

































English Language Teaching Educational Journal   ISSN 2621-6485 

Vol. 8, No. 3, December 2025, pp. 164-176  

        https://doi.org/10.12928/eltej.v8i3.14228        http://journal2.uad.ac.id/index.php/eltej/index         eltej@pbi.uad.ac.id  

Exploring AI-mediated linguistic and cognitive support for non-

native English speakers in English-only higher education 

Ching-Yi Tien a, 1, *, Noor Azam Haji-Othman b, 2 

a Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan 
b Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Brunei Darussalam 

 
1 tien@isu.edu.tw *; 2 azam.othman@ubd.edu.bn  

 

*corresponding author   

A R T I C L E  I N F O 

 
 

A B ST R ACT   

 

 

Article history 

Received 10 August 2025 

Revised 25 November 2025 

Accepted 9 December 2025 

Available Online 15 December 2025 

 The release of ChatGPT by OpenAI in 2022 marked a major milestone, 
beginning a new phase in how artificial intelligence (AI) is used in 
education and transforming the way students access and engage with 
academic resources. This study explores how non-native English-
speaking students handle the language challenges of content-area 
courses in higher education when instruction is focused solely on 
professional subjects in an English-only setting, with limited explicit 
language support to improve English skills. Using a sample of 
undergraduate students (N = 63), this research examines differences in 
AI tool usage, perceptions, and outcomes based on gender, nationality, 
major, and year of study. Independent samples t-tests and one-way 
ANOVA were performed on variables that measure perceived 
language learning benefits, cognitive skill development, creative tool 
use, and the overall effectiveness of AI tools. Open-ended questions 
provided qualitative data for this study. The findings show that AI and 
Generative AI tools meet students’ linguistic needs, address cognitive 
demands, support personalized learning, enhance understanding of 
subject material, and improve academic performance. Contrary to 
earlier research suggesting male students are more proficient with AI, 
this study finds that female students are more likely to view AI tools 
as beneficial for integrating thinking and higher-order cognitive skills. 
The study also discusses potential challenges related to AI use and 
notes its methodological limitations.  

 

© The Authors 2025. Published by Universitas Ahmad Dahlan. 

This is an open access article under the CC–BY-SA license. 

    

 

 
Keywords 

Artificial intelligence  

Higher education  

Language learning 

Cognitive ability 

NNES student 

 

 

 

 

How to Cite: Tien, C. Y., & Haji-Othman, N. A. (2025). Exploring AI-mediated linguistic and cognitive support 
for non-native English speakers in English-only higher education. English Language Teaching Educational 
Journal, 8(3), 164-176. https://doi.org/10.12928/eltej.v8i3.14228   

1. Introduction  

Since John McCarthy coined the term Artificial Intelligence (AI) in 1956 (Zawacki-Richter et al., 
2019), the field has evolved from abstract theory to a pervasive force shaping nearly all aspects of 
modern life. Within education, the COVID-19 pandemic accelerated the adoption of digital 
technologies, prompting a rapid shift toward online and blended learning models (Salido et al., 2025; 
Ng et al., 2023). This transformation laid the groundwork for the growing integration of AI-supported 
learning tools in higher education. The release of ChatGPT by OpenAI in 2022 represents a significant 
turning point in the use of AI in education, particularly in how learners interact with and access 
language learning resources (He, 2025; Moorhouse, 2024; Moorhouse & Wong, 2025; Moorhouse et 
al, 2025). Concurrently, educational institutions are increasingly incorporating Generative AI (GenAI) 
to enhance personalized learning. These systems allow instructional content to be tailored to learners’ 

https://doi.org/10.12928/eltej.v8i3.14228
http://journal2.uad.ac.id/index.php/eltej/index
mailto:eltej@pbi.uad.ac.id
mailto:tien@isu.edu.tw
mailto:azam.othman@ubd.edu.bn
http://creativecommons.org/licenses/by-sa/4.0/
https://doi.org/10.12928/eltej.v8i3.14228
http://creativecommons.org/licenses/by-sa/4.0/
http://crossmark.crossref.org/dialog/?doi=10.12928/eltej.v8i3.14228&domain=pdf
https://orcid.org/0000-0002-6347-6116
https://orcid.org/0000-0003-3218-4492


165 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

individual needs, styles, and paces (Trošelj et al., 2024), with GenAI technologies offering new forms 
of interactivity and customization that traditional methods often lack (Torun & Ozer Sanal, 2025). 

Within this context, AI and GenAI tools are being explored for their potential to support second-
language learners in higher education (Alqarni et al, 2024; Chen, 2024; Choi, 2025; Chuang & Yan, 
2025; Kwok et al., 2024; Liu & Fan, 2025; Nguyen  & Nguyen, 2025). Prior studies have highlighted 
several benefits of AI integration, including increased learner autonomy (Moorhouse & Wong, 2025), 
enhanced vocabulary acquisition (Alharbi & Khalil, 2023), and improved academic writing (Chen et 
al., 2026; Thangthong et al., 2024; Ozfidan et al., 2024), gender differences in engagement with GenAI 
tools in academic contexts (Hillman, 2025; Tang, 2025). These tools allow students to design 
individualized practice tasks, receive instant feedback, and engage in simulated conversations, 
features particularly valuable for learners with limited access to consistent language practice 
opportunities. 

Even before the rise of GenAI, tools like Grammarly have been widely adopted for academic 
writing support (van Wyk, 2025). Dizon and Gayed (2024) conducted a systematic review of 
Grammarly’s use in L2 contexts. They argued that such tools are best employed alongside instructor 
feedback, allowing teachers to focus on higher-level concerns such as content and coherence, while 
the tool addresses surface-level errors. However, they caution that effective use of these tools requires 
training in their strengths and limitations to avoid blind dependence and to foster critical engagement.  

