ISSN 2187-4123

実用英語教育学会紀要

SPELT JOURNAL
第 13 号

The Society of Practical English Language
Teaching
2024

3

実用英語教育学会

目次
巻頭言

実用英語教育学会 会長

釣 晴彦…………………………………………………………………… 2

President’s
Message

The Society of Practical English
Language Teaching, President

TSURI, Haruhiko[ 実践論文 ]Using Artificial Intelligence to Improve Students’ English Proficiency:
Fostering Learner Autonomy Among Japanese University Students ………………………………………… 3
北海道科学大学

三浦 寛子

Hokkaido University of Science

MIURA, Hiroko

北海道科学大学

小野 祥康

Hokkaido University of Science

ONO, Yoshiyasu[ 投稿規程 ]……………………………………………………………………………………………………………………………………………………………… 161

巻頭言
SPELT JOURNAL 第 13 号の発刊に寄せて
実用英語教育学会の第13号研究紀要が発行された。
今回は本紀要に1本の研究論文が寄稿された。
題は「Using Artificial Intelligence to Improve Students’English Proficiency: Fostering Learner Autonomy
Among Japanese University Students」である。この論文は、生成系 AI の今までの変遷をたどり、日本の大学生に会話型 AI アプリケーションを活用しての学習の成果を調査し検証した内容である。
Small GPTalk からのフィードバックは大変興味深いデータである。学習者はそのツールを使用して学習の面白さを理解すると持続的な学習の必要性が高まり、学習者としての自律性が高まるのではないかと期待される。今回は、被験者となった大学生の人数が制約上少なかったが、検証した結果、
このツールがきっかけとなって自律的な学習者になるという期待は裏付けられなかった。恐らく今後生成系 AI を活用した研究が未来を見据えた教育・研究として、沢山発表されていくだろう。今回のこの研究論文は、先行研究となってさらに研究を継続していくことに繋がることを願っている。
実用英語教育学会の第 12 回研究大会で青山学院大学名誉教授の小張敬之氏が「Web3.0/Society5.0/
メタバース時代の英語教育」の題で、英語をコミュニケーションの手段として、AI/ICT を利用した実践教育の講演を思い出した。生成系 AI や ICT の技術を用いて英語力を鍛え、ものの見方や考え方を幅広くし、自らの世界観を持たせることがこれからの社会になると、今後の生成系 AI の活用が予言されていた。講話の内容が非常に印象深く、今でも記憶に残っている。
生成系AIの急速な進歩は2022年11月下旬に公表され世界中が注目したChatGPTが代表である。
その利用方法は、企業や大学をはじめとする多くの教育現場で話題を呼び、様々な影響を及ぼしている。ChatGPT は、研究活動に多くの利点をもたらす一方で、一部のタスクには適していないという点も指摘されている。しかし生成系 AI の進歩は今ものすごい勢いで進んでいる。また同時に高校や大学で ChatGPT の使用にあたっての注意書が出されていることも事実である。これだけインターネットが普及した社会では、もはやインターネットを禁止すると規制してもナンセンスである。
変化のスピードが特に速いデジタル技術の使い手を増やし、新しいスキルを習得する「リスキリン
グ」がビジネスの中でも大規模に行われている。まさに教育界が一番遅れているかも知れない。生成系 AI で個別化教育ができ自己肯定感を上げることができるツールとしての活用が可能な時代になるのがすぐ近くにあると思う。
実用英語教育学会は、これからもさらなる研究と実践の蓄積をしていく所存である。小学校、中学校、高校、大学と連携を常に密にして一層発展させていきたい。研究論文や実践報告を是非積極的に応募していただければありがたい。ご意見を含めて皆さんのご支援ご協力を今後も宜しくお願いしたい。
実用英語教育学会会長

