning
1. Collect the data for food and beverage service in English
2. Present food and beverage service in English
6
7
8 1. R Studio: File Click: Data Crawling
2. Copy Hotel English websites and paste them on R Studio, R Script
3. Crawling Naver blog data/ Daum blog data
9 Midterm exam
10 Module 3: Housekeeping Step 3: Imputing data into a hotel English chatbot Problem based learning
1. Collect the data for housekeeping English
2. Present housekeeping English
11 1. Imputing data into hotel English chatbot: Java Script (see Appendix 1)
12 2. Imputing data into hotel English chatbot: Dialogflow (see Appendix 2)
13 Step 4: Working with a hotel English chatbot 3. Working with a hotel English chatbot
14
15 Final exam
TABLE 5
Paired Comparison of Intercultural Sensitivity Scale
M SD SE t df p
Pair 1 ic1 - pic1 .163 1.153 .176 .926 42 .180
Pair 2 ic2 - pic2 −.605 1.400 .213 −2.833 42 .004
Pair 3 ic3 - pic3 .070 1.223 .186 .374 42 .355
Pair 4 ic4 - pic4 −.047 1.413 .216 −.216 42 .415
Pair 5 ic5 - pic5 −.279 1.469 .224 −1.246 42 .110
Pair 6 ic6 - pic6 −.116 1.313 .200 −.581 42 .282
Pair 7 ic7 - pic7 −.326 1.629 .248 −1.311 42 .099
Pair 8 ic8 - pic8 .395 .955 .146 2.716 42 .005
Pair 9 ic9 - pic9 −.140 1.407 .215 −.650 42 .260
Pair 10 ic10 - pic10 −.023 1.354 .206 −.113 42 .455
Pair 11 ic11 - pic11 .023 .859 .131 .178 42 .430
Pair 12 ic12 - pic12 −.093 1.360 .207 −.449 42 .328
Pair 13 ic13 - pic13 .093 1.211 .185 .504 42 .309
Pair 14 ic14 - pic14 .256 1.093 .167 1.535 42 .045
Pair 15 ic15 - pic15 −.116 1.651 .252 −.462 42 .323
Pair 16 ic16 - pic16 .163 1.067 .163 1.000 42 .162
Pair 17 ic17 - pic17 −.116 1.117 .170 −.683 42 .249
Pair 18 ic18 - pic18 −.279 1.723 .263 −1.062 42 .147
Pair 19 ic19 - pic19 −.395 1.312 .200 −1.976 42 .027
Pair 20 ic20 - pic20 −.116 1.313 .200 −.581 42 .282
Pair 21 ic21 - pic21 .163 1.174 .179 .909 42 .184
Pair 22 ic22 - pic22 −.140 1.373 .209 −.666 42 .254
Pair 23 ic23 - pic23 .116 1.096 .167 .696 42 .245
Pair 24 ic24 - pic24 .023 1.354 .206 .113 42 .455

*p < .01,

**p < .05

Note 1. Interaction Engagement items are Pair 1,11, 13, 21, 22, 23, and 24, Respect for Cultural Differences items are Pair 2, 7, 8, 16, 18, and 20, Interaction Confidence items are Pair 3, 4, 5, 6, and 10, Interaction Enjoyment items are Pair 9, 12, and 15 and Interaction Attentiveness items are 14, 17, and 19.

Note 2. ic refers to pre-intercultural communication items and pic refers to post intercultural commutations items.

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Appendices

APPENDIX 1

Imputing Data on Java Script

stem-2023-24-2-15-app1-2.pdf
APPENDIX 2

Imputing Data on Dialogflow

stem-2023-24-2-15-app1-2.pdf
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