Then, the emergence of ChatGPT has intensified scholarly interest in AI’s pedagogical value 
(Alhur et al., 2025; Lin & Tsou, 2025; Tsou et al., 2024). Studies have shown positive correlations 
between its use and improved student performance, engagement, and perceptions of learning (Lu et 
al., 2024; Wang & Fan, 2025; Wei, 2023). Research also points to AI’s potential to enhance 
vocabulary acquisition and content-specific learning. In their 2024 study, Nga and Ha explored the 
use of AI to improve vocabulary among university students, highlighting the importance of addressing 
data privacy and security concerns. They emphasized that educational institutions must safeguard 
student data and that educators should be cautious about students becoming overly dependent on AI 
tools, as this reliance could impede the development of autonomous learning abilities. Similarly, 
Nguyen (2023), in a study on sustainable fashion education, observed that students using NLP tools 
like ChatGPT reported improved vocabulary and content comprehension (Rahimi et al., 2025; Zhou 
et al., 2025). However, studies also stressed the need for clear instructional policies to prevent passive 
learning and to encourage active engagement (Al-Harahsheh et al., 2025; Tran, 2025). Nevertheless, 
AI engagement is not always reflective or strategic. Burkhard (2022) observed that while some 
learners adopted AI writing tools uncritically, raising concerns about unintentional plagiarism, still 
others hesitated due to skepticism or a lack of metacognitive strategies. 

Beyond language development, AI is increasingly researched for its impact on students’ cognitive 
growth (Ng, 2024; Wang et al., 2025). Holmes et al. (2023) argue that when integrated effectively, AI 
tools can enhance critical thinking by encouraging learners to analyze, evaluate, and generate content. 
However, findings remain mixed. Xu (2024) reported positive cognitive outcomes among university 
students using AI-assisted learning, while Gerlich (2025), employing a large-scale mixed-methods 
design, found that frequent reliance on AI tools may lead to cognitive offloading and reduce critical 
thinking, especially when not supported by pedagogical scaffolding.  

Despite its growing presence, the adoption of AI in educational settings remains uneven. Zawacki-
Richter et al. (2019), in a systematic review of 146 studies, found that research in this field is 
dominated by STEM disciplines, with only a small fraction led by scholars from education. This 
highlights the need for more pedagogically-grounded research on how AI affects language learning, 
particularly in subject-related courses and cognitive engagement. Additionally, concerns about AI’s 
implications for human expertise continue to surface. For instance, Humanika and Radjaban (2024) 
found that while some translation students viewed AI as a useful tool to enhance efficiency, others 
feared that it could diminish job prospects by automating complex linguistic tasks. However, its 
limitations in processing idiomatic and culturally embedded language remain evident. 

Despite increasing interest in AI in education, a significant research gap persists regarding how 
non-native English-speaking (NNES) students in content-based courses in EFL higher education 
settings use AI to support both linguistic and cognitive development. Few studies focus on learners in 
non-English-speaking contexts, such as Taiwan, where international and local students share 
multilingual academic environments but face similar linguistic challenges in English-medium 



ISSN 2621-6485 English Language Teaching Educational Journal 166 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

coursework. To fill this gap, the present study investigates how NNES students at an international 
college in Taiwan use AI tools to enhance language skills, academic achievement, and cognitive 
engagement in content courses with limited explicit language support. The findings aim to clarify AI’s 
potential and limitations in EMI classrooms and contribute to more informed pedagogical decision-
making. Unlike previous research centered on language-specific applications, this study explores how 
students use AI to bridge both linguistic and cognitive gaps in English-only content-course 
environments. The research questions guiding this study are as follows. 

1. How do AI tools assist non-native English-speaking students (NNES) in managing linguistic 
demands in content-area courses where instructors do not address students’ language 
development? 

2. To what extent do AI applications support cognitive development among non-English-
speaking students? 

3. What are students’ perceptions of the usefulness, challenges, and integration of AI tools in 
their academic routines? 

4. Are gender, student status, academic major, and year-of-study significant factors influencing 
students' adoption of AI applications? 

2. Method 

2.1. Participants  

Participants were recruited through convenience sampling from two courses at an international 
college in southern Taiwan. Class One consisted of 14 students (22.2%) from the Program in 
International Media and Entertainment Management enrolled in advertising courses, while Class Two 
included 36 students (57.1%) from the Department of Global Communications and Applied English 
taking an Introduction to Linguistics course. Additional students from other departments also enrolled. 
Both 18-week courses were delivered in English and focused on content instruction with minimal 
explicit language support. The demographic breakdown of the 63 participants was as follows: 18 
(28.6%) were male, 44 (69.8%) were female, and 1 (1.6%) preferred not to specify their gender. 
Among the participants, 25 (39.7%) were international students, while 38 (60.3%) were local students. 
International students mainly came from Southeast and East Asian countries such as Indonesia, 
Thailand, Vietnam, and Japan. The students' year distribution was: Freshman (1.6%), Sophomore 
(30.2%), Junior (47.6%), and Senior (20.6%). All participants had studied English for more than ten 
years.  

2.2. Data Collection Technique 

Data for this research were collected at the end of the spring 2024 semester. The questionnaire 
items were adapted from prior studies on AI-assisted learning (e.g., Lu et al., 2024; Wang & Fan, 
2025) and were reviewed by two experts in applied linguistics for content validity. A structured 
questionnaire contained 32 items, with four items in Part I aimed at understanding the demographic 
information of the participants; eight items in Part II seek to find out the use of AI tools in students’ 
English language skills; six items in Part III aim to reveal AI applications that support students’ 
cognitive skills; nine items in Part IV aim to investigate how students integrate AI tools into their 
study routines; Part V included five open-ended questions about students’ preferred tools, perceived 
improvements, challenges, and suggestions for using AI in their academic work. 

Participants rated their agreement on a 5-point Likert scale (1 = strongly disagree, 5 = strongly 
agree). To assess the survey's internal consistency, Cronbach’s alpha was calculated. The scale 
demonstrated excellent internal consistency (Cronbach’s α = .924; 23 items), indicating that the items 
reliably measure the same construct. Additionally, the Cronbach’s alpha based on standardized items 
was 0.930, showing that even when accounting for differences in item variance, the reliability remains 
very high. According to conventional thresholds, a Cronbach’s alpha above 0.90 is considered 
excellent (George & Mallery, 2003). All items confirmed that the questionnaire was effective in 
measuring responses related to the use of AI applications among the participants in higher education.  