釣 晴彦

北海道文教大学教授

2

実用英語教育学会紀要 第 13 号 2024.03〔実践論文〕SPELT JOURNAL, No.13 (March 2024)
ISSN 2187-4123

Using Artificial Intelligence to Improve Students’ English Proficiency:
Fostering Learner Autonomy Among Japanese University Students
Hiroko MIURA, Yoshiyasu ONO(Hokkaido University of Science)
Abstract
Over the past decade, the concerns regarding artificial intelligence (AI) have grown rapidly.
Although an increasing number of studies have been conducted on language classrooms, noclear guidelines exist regarding their use. This study examined the effects of conversational AI
applications on Japanese university students. After a discussion of the history of AI and its use ineducation, the necessity of AI for non-native English speakers is demonstrated in two studies.
Previous research has indicated that conversational agents encourage students to interact withartificial intelligence and promote learner autonomy. Following the SmallGPTalk presentation,
the methodology used to conduct surveys among university students in this study was elucidated.
Although the quantitative survey found no significant differences, many students stated that theapplication helped them improve their English skills, particularly regarding proficiency andgrammar, in a safe environment that allowed them to make mistakes. In contrast, lengthy
messages from artificial intelligence instructors discouraged them from using the application
outside the classroom, and the students expected sympathy from artificial intelligence. Contrary
to the expectations, the students’ enjoyment in using the agent did not enhance their autonomy.
Future research should explore methods to help students recognize their improvements throughthe use of conversational agents.

1. Introduction
Artificial intelligence (AI), which automatically proved a mathematical theorem, was first
introduced in the United States in 1956 (Arai, 2018). Extensive research has since been
conducted on this topic. The first AI boom occurred between the late 1950s and the 1960s, and asecond boom occurred during the 1980s. During this time, people tried to create an expert
3system specialized for a specific field; for example, a system that had obtained ample
information on rules and laws and was expected to play the role of a lawyer. However, thedifficulties this entails in quantifying ambiguous things are challenging. The Internet has
expanded rapidly since search engines first appeared in the mid-1990s, and massive amounts ofdata have accumulated as web resources, which provides examples from which computers learn
patterns to make independent decisions. This is called machine learning, which led to the third
AI boom in mid-2010. Deep learning, a type of machine learning, has further expanded thisboom.
Currently, AI is one of the most popular research topics. Many people worldwide havealready used voice assistants, such as Apple’s Siri, Amazon’s Alexa, and Google Assistant.
Automated cars are also being rapidly developed, and the Japan National Police Agency (n.d.)
has allowed such vehicles to be tested in public road demonstration experiments, as these cars
are expected to reduce accidents and alleviate traffic congestion. AI can learn from enormous
amounts of data, whereas traditional robots can only work based on programs set up in advance.
Consequently, drastic changes in AI, including language-related changes, have been observed inmany fields.
Chen et al. (2020) analyzed AI-powered education (AIEd) literature from 1999 to 2019 andfound that the number of AIEd studies rapidly increased between 2012 and 2019. In theliterature, the frequently used keywords were “education,” “machine learning,” “robotics,”“artificial intelligence,” and “deep learning.” These AI-related words have been used in andinfluenced educational institutions throughout the previous decade of the 21st century.
Huang et al. (2023) investigated 4,519 empirical papers focusing on AI technologies tosupport learning and teaching, and found that AI is frequently used to assist students in writing,reading, vocabulary, grammar, speaking, and listening. According to their research, the mostpopular topics are automated writing evaluation, intelligent tutoring systems for reading andwriting, automated error detection, computer-mediated communication, natural language
learning, and vocabulary learning (Huang et al., 2023).
2. Literature Review
Universities and educational institutions are not yet ready to issue guidelines on the use of AI,
and view generative AI and ChatGPT as threats to students’ development of critical thinking,writing abilities (Hong, 2023), creativity, and problem-solving skills (Kasneci et al., 2023).
However, some individuals have welcomed generative AI. This chapter discusses two papers on
the use of AI by non-native English speakers.
2.1 AI Use for Non-Native English Speakers in Science
Amano et al. (2023) highlighted the severity of language barriers that non-native English
speakers face in science and urged the appropriate use of AI tools to reduce them. They
conducted online research on 908 environmental science researchers from eight countries who
4had published at least one first-authored peer-reviewed paper in English, and found that 95
percent of these non-native English speakers needed help in conducting and communicating
science in English.
Amano et al. (2023) state that certain points should not be ignored. First, non-native English
speakers need more time to read and write English-language papers than to perform the sametasks in their first language. Second, they require more effort than native speakers to proofread
their papers in English. Third, non-native English speakers are more likely to have their papers
rejected by journals because of their English writing skills. The frequency of language-related
paper rejection among non-native English speakers was 2.5–2.6 times higher than that among
native English speakers. Fourth, 30 percent of early-career (those who have published five or
fewer English papers) non-native English speakers of high-income nationalities, such as
Japanese and Spanish, often or invariably refrained from participating in English language
conferences due to language barriers. In addition, about half of the early-career non-native
English speakers of high-income nationalities often or always avoided oral presentations for theabovementioned reasons. Non-native English speakers must spend much more time preparing
the presentations in English than do their native English counterparts. However, this tendency
does not hold for first-language presentations.
After the authors identified these disadvantages, they pointed out that the scientificproductivity of non-native English speakers was undoubtedly much lower than that of native
English speakers and noted that these disadvantages led to a tremendous inequality between
native and non-native English speakers in the development of their scientific careers by
imposing an inevitable burden on non-native English scientists.
These disadvantages may be frustrating because of the additional time, cost, effort, and lost
opportunities due to language barriers. Therefore, they insist that AI is important for non-native
English scientists as well as beneficial to the careers of Japanese university students by helping
them transform themselves into global players and gain treatment equal to that of native English
speakers in the same field in the future if they are familiar with AI.
2.2 Use of Machine Translation for Japanese Graduate Students
Fukunaga and Yip (2023) studied the use of machine translation (MT) by 39 first-year
Japanese graduate students in Science, Technology, Engineering, and Mathematics (STEM). All
students had experience in using MT tools, such as DeepL, Google Translate, and Weblio
Translate. Many reported that they copied and pasted sentences from MT, as it reduced errors
and created sentences that the students found difficult to express owing to their limited
proficiency. However, while they noticed that MT was helpful, they needed clarification
regarding the accuracy of the MT output. Fukunaga and Yip (2023) revealed that students’ lackof strategies for effective MT use negatively impacted the improvements in their skills generated
by the technology's translation quality and knowledge construction.
The authors concluded that Japanese universities must have clear guidelines for ethical MTuse, and EFL/ESP instructors must consider how to integrate MT into the school policy
5