167 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

2.3. Data analysis 

Descriptive statistics were generated using SPSS version 27. Before conducting inferential tests, 
data were checked for normality and homogeneity of variance. A one-way ANOVA was used to 
examine differences across gender, major, and year of study, and independent samples t-tests 
compared international and local Taiwanese students. Open-ended responses were analyzed through 
thematic analysis to identify recurring themes in AI use. Ethical approval and informed consent were 
obtained prior to data collection. 

3. Finding and Discussion 

3.1 Findings 

To assess how effective AI tools are in helping non-native English-speaking students handle 
linguistic challenges in content-area courses where instructors do not prioritize language development, 
descriptive statistics were utilized to analyze questionnaire data. The open-ended responses were 
reviewed to supplement the qualitative data and assist the researcher in gaining a more comprehensive 
understanding of how students used the AI tool during their academic programs. The following 
sections generally address the four research questions of this study.  

RQ1: How do AI tools assist non-native English-speaking (NNES) students in managing 
linguistic demands in content-area courses where instructors do not address students’ 
language development? 

To answer the first research question, the data from Part II of the questionnaire were analyzed 
using SPSS 27 software. The results are arranged in descending order to highlight the most-to-least 
perceived benefits of AI tools.   

 

Table 1.  Students’ Usage of AI Tools for Addressing Linguistic Challenges 

Item Mean S.D. 
1.2 AI tools have helped me with grammar and sentence structure. 4.19 1.030 
1.4 AI tools have made me more confident in writing English. 3.97 1.015 
1.1 AI tools have enhanced my vocabulary. 3.95 1.069 
1.5 AI tools have helped me to understand English-only content courses 
better. 

3.84 1.081 

1.8 AI applications make my schoolwork more engaging and fun. 3.78 .941 
1.7 AI applications give valuable feedback on my language skills. 3.73 .971 
1.3 AI tools have improved my pronunciation and speaking skills. 3.51 1.230 

 

Table 1 summarizes students’ perceptions of how AI tools support their language needs. Students 
rated AI as most helpful for improving grammar and sentence structure (M = 4.19, SD = 1.03), 
followed by boosting writing confidence (M = 3.97, SD = 1.02) and expanding vocabulary (M = 3.95, 
SD = 1.07). They also agreed that AI tools improved their understanding of English-only content 
courses (M = 3.84, SD = 1.08). Pronunciation and speaking skills received the lowest scores (M = 
3.51, SD = 1.23), indicating more limited perceived benefits. AI was seen as moderately effective for 
engagement (M = 3.78, SD = 0.94) and providing feedback (M = 3.73, SD = 0.97). Regarding the 
most useful tool, ChatGPT was chosen by 54% of students, followed by Grammarly (33.3%), Copilot 
(4.8%), Gemini (1.6%), and others (6.3%). 

Responses from the open-ended section of the qualitative data include a question about “what 
specific AI tools or applications have you used to improve your English language skills?” ChatGPT 
was mentioned 20 times, followed by Grammarly (14), Quillbot (4), Google (3), VoiceTube (2), and 
others (2) by the participants. These results suggest that ChatGPT and Grammarly are the primary 
tools students prefer for improving or getting help with their language skills. When asked, can you 
describe any notable improvements you have noticed in your English skills or thinking abilities since 
using AI tools? Participants reported various improvements from using AI or GenAI applications, 
such as a better vocabulary, improved grammar and sentence structure, enhanced comprehension and 
speed, increased confidence, and stronger critical thinking. Below are the students' direct quotes.   



ISSN 2621-6485 English Language Teaching Educational Journal 168 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

S3:  I learned more vocabulary that I do not usually use in daily life. 

S7:  AI tools have helped me enhance my vocabulary, grammar, and sentence structure. 

S31:  My sentence structure is better than before. 

S46: Grammarly helps me to correct my grammar mistakes, and ChatGPT gives me lots of useful 
information. 

S50:  I can understand the content more easily than before. 

S26:  Find the answer quickly. 

S39:  More confident to type English, no need to worry about mistakes 

S45:  I learn to know what good, high-quality writing and information look like. 

Both descriptive statistics and participant feedback show that AI and GenAI tools significantly 
enhance the English language skills of non-English speakers, especially when explicit English 
instruction is not the main focus of the content courses. 

RQ2: To what extent do AI applications support cognitive development among non-English-
speaking students? 

To address RQ2, quantitative data from questionnaire Part III were analyzed. The descriptive 
statistical results are presented in Table 2. The results are ordered from highest-to-lowest to emphasize 
the perceived benefits of AI tools in cognitive skills as reported by students.   

 

Table 2.  The Utilization of AI Tools for Cognitive Skills 

Item Mean S.D. 
2.1 AI applications have improved my ability to analyze information. 3.95 .958 
2.2 AI applications have helped me in developing better problem-solving 
strategies. 

3.90 .928 

2.4 AI applications have enhanced my ability to make connections between 
different pieces of information. 

3.86 .965 

2.3 AI applications have made me more critical of the information I encounter. 3.84 .884 
2.5 AI applications have helped me improve my focus and concentration. 3.44 .980 

 

Table 2 shows students’ perceptions of how AI tools supported their cognitive skills. AI was rated 
most helpful for analyzing information (M = 3.95, SD = 0.96), followed by developing problem-
solving strategies (M = 3.90, SD = 0.93) and connecting ideas (M = 3.86, SD = 0.97). Critical thinking 
also showed positive, though slightly lower, improvement (M = 3.84, SD = 0.88). The lowest rating 
was for sustaining focus and concentration (M = 3.44, SD = 0.98), indicating a more limited perceived 
effect. When asked which tool best supported problem-solving, students most often chose ChatGPT 
(60.3%), followed by Google (30.2%), Copilot (4.58%), Gemini (3.2%), and others (1.16%), 
reflecting a strong preference for general-purpose AI tools. 

In response to the open-ended question about whether their cognitive abilities had improved after 
using AI tools, students reported both benefits and some concerns. Below are selected direct quotes 
from the participants. 

S14: I can go through with my ideas from concept to execution. It provides me with a clear structure and 
step-by-step solutions to problems, which have improved my ability to think outside the box. Running 
my essays through ChatGPT allows me to catch any redundant information/phrases/etc., and the 
program gives me feedback that I can apply in real-time. 

S50: I can understand the content more easily than before.  

S57: I have become better at clarifying any data or material from my learning.  

Several participants highlighted the cognitive benefits of using AI tools like ChatGPT in their 
academic work. They noted improvements in organizing ideas from concept to execution, enhanced 
problem-solving through structured and step-by-step guidance, and the ability to think more 
creatively. Additionally, AI tools were seen as helpful for real-time feedback, improving clarity, 
minimizing redundancy in writing, and making learning materials easier to understand.  



169 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

Despite the positive effects of using AI and generative AI (GenAI), several students expressed 
concerns about becoming overly reliant on these tools. Their responses reflect worries about 
diminished independent thinking and writing skills. Selected quotes include: 

S9:   I cannot think by myself. 

S42: I have become lazy to think and go directly to AI when I encounter questions. 

S52: It is getting worse because I do not have to think about it by myself. 

S63: Initial overreliance on AI tools to generate content… worsened my ability to express myself through 
writing. 

Some students reported that excessive reliance on AI tools negatively impacted their cognitive 
engagement. They noted a decline in their motivation to think independently, problem-solve, and 
articulate their thoughts clearly in writing. These reflections highlight the importance of balancing AI 
use with active learning to avoid dependency and maintain essential critical thinking skills. 

Q3: What are students’ perceptions of the usefulness, challenges, and integration of AI tools 
in their academic routines? 

To address the research question concerning students’ perceptions of the usefulness, challenges, 
and integration of AI tools in their academic routines, Table 3 presents the items sorted by mean score, 
from highest-to-lowest. 

Table 3.  Usage of AI Tools in Academic Routines 

Item Mean S. D. 
3.5 I believe AI tools are beneficial for language learning. 4.05 .888 
3.8 I have seen positive results in my learning due to AI tools. 3.90 .979 
3.6 AI tools have helped me achieve my learning goals. 3.90 .979 
3.2 AI tools like Canva are easy to navigate and use. 3.84 1.208 
3.1 I always use the AI tool Canva to create presentation content.  3.78 1.301 
3.7 AI tools have helped me improve my grade. 3.73 .987 
3.9 I have no negative concerns about using AI tools for my studies. 3.33 1.178 
3.4 AI tools like Midjourney are easy to navigate and use. 2.98 1.338 
3.3 I always use the AI tool Midjourney to create visual content.  2.70 1.466 
3.5 I believe AI tools are beneficial for language learning. 4.05 .888 

 

The data in Table 3 shows a largely positive attitude toward AI tools in educational settings. The 
highest agreement is for the statement “I believe AI tools are beneficial for language learning” (M = 
4.05, SD = 0.89), indicating a strong consensus on AI’s value in improving language skills. Positive 
impacts on learning outcomes are also reflected in responses to “I have seen positive results in my 
learning due to AI tools” and “AI tools have helped me achieve my learning goals” (both M = 3.90, 
SD = 0.98), suggesting that AI is considered effective for advancing education. AI’s role in improving 
academic performance is moderately supported (“AI tools have helped me to improve my grade,” M 
= 3.73, SD = 0.99), confirming their perceived usefulness beyond simple tasks. Regarding concerns, 
the average score for “I have no negative concerns about using AI tools for my studies” is 3.33 (SD = 
1.18), reflecting a generally neutral to positive view, although some students still hold reservations. 

The participants recruited for this study were enrolled in university-level Linguistics and 
Advertising courses. The researcher also aimed to explore whether students were using other 
generative AI tools, such as Canva and MidJourney. Therefore, additional questions regarding the 
application of these tools were included in the questionnaire. The results below report students’ ease 
of use and tool preferences. When examining usability, tools such as Canva received moderately 
favorable ratings for ease of navigation and use (M = 3.84, SD = 1.21). The regularity of Canva’s use 
for creating presentation content is also relatively high (M = 3.78, SD = 1.30), indicating its popularity 
and user-friendliness among students. In contrast, lower means are observed for Midjourney in both 
ease of use (M = 2.98, SD = 1.34) and frequency of use for creating visual content (M = 2.70, SD = 
1.47). These results highlight potential barriers for participants, such as learning curves or tool 
accessibility, associated with more specialized AI tools. Overall, tools focused on language and 
presentation (e.g., Canvas) are seen as more accessible and beneficial compared to generative visual 
AI (e.g., Midjourney), which may necessitate further training or support. The results also suggest that 



ISSN 2621-6485 English Language Teaching Educational Journal 170 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

while AI tools are embraced for both content creation and skills development, their adoption is 
differentiated by type and complexity. 

To better understand students’ perceptions of the usefulness, challenges, and integration of AI 
tools, the survey included several open-ended questions. The qualitative responses showed that 
ChatGPT was by far the most frequently used tool (30 mentions), followed by Canva (6), Grammarly 
(4), Quillbot (3), and Gemini (2). This pattern highlights a strong reliance on a single, general-purpose 
generative AI tool for various academic tasks, while other applications were used more selectively for 
functions such as editing, paraphrasing, and visual content creation. 

Students also reported several challenges related to AI use. The most common concern was 
overreliance, with some participants noting that frequent dependence on AI made them “lazy to think” 
and weakened their ability to express ideas independently. Others expressed concerns about accuracy, 
pointing out that AI sometimes produces outdated, incorrect, or awkward translations, highlighting 
the need for critical evaluation and fact-checking. Language barriers were also mentioned, as certain 
tools struggled to interpret user intent or generate context-appropriate phrasing. Additionally, though 
less frequently, concerns included cost, limited access to full features, internet dependency, and 
regional restrictions blocking access to specific platforms, all of which could hinder students’ ability 
to explore a broader range of AI tools. 

Q4: Are gender, student status, academic major, and year-of-study significant factors 
influencing students' adoption of AI applications? 

To answer the final research question, descriptive statistics, analysis of variance (ANOVA), and 
independent samples t-tests were used. The results are shown in the following sections. 