curriculum and student goals (Fukunaga & Yip, 2023).
Because students have already used AI with MT, they possess the necessary competence, so
it is essential that they understand and be aware of the issues that they may encounter. This isparticularly important when AI is used in educational institutions because it is quite likely thatthey will use the technology in their studies without knowing the risks.
2.3 Natural Language Processing
Natural language processing (NLP) is the ability of a computer program to manipulate
spoken and written languages, and it is applied by Siri, Alexa, and Google Assistant, as
mentioned above. NLP can immediately transcribe discussions in meetings or conversations
between the staff at call centers and customers. Incorporating ChatGPT into a language-learning
environment provides many opportunities to explore, enhance, and personalize student learning
experiences. One study showed that students can practice and improve their conversation skills
by interacting with AI (Ayedoun et al., 2019), allowing students to learn the target language bycommunicating with the AI at their own pace.
The effectiveness of conversational agents, such as chatbots in promoting students’ learning
success has been analyzed in several studies. Tran et al. (2019) used a Facebook auto-messenger
in a conversational activity to teach prepositions. The participants were 100 students who werelearning prepositions with a supplied paper and their teacher’s lecture (control group), while
another 100 were learning prepositions with the help of a Facebook chatbot (experimentalgroup). Students in the experimental group enjoyed a new way of learning and interacted withthe chatbot even outside of class meetings. The authors reported that the target grammar items
were best understood when students were afforded autonomy (Tran et al., 2019).
Another study was conducted with 176 undergraduates in Spain and Poland who
independently interacted with three types of conversational agents, including text-based andvoice-enabled interactions, for three weeks (Belda-Medina & Calvo-Ferrer, 2022). Analysis of
the quantitative and qualitative data showed a gap between student preparation and recent
advances in the application of AI in language learning. They found that privacy must be
considered carefully, particularly when young language learners use conversational agents. In
addition, students preferred text-based interactions as voice-enabled interaction sounded robotic
and unnatural (Belda-Medina & Calvo-Ferrer, 2022).
There is another study conducted on Japanese university students who spent two months
using Amazon Alexa, an intelligent personal assistant (IPA), at home for autonomous
second-language learning (Dizon & Tang, 2020). Although most of the students found Alexa
beneficial for learning the language, they gave up communicating with the IPA when theystruggled with the interactions.
These studies indicate that NLP might contribute to the development of student autonomy if
the interaction goes well. However, precautions for its use must be taken in advance, andfamiliarity with conversational agents and human-like communication are essential.

62.4 Students’ Autonomy
Because the target learners in our study were university students, students’ autonomy wasconsidered as an important factor. Each student has a different schedule; therefore, they must
decide what to do and when to do it. Learner autonomy is defined as “the ability to take chargeof one’s own learning (Little, 2007, p.15).” Little says, “adult language learning programmes
should be capable of meeting the specific communicative needs of individual learners (2007,p.16).” Adult education needs to “develop the individual’s freedom by developing those abilities
which will enable him to act more responsibly in running the affairs of the society in which helives” (Holec, 1981, p.1).
Miura (2020) stated that students assigned more challenging tasks demonstrated richer
speech and also reported higher levels of satisfaction. This suggests that students are likely to
seize opportunities to enhance their language skills when facing challenging and beneficial tasks
that students believe to be valuable. Consequently, when students have meaningful materials,
their engagement expands beyond the classroom, fostering greater autonomy.
During the COVID-19 pandemic, student autonomy seemed to be in greater demand. Our
school provided only online lessons for a while, and students took some of the lessons online
and others on-demand. Even after the school decided to offer lessons on campus, there weredays when students took only online or on-demand lessons at home. This means that they wererequired to plan to study independently.
3. Aim of this Research and SmallGPTalk
Since we have investigated the importance of AI for non-native speakers of English and theresults related to previous studies of NLP, this study examined the possibilities of fostering
student’s autonomy and enriching their expression using a conversational agent called
SmallGPTalk as a means for students to practice speaking in circumstances where face-to-facecommunication is restricted because of the COVID-19 pandemic.
SmallGPTalk is a free application produced by Classmethod, Inc. (2023) that facilitatescasual conversations with AI English tutors “sent” from ChatGPT on LINE. This provides asituation similar to that of a one-on-one English conversation. By using LINE, the exchange ofmessages in a certain context creates a more authentic and human-like conversation experience
than typing ChatGPT directly.
Users are ready to use the application after accepting a friend request from SmallGPTalk.
They can start by choosing one of the three topics –favorite hobbies, a memorable vacation, or afavorite type of food– suggested by SmallGPtalk and begin chatting by texting a message. It is
possible to change the topic during the interaction and start over. Replies from the AI instructors
appear instantly. When the user wants to stop chatting, they simply type “Owarimasu” (I will
finish) in Japanese. They then receive personal feedback (see Figure 1), which is not always
possible for human instructors.
When students engage in communicative activities in pairs or groups, there is often a7disparity in their English proficiency levels. Fluent English speakers may feel disappointed
when paired with those with limited proficiency, whereas the latter may feel intimidated by theformer. SmallGPTalk can adapt to each student’s level and provide simpler sentences ifnecessary. This can be a good motivation for its use with students.
Figure 1
An Example of Feedback from SmallGPTalk

Note. This figure demonstrates an example of feedback from SmallGPTalk. Adapted from Classmethod,Inc., 2023 (https://dev.classmethod.jp/articles/smalltalk-with-chatgpt-small-gptalk/).

4. Context, Method, and Materials
4.1 A Preliminary Survey
4.1.1 Overall Information about the Preliminary Survey
A preliminary survey was conducted to determine whether SmallGPTalk was sufficiently
user-friendly for Japanese university students, and whether it could contribute to theirconversational skills. According to the Mobile Society Research Institute (2023), established by
NTT DOCOMO Inc., a major telecom carrier in Japan, LINE (a free communications
application) has the highest usage rate among all generations in Japan. More specifically, 94.4%
of Japanese teenagers use it, followed by 92.8% of young adults in their 20s. All students in thetarget classes were LINE users and were therefore accustomed to sending or accepting friend
requests on LINE.
Students were sorted according to their placement test scores and classified into three levels:
primary, intermediate, and advanced. Classes were more likely to be decided by the number ofstudents per class than by the students’ actual English level. Approximately 20% of the students
were in the advanced class, 20% in the primary class, and the rest were in the intermediate class.
Only intermediate (80%) and advanced (20%) classes were available for students in the Faculty8