1) Gender 

An ANOVA was performed to explore gender differences in perceptions of AI tools among male, 
female, and those who select “prefer not to say.” Although most items showed no statistically 
significant differences, suggesting generally similar perceptions across groups, three items revealed 
notable variation. For Item 1.6 (preferred AI tool for language learning), females more often chose 
higher-coded tools than males, F(2, 60) = 4.23, p = .019. Significant differences also appeared for 
Item 2.1 (analytical ability), F(2, 60) = 3.58, p = .034, and Item 2.4 (integrative thinking), F(2, 60) = 
3.71, p = .030, with female participants perceiving greater cognitive benefits. Overall, while attitudes 
toward AI were similar across genders, differences were seen in tool preferences and higher-level 
thinking skills, especially among female students. 

2) International vs. Local Students 

Independent samples t-tests were performed to compare perceptions of AI tools between 
international and local students. No statistically significant differences were found across all items. 
For example, regarding whether AI tools improved vocabulary (Item 1.1), the difference was not 
significant, t(61) = -0.433, p = .667. Similarly, there were no significant differences for grammar 
support, pronunciation, writing confidence, or problem-solving skills (all p > .05). These findings 
suggest that both international and local students generally hold similar views on how AI tools 
influence their learning. 

3) Academic Major 

An ANOVA was conducted to determine whether students’ academic majors (i.e., IMEM, GCAE, 
or other) affected their opinions of AI tools in language learning and cognitive development. No 
significant differences were found for any item (all p > .05). For example, the difference in perceptions 
of grammar and sentence structure support was close but not statistically significant, F(2, 60) = 2.73, 
p = .074. This indicates that students from different academic backgrounds generally view the impact 
of AI tools similarly. 

4) Year-of-Study 

ANOVA results revealed significant differences across academic years. Academic engagement 
(Item 1.8) and perceived development of critical thinking (Item 2.3) varied notably, F(3, 59) = 4.05, 
p = .011, and F(3, 59) = 2.91, p = .042, respectively. Problem-solving applications (Item 2.6) 
approached significance, F(3, 59) = 2.70, p = .054, indicating a possible trend in tool preference across 
levels. These results suggest that academic progression influences certain perceptions of AI, with the 
most notable differences observed in engagement. 



171 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

3.2. Discussion 

This study aimed to examine how non-native English-speaking students handle the linguistic 
challenges of content-area courses in higher education when instruction is solely focused on 
professional subjects in an English-only setting, with limited explicit language support to improve 
English proficiency. Four research questions were addressed and discussed. While some findings align 
with existing literature, several new insights have also emerged. 

Both quantitative and qualitative findings indicate that AI and GenAI applications play a 
substantial role in meeting students’ linguistic needs, thereby enhancing their academic performance 
in content-area courses. Students reported that these tools were particularly effective in supporting 
grammar improvement, vocabulary expansion, writing accuracy, and comprehension of complex 
subject matter. This aligns with prior research suggesting that AI-assisted learning environments can 
provide immediate feedback, scaffold language learning, and reduce cognitive load, allowing learners 
to focus more effectively on content understanding (e.g., Ng et al., 2023; Moorhouse & Wong, 2025).  

In the present study, participants highlighted the value of AI tools in bridging the gap between their 
existing English proficiency and the linguistic demands of English-only instruction. For example, AI-
powered grammar checkers and writing assistants were frequently cited as instrumental in producing 
academically appropriate texts. Meanwhile, AI chatbots and translation features helped clarify 
discipline-specific terminology and concepts. The findings also revealed that students perceived these 
tools as confidence-building resources, enabling them to participate more actively in academic 
discussions and written assignments. Moreover, the results indicate that participants have become 
adept at using AI and GenAI tools to engage in personalized, or so-called adaptive, learning, an 
approach supported by numerous studies (Campbell & Cox, 2024; Shalevska, 2024; Torun & Sanal, 
2025; Wei, 2023). The notable improvement in students’ knowledge after using tools such as 
ChatGPT and Grammarly highlights their potential to foster independent learning. Further, it fosters 
autonomous learning, a vital skill in higher education. However, the results also suggest that while AI 
tools are embraced for content creation and skills development, their adoption is differentiated by type 
and complexity. 

The study also revealed that students perceive AI tools as valuable for boosting cognitive skills. 
Quantitative data showed that the top-rated benefit was improved analytical skills, followed closely 
by better problem-solving strategies and the ability to combine information from different sources. 
Improvements in critical thinking were also recognized, though to a lesser extent, while focus and 
concentration received the lowest ratings, indicating a more variable impact in this area. These 
findings align with Xu’s (2024) study, which showed that AI can significantly enhance cognitive skills 
like critical thinking and problem-solving. Qualitative feedback reinforced these findings. Students 
explained how AI tools, especially ChatGPT, helped them organize ideas from start to finish, approach 
problems systematically, think more creatively, and improve the clarity of their writing through real-
time feedback. Many also noted that AI made learning materials more accessible and easier to 
understand.  

Overall, these linguistic and cognitive outcomes suggest that AI tools serve as a dual scaffold in 
EMI settings. As students develop more precise vocabulary, grammar, and comprehension with AI 
support, they can shift cognitive resources toward higher-level tasks such as analysis, synthesis, and 
problem-solving. This interaction indicates that linguistic improvements may enable more advanced 
cognitive engagement in English-only courses. At the same time, the results emphasize the importance 
of preventing overreliance. Incorporating AI-literacy activities, such as evaluating AI-generated 
outputs or keeping brief reflective logs, can help students use AI more strategically while preserving 
opportunities for independent thinking. Several participants reported decreased motivation to think 
independently, reduced autonomy in problem-solving, and a weaker ability to express ideas without 
AI assistance. This finding echoes Gerlich’s (2024) study, which showed that greater use or 
dependence on AI tools is associated with reduced or declining critical thinking skills. These concerns 
align with ongoing debates about balancing AI support with the development of independent cognitive 
and language skills. 