of Health Sciences, which means that there were some students whose English proficiency was
not sufficiently high in the intermediate class.
As the students were familiar with LINE, SmallGPTalk appeared to be an attractive tool forenhancing their communication skills. The application was introduced in two intermediateclasses consisting of mixed student groups from the Radiological and Rehabilitation
Departments. One class consisted of 27 freshmen, whereas the other had 29 sophomores. The
aim of the class was to develop students' proficiency in the four language skills, andSmallGPTalk seemed to play an important role in fostering communication.
The instructor first taught the participants how to use it and for what purposes, and showed
examples that might be developed by chatting with an AI instructor. Possible risks related to
ChatGPT were also stated, and the students were requested not to post their personal information.
In addition, they were instructed not to start using SmallGPTalk before class, as it could only be
used once a day owing to the system configuration. Each student and instructor conducted atleast three exchanges per session.
4.1.2 Results and Discussion
The findings revealed the following. First, SmallGPTalk was generally accepted positively
by students. However, according to the instructor’s observations, the learners took aconsiderably longer time to understand the replies they received and express themselves in
response to the messages in English. Some students spent considerable time looking up
expressions on the Internet during their interactions.
This preliminary survey suggested that SmallGPTalk was more likely to benefit
advanced-level students as they are likely to be able to use the target language easily enough tokeep the conversation going than intermediate students.
4.2 A Second Survey
4.2.1 Overall Information about the Primary Survey
Based on the preliminary survey, the following survey was planned for advanced-classstudents: The same type of prior explanation as that for the students who contributed to thepreliminary survey was provided to 25 sophomores in an advanced class. The instructor
explained that all data would be collected for research and shared in a form that would not allow
individuals to be identified. Each student had the right to refrain from contributing by refusing toanswer nine questions in the final class. Data from two students who were absent on either the
pre- or post-survey days were missing, so the data of 23 students were analyzed in this survey.
For seven consecutive weeks, the students used SmallGPTalk for 15–20 minutes in eachclass. Before introducing SmallGPTalk to the students, the teacher asked them four questions
orally in English, and they wrote their answers on a sheet of paper. After engaging in seven chatsessions using SmallGPTalk, the same process was repeated in the second survey to examine
whether more enriched content was present. The data were entered into a spreadsheet forcomparison. The four questions were as follows:91. What do you want to talk about today?
2. Why did you choose this topic?
3. Why don't we talk about your hobbies?
4. By the way, what do you like to do in your free time?
In addition, data were collected from 24 students’ responses to nine questions on Google
Forms on the last day of SmallGPTalk use in class. However, the data from only 23 students
were used in this study. This survey was conducted in Japanese. Participants answered thefollowing questions:
1. Did you use SmallGPTalk outside of classes?
I used it only in class.
I used it once or twice.
I used it around 3–5 times.
I used it around 6–10 times.
I used it over 10 times.
2. Did you enjoy talking with SmallGPTalk?
I enjoyed it a lot.
I enjoyed it.
I did not really enjoy it.
I did not enjoy it at all.
3. What is the advantage of chatting with SmallGPTalk rather than with an actual
human?
4. What is the disadvantage of chatting with SmallGPTalk than with a real human?
5. Please share one experience you had while using SmallGPTalk.
6. Please share whether you have learned to communicate in English or have improved
your communication skills by chatting with SmallGPTalk.
7. Would you like to continue using SmallGPTalk or another application to improve yourEnglish skills after completing this course?
Yes, absolutely.
Yes, if I feel like it.
Probably not.
Never.
8. Please tell us why you answered Yes/No to question 7.
9. Do you have any feedback on SmallGPTalk?

10

The majority of the questions required description-style answers, except for questions 1, 2, and 7,
which were multiple-choice questions.
Lastly, the students also kept records of their topic and their thoughts on the talk after eachsession, although these data are not dealt with in this paper, but were collected for further study.
4.2.2 Results of the Quantitative Data and Discussion
The students’ answers to the four questions in the pre- and post-survey were compared to see
if there were richer sentences, complicated structural sentences, or other traits. The expectations
for using SmallGPTalk include increasing students’ vocabulary, providing good examples for
explaining things, and expressing opinions.
No remarkable differences were found in the quantitative data between the pre- andpost-surveys. There are several possible explanations for this. First, students’ answers tended to
be short. Through several experiences of chatting with AI instructors, they became accustomed
to receiving long messages, taking a while to read them, and quickly sending short replies. Next,
the students might have felt bothered, as they were required to answer the questions after thechat using SmallGPTalk. Chatting through SmallGPTalk seven times might have been
insufficient to improve their conversational skills.
Another possible reason is that the expected answers to Questions 1, 3, and 4 did not need to
be long in the first place. For example, the expected answer to the question, “What do you wantto talk about today?” is “I want to/I’d like to talk about [ noun(s) ].” As an examination of thequantitative data, the answers to Question 2, “Why did you choose this topic?” were the only
possible data to compare the pre- and post-survey, and they were not sufficient to finddifferences.
However, only slight changes were observed. Some students used Japanese words such as
魅力(attractiveness) and soba (Japanese buckwheat noodles) in the pre-survey, but no one used
Japanese words in the post-survey, except for proper nouns and the word manga, which is
widely used as an English word. When they used Japanese or Romanized Japanese in English
sentences without any explanation during the interactions, the AI instructor explained them inEnglish. This type of interaction can influence student attitudes.
4.2.3 Result of the Questionnaire Data and Discussion
The most popular answer response to question 1 was “I used it once” (58.3%), and 37.5% of
the participants responded, “I used it once or twice.” The remainder used it 3–5 times, whichshows that SmallGPTalk was mainly used in class, but not frequently outside the classroom.
This means that the activity did not contribute to the development of students’ autonomy.
Answers to Question 2 revealed that many of the students liked using SmallGPTalk.
Approximately 29.2% of the participants answered, “I enjoyed it a lot,” and 62.5% said, “Ienjoyed it,” although 8.3% did not enjoy talking to the AI instructor. The responses to Questions
1 and 2 indicated that the students did not use the application to improve their English outside ofclass, even though they enjoyed using it in class.
11

Regarding Questions 3 and 5, the most common answer to the good points of using
SmallGPTalk was that the AI instructors could converse on any topic that the students had
started. Most of the students shared episodes on Google Forms that indicated the deep
impression this experience made on them. For example, the AI guessed the title of a movie thatone student spoke about, whereas another AI pretended to be interested in cooking. Students
were also pleased that they did not have to worry about making the instructors wait when they
took time to reply or when they struggled with long replies that included unknown vocabulary.
Quick responses and the natural flow of the conversation without correcting grammaticalmistakes were their favorite points. Because many students hesitated to speak English in public,
the application encouraged them to use English.
In contrast, according to the results of Question 4, nine students (37.5%) felt that the replies
they received were extremely lengthy and that they were unable to read and understand thecontent. Five students (20.8%) were disappointed that they could not experience human-likereactions from the AI instructors. Students hoped to have the same instructors to build good
relationships with them. It is evident from the students’ comments that they expected AI
instructors to act like partners or friends who were eager to understand them.
Regarding Question 6, 22 out of 24 students (91.7%) realized that SmallGPTalk helped them
improve their English proficiency and grammar skills. Many participants discussed aspects suchas vocabulary and grammar. Replies often included words that the students had almost forgottenor that were unknown. The replies also introduced phrases and expressions with context. In
addition, they recognized that they did not have to worry about making mistakes. One student
used strategies to consider different ways of expressing unknown words. The remaining two
who did not feel any improvement seemed to have a positive view of SmallGPTalk but said thatthey needed more time to realize their progress.
Overall, 8 students (33.3%) responded that they would like to continue using SmallGPTalk,
12 students (50%) shared that they might use it, and 4 students (16.7%) answered that they
probably would not use it. Students who used SmallGPTalk outside the class wanted to continue
using it (see Table 1), but only moderately so. Table 2 shows the tendency of students who
enjoyed using SmallGPTalk in class to use it outside class as well, but only moderately. These
results indicate that the enjoyment in using ChatGPT does not necessarily lead to its continued
use. This could be because their major was not English, and their desire to improve their English
was not their first priority.
However, this case study is limited in terms of the scope of the target. Analyses wereconducted to determine whether there was a trend; however, no statistically significant
differences were observed.