A key contribution of this study is examining how background factors (gender, nationality, 
academic major, and year of study) influence students’ adoption and perceptions of AI tools. 
Regarding gender, although overall attitudes toward AI were similar, female students were more likely 
to view AI as helpful for integrative thinking and higher-level cognitive skills. This finding contrasts 



ISSN 2621-6485 English Language Teaching Educational Journal 172 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

with studies like Tang et al. (2025) and Hillman (2025), which reported stronger AI engagement 
among male students. These differences may stem from contextual factors such as cultural 
background, academic environment, or disciplinary expectations. 

There were no significant differences between international and local students or across academic 
majors, indicating that these groups generally shared similar views on AI’s linguistic and cognitive 
benefits. However, the year of study showed notable variation; upper-year students reported higher 
engagement and greater cognitive improvements from using AI. This pattern suggests that academic 
maturity and familiarity with AI may influence how students integrate these tools into their learning. 
These results highlight the need to customize AI-supported learning activities to match students’ 
developmental levels and academic needs. 

3.3. Limitations 

This study has several limitations. First, convenience sampling from two classes at a private 
university in southern Taiwan restricts how broadly the findings can be applied. Second, although 
quantitative data were complemented with open-ended responses, the absence of interviews prevented 
a deeper exploration of student perspectives. Future research should incorporate focus groups and 
additional data sources, such as behavioral traces or performance measures, and explore different 
disciplines to see if gender and year-of-study effects remain. The findings also highlight the 
importance of pedagogical strategies that use AI as a scaffold to support independent learning and 
critical thinking. Training students to use AI strategically rather than routinely will help maximize its 
benefits and reduce overdependence.  

4. Conclusion 

This study examined how non-native English-speaking students manage the linguistic demands of 
English-only content courses with minimal language support. The results show that AI tools, 
especially ChatGPT and Grammarly, significantly assisted students with their language skills and 
thinking processes, enhancing grammar, vocabulary, and analytical abilities, though concerns about 
reliance persisted. Perceptions were generally positive across various groups, with some differences 
based on academic year. More research in broader settings is needed to understand how NNES 
students use AI and the challenges they face. The study highlights the need for clear institutional 
guidelines to encourage responsible and strategic use of AI in higher education. As AI becomes more 
integrated into learning environments, universities should promote practices that improve teaching, 
learning efficiency, and student engagement. Overall, this research provides empirical evidence from 
a non-native English-speaking context, demonstrating AI’s potential to support language and 
cognitive development while emphasizing the importance of guiding learners to use AI thoughtfully 
and independently.  

 

 

Acknowledgment 

The researcher gratefully acknowledges the participation and valuable contributions of all the 

students who took part in this study. Their time, insights, and willingness to share their experiences 

made this research possible.  

 

Declarations 

Author contribution : The research was collaboratively undertaken by both authors, 
encompassing topic selection, proposal development, 
methodological application, data analysis, and the preparation of the 
discussion section.  

Funding statement : No funding is available for this research. 

Conflict of interest : I declare that there are no competing interests. 



173 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

Ethics Declaration : As the authors, we confirm that this work has been written based on 
ethical research principles in compliance with our university’s 
regulations and that the necessary permission was obtained from the 
relevant institution during data collection. We fully support ELTEJ’s 
commitment to upholding high standards of professional conduct and 
practicing honesty in all academic and professional activities.   

Additional 
information 

: No additional information is available for this paper. 

 

 
REFERENCES  

Al-Harahsheh, A., Almahasees, Z., & Al Rousan, R. (2025). The use of ChatGPT in academic writing 

by university students in Jordan. International Journal of Information and Education 

Technology, 15(7), 1468–1476. https://doi.org/10.18178/ijiet.2025.15.7.2348 

Alharbi, K., & Khalil, L. (2023). Artificial Intelligence (AI) in ESL vocabulary learning: An 

exploratory study on students’ and teachers’ perspectives. Migration Letters, 20(S12), 1030–

1045. https://www.researchgate.net/publication/380465714 

Alhur, A. A., Khlaif, Z.N., Hamamra, B., Hussein, E. (2025) Paradox of AI in Higher Education: 

Qualitative Inquiry Into AI Dependency Among Educators in Palestine. JMIR Med Educ, 

11:e74947. hppts://doi: 10.2196/74947  

Alqarni, O. M., Curle, S., Mahdi, H. S., & Ali, J. K. M. (2024). Medical Students’ Perceptions of 

ChatGPT Integration in English Medium Instruction: A Study from Saudi Arabia. Forum for 

Linguistic Studies, 6(5), 749–763. https://doi.org/10.30564/fls.v6i5.6975  

Bälter, O., Kann, V., Mutimukwe, C., & Malmström, H. (2024). English-medium instruction and 

impact on academic performance: A randomized control study. Applied Linguistics Review, 

15(6), 23732396. https://doi.org/10.1515/applirev-2022-0093  

Burkhard, M. (2022). Student perceptions of AI-powered writing tools: Towards individualized 

teaching strategies. In Proceedings of the 19th International Conference on Cognition and 

Exploratory Learning in Digital Age (CELDA 2022) (pp. 73–81). IADIS Press. 

https://eric.ed.gov/?id=ED626893  

Campbell, L. O., & Cox, T. D. (2024). Utilizing Generative AI in Higher Education Teaching and 

Learning. Journal of the Scholarship of Teaching and Learning, 24(4), 162-173. 

https://doi.org/10.14434/josotl.v24i4.36575  

Chen, X., Zhou, X., & Goh, Y. S. (2026). How Chinese as a foreign language learners use generative 

AI for oral script-writing: A qualitative perspective on cognitive scaffolding in project-based 

learning. Acta Psychologica, 262, 106071. https://doi.org/10.1016/j.actpsy.2025.106071  

Chen, Y. (2024). Exploring the impact of ChatGPT on Chinese students’ foreign language anxiety 

in EMI higher education. Journal of Education, Humanities and Social Sciences, 39, 347–360. 

https://doi.org/10.54097/psy0yy79 

Choi, W. (2025). The role of changes in pronunciation ability and anxiety through Gen-AI on EFL 

learners’ self-directed speaking motivation and social interaction confidence: A CHAT 

perspective. System, 133, 103788. https://doi.org/10.1016/j.system.2025.103788  

Chuang, P.-L., & Yan, X. (2025). Language assessment in the era of generative artificial intelligence: 