12

Table 1
Relationship between the frequent use of SmallGPTalk and its future use

n=24

Absolutely. I will

I will use it if I feel

continue using it.

like it.

3
5
0
0
8

Only in class
Once or twice
Around 3–5 times
Over 5 times
Total

Probably not

7
4
1
0
12

Never

4
0
0
0
4

0
0
0
0
0

Table 2
Relationship between the frequent use of SmallGPTalk and satisfaction levels

n=24

I enjoyed it a

I enjoyed it.

lot.
Only in class
Once or twice
Around 3–5 times
Over 5 times
Total

3
4
0
0
7

I did not really enjoy

I did not enjoy it at all.

it.

9
5
1
0
15

2
0
0
0
2

0
0
0
0
0

4. Conclusion
For non-native English speakers, AI offers many opportunities to compensate for their
weaknesses in using the language. The AI era has already begun, and it is important for students
to learn how to make good use of it. This study was conducted to determine whether
SmallGPTalk, a conversational agent, stimulates students’ autonomy and enriches their English
language expressions.
Although the students enjoyed chatting with the AI instructors and felt that their English had
improved through the activities, many did not use it outside the classroom. This implies that
SmallGPTalk did not strongly motivate users to use it continuously. Based on the students’answers to the questionnaires, we conclude that the use of SmallGPTalk did not develop thestudents’ autonomy.
Chatting for 15 minutes in seven classes might not have been sufficient to recognize theirclear improvement, but nearly 92% of the students felt that AI helped them improve their
vocabulary, including collocations, through interaction in an atmosphere where they felt safe to
make mistakes and take time to reply. However, a sense of reward—that is, an awareness of
their growth—can be a good source of motivation.
Future studies should consider maintaining records of what the students have learned or13perceived to be necessary to facilitate their awareness. This has the potential to motivate them to
use the application or other meaningful resources as they become aware of the value of what
they have learned or felt. Therefore, it is important to find an effective way to develop learnerautonomy. In addition, the quantitative survey must be modified. The topics chosen by students
and the frequency of discussions on the same topic may influence the content of theirexpressions. Therefore, for the quantitative survey, it would be advisable to let students choose a
topic from the following three: favorite hobbies, a memorable vacation, and favorite type offood, and write a decent number of sentences about the chosen topic. It would then be easier tosee if they have learned vocabulary through coherent communication with AI tutors.

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M., González-Trujillo, J. D., Montaño-Centellas, F., Paudel, K., White, R. L., & Veríssimo,
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Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues andapplications of artificial intelligence in language education. Educational Technology &Society, 26(1), 112-131.
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SPELT JOURNAL 投稿規定
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①日本語文献の発行年のあとのピリオドは不要。
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Baker, S. C. & MacIntyre, P. D. (2000). The role of gender and immersion incommunication and second language orientations. Language Learning, 50,
311-341.
伊田勝憲 (2002)「学習動機づけの統合的理解に向けて」.『名古屋大学大学院教育発達科学研究科紀要』, 49, 65-76.
池田 央 (1989)「階層クラスター分析」. 池田央(編)『統計ガイドブック』(pp.199-200)
東京：新曜社.
文部科学省(2017)「全国学力・学習状況調査における中学校の英語の実施に関する最終報告 基礎資料（平成 28 年度英語力調査結果（中学 3 年生）の速報）」
文部科学省ホームページ（ http://www.mext.go.jp/component/
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（①と②
は１つの章にまとめてもよい。
）➂「調査の方法」や「研究の方法」等として、研究課題・仮説（②の最後に述べるか、独立した章を設けてもよい）、調査参加者・実験被験者、データ収集に用いた試験・質問紙等の道具、調査・実験の手続き、教育介入の内容と手順、分析の方法等を述べる。
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speltjournal@spelt.main.jp
竹内典彦(SPELT JOURNAL 編集委員長）北海道情報大学経営情報学部先端経営学科
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学会誌委員竹内 典彦
三浦 寛子

査読協力者（五十音順）
杉浦 理恵

照山 秀一

『実用英語教育学会紀要』 SPELT JOURNAL

2024 年 3 月 31 日 発行
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ISSN 2187-4123