Opportunities, challenges, and future directions. System, 134, 103846. 

https://doi.org/10.1016/j.system.2025.103846  

Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration 

through various case studies in teacher professional development. Computers and Education 

Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178 

https://doi.org/10.18178/ijiet.2025.15.7.2348
https://www.researchgate.net/publication/380465714
https://doi.org/10.30564/fls.v6i5.6975
https://doi.org/10.1515/applirev-2022-0093
https://eric.ed.gov/?id=ED626893
https://doi.org/10.14434/josotl.v24i4.36575
https://doi.org/10.1016/j.actpsy.2025.106071
https://doi.org/10.54097/psy0yy79
https://doi.org/10.1016/j.system.2025.103788
https://doi.org/10.1016/j.system.2025.103846
https://doi.org/10.1016/j.caeo.2024.100178


ISSN 2621-6485 English Language Teaching Educational Journal 174 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

Dizon, G., & Gayed, J. M. (2024). A systematic review of Grammarly in L2 English writing contexts. 

Cogent Education, 11(1), Article 2397882. https://doi.org/10.1080/2331186X.2024.2397882  

Gayed, J. M. (2025). Educators’ perspective on artificial intelligence: equity, preparedness, and 

development. Cogent Education, 12(1), Article 2447169. 

https://doi.org/10.1080/2331186X.2024.2447169  

George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference 

(4th ed.). Boston: Allyn & Bacon. 

Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical 

Thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006  

Hashem Mahmoud Muslim al-Zyoud (2020). The Role of Artificial Intelligence in Teacher 

Professional Development. Universal Journal of Educational Research, 8(11B), 6263–6272. 

http://doi:10.13189/ujer.2020.082265     

He, G. (2025). Predicting learner autonomy through AI-supported self-regulated learning: A social 

cognitive theory approach. Learning and Motivation, 92, 102195. 

https://doi.org/10.1016/j.lmot.2025.102195  

Hillman, N. (2025, February 26). Student generative AI survey 2025. Higher Education Policy 

Institute (HEPI). https://www.hepi.ac.uk/2025/02/26/student-generative-ai-survey-2025/  

Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial Intelligence in Education. Globethics 

Publications, 621–653. https://doi.org/10.58863/20.500.12424/4276068 

Huang, Y. K., Chang, M., & Liu, S. (2025). Taiwanese high school students’ perspectives on artificial 

intelligence and its applications. Computers in Human Behavior Reports, 17, 100550. 

https://doi.org/10.1016/j.chbr.2024.100550  

Humanika, E. S. & Radjaban, R, Y. (2024). Shaping the future of translation careers: Student interest 

and the need for curriculum reform in the AI Era. English Language Teaching Educational 

Journal, 7(3), 139-149. https://doi.org/10.12928/eltej.v7i3.12016 

Kwok, A. P. K., Wong, Y. H., Wong, K. C., & Chan, C. H. (2024). AI Meeting Assistants in English-

medium university lectures in Hong Kong, China: A double-edged sword for student 

perception. International Journal of Information and Education Technology, 14(9), 1271–

1276. https://doi.org/10.18178/ijiet.2024.14.9.2156 

Lin, L., & Tsou, W. (2025). Technology-mediated online EMI professional development: 

Developing faculty self-efficacy and teaching practice through the community of inquiry 

framework. System, 133, 103727. https://doi.org/10.1016/j.system.2025.103727  

Liu, H., & Fan, J. (2025). AI-Mediated Communication in EFL Classrooms: The Role of Technical 

and Pedagogical Stimuli and the Mediating Effects of AI Literacy and Enjoyment. European 

Journal of Education, 60(1), e12813. https://doi.org/10.1111/ejed.12813  

Liu, S., Guo, D., Sun, J., Yu, J., & Zhou, D. (2020). MapOnLearn: The use of maps in online learning 

systems for education sustainability. Sustainability,12(17), 

https://doi.org/10.33902/su12177018  

Lu, J, Zheng R, Gong Z, & Xu H. (2024). Supporting teachers’ professional development with 

generative AI: the effects on higher-order thinking and self-efficacy. IEEE Trans Learn 

Technol 17:1279 1289. https://doi.org/10.1109/tlt. 2024.3369690  

Moorhouse, B. L. (2024). Generative artificial intelligence and ELT. ELT Journal, 78(4), 378–392. 

https://doi.org/10.1093/elt/ccae032  

Moorhouse, B. L., & Wong, K. M. (2025). Generative Artificial Intelligence and Language 

Teaching. Cambridge University Press. https://doi.org/10.1017/9781009618823  

 

https://doi.org/10.1080/2331186X.2024.2397882
https://doi.org/10.1080/2331186X.2024.2447169
https://doi.org/10.3390/soc15010006
http://doi:10.13189/ujer.2020.082265
https://doi.org/10.1016/j.lmot.2025.102195
https://www.hepi.ac.uk/2025/02/26/student-generative-ai-survey-2025/
https://doi.org/10.58863/20.500.12424/4276068
https://doi.org/10.1016/j.chbr.2024.100550
https://doi.org/10.12928/eltej.v7i3.12016
https://doi.org/10.18178/ijiet.2024.14.9.2156
https://doi.org/10.1016/j.system.2025.103727
https://doi.org/10.1111/ejed.12813
https://doi.org/10.33902/su12177018
https://doi.org/10.1109/tlt.%202024.3369690
https://doi.org/10.1093/elt/ccae032
https://doi.org/10.1017/9781009618823


175 English Language Teaching Educational Journal   ISSN 2621-6485 

         Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

Moorhouse, B. L., Wan, Y., Wu, C., Wu, M., & Ho, T. Y. (2025). Generative AI tools and 

empowerment in L2 academic writing. System, 133, 103779. 

https://doi.org/10.1016/j.system.2025.103779  

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., &, Chu S. K. W. (2023). Teachers’ AI digital 

competencies and twenty-first century skills in the postpandemic world. Educational 
Technology Research & Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-

10203-6  

Ng, D. T. K., Xinyu, C., Lok Leung, J. K., & Wah Chu, S. K. (2024). Fostering students' AI literacy 

development through educational games: AI knowledge, affective and cognitive engagement. 

Journal of Computer Assisted Learning, 40(5), 2049-2064. https://doi.org/10.1111/jcal.13009  

Nga, L. T. T., & Ha, D. H. (2024). Using artificial intelligence to enhance students' vocabulary in 

higher education. Journal of Contents Computing, 6(1), 1–11. 

https://doi.org/10.9728/jcc.2024.06.6.1.1  

Nguyen, H. T., & Nguyen, P. T. (2025). ChatGPT in Language Education: Applications and 

Implications for Teaching and Learning. Educational Process: International Journal, 18, 

e2025464. https://doi.org/10.22521/edupij.2025.18.464    

Nguyen, T. H. M. (2025). Factors affecting the use of ChatGPT in academic writing perceived by 

English-majored students. VNU Journal of Foreign Studies, 41(3), 110–125. 

https://doi.org/10.63023/2525-2445/jfs.ulis.5519 

Nguyen, T. T. P. (2023). Factors affecting the use of ChatGPT in academic writing perceived by 

English-majored students. VNU Journal of Foreign Studies, 39(4), 47–59. 

https://doi.org/10.25073/2525-2445/vnufs.4763 

Ozfidan, B., El-Dakhs, D. A. S., & Alsalim, L. A. (2024). The use of AI tools in English academic 

writing by Saudi undergraduates. Contemporary Educational Technology, 16(4), ep527. 

https://doi.org/10.30935/cedtech/15013     

Rahimi, A. R., Sheyhkholeslami, M., & Mahmoudi Pour, A. (2025). Uncovering personalized L2 

motivation and self-regulation in ChatGPT-assisted language learning: A hybrid PLS-SEM-

ANN approach. Computers in Human Behavior Reports, 17, 100539. 

https://doi.org/10.1016/j.chbr.2024.100539  

Salido, A., Syarif, I., Sitepu, M. S., Suparjan, Wana, P. R., Taufika, R., & Melisa, R. (2025). Artificial 

intelligence and critical thinking in higher education: A bibliometric and systematic review. 

Social Sciences & Humanities Open, 12, 101924. https://doi.org/10.1016/j.ssaho.2025.101924  

Shalevska, E. (2024). The future of political discourse: AI and media literacy education. Journal of 

Legal and Political Education, (1), 50-61. https://doi.org/10.47305/JLPE2411050sh  

Tang, B., Nie, S., Liang, Y., Xu, C., & Li, J. (2025). Gender differences in engagement with 

generative AI tools in academic contexts. PNAS Nexus, 4(2), pgae591. 

https://doi.org/10.1093/pnasnexus/pgae591  

Tarisayi, K., & Manhibi, R. (2025). Revolutionizing Education in Zimbabwe: Stakeholder 

Perspectives on Strategic AI Integration. Journal of Learning and Teaching in Digital Age, 

10(1), 87-93. https://doi.org/10.53850/joltida.1493508  

Thangthong, P., Phiromsombut, J., & Imsa-ard, P. (2024). Navigating AI writing assistance tools: 

Unveiling the insights of Thai EFL learners. THAITESOL Journal, 37(1), 111–131. 

Torun, F. & Ozer Sanal, S. (2025). The perspectives of academicians and students regarding the use 

of generative artificial intelligence in higher education. International Journal of Technology in 

Education (IJTE), 8(1), 65-87. https://doi.org/10.46328/ijte.883    

Tran, T. N. M. (2025). Factors affecting the use of ChatGPT in academic writing perceived by 

English-majored students. VNU Journal of Foreign Studies, 41(3), 110–125. 

https://doi.org/10.63023/2525-2445/jfs.ulis.5519 

https://doi.org/10.1016/j.system.2025.103779
https://doi.org/10.1007/s11423-023-10203-6
https://doi.org/10.1007/s11423-023-10203-6
https://doi.org/10.1111/jcal.13009
https://doi.org/10.9728/jcc.2024.06.6.1.1
https://doi.org/10.22521/edupij.2025.18.464
https://doi.org/10.63023/2525-2445/jfs.ulis.5519
https://doi.org/10.25073/2525-2445/vnufs.4763
https://doi.org/10.30935/cedtech/15013
https://doi.org/10.1016/j.chbr.2024.100539
https://doi.org/10.1016/j.ssaho.2025.101924
https://doi.org/10.47305/JLPE2411050sh
https://doi.org/10.1093/pnasnexus/pgae591
https://doi.org/10.53850/joltida.1493508
https://doi.org/10.46328/ijte.883
https://doi.org/10.63023/2525-2445/jfs.ulis.5519


ISSN 2621-6485 English Language Teaching Educational Journal 176 

 Vol. 8, No. 3, December 2025, pp. 164-176 

 Tien, C. Y., & Haji-Othman, N. A. (Exploring AI-mediated linguistic and cognitive support for non-native…..) 

Trošelj, D. B., Maričić, S., & Ćurić, A. (2024). Growing Interest in AI in Education: Systematic 

Literature Review. In M. Shelley & O. T. Ozturk (Eds.), Proceedings of ICRES 2024-- 
International Conference on Research in Education and Science (pp. 2410-2427), Antalya, 

Turkiye. ISTES. 

Tsou, W., Lin, A. M. Y., & Chen, F. (2024). Co-journeying with ChatGPT in tertiary education: 

identity transformation of EMI teachers in Taiwan. Language, Culture and Curriculum, 37(4), 

529–543. https://doi.org/10.1080/07908318.2024.2362326  

van Wyk, M. M. (2025). Student Teachers’ Leveraging GenAI Tools for Academic Writing, Design, 

and Prompting in an ODeL Course. Open Praxis, 17(1), pp. 95–107. https://doi.org/10.55982/ 

openpraxis.17.1.711  

Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning 

perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social 

Sciences Communications, 12(1), 1-21. https://doi.org/10.1057/s41599-025-04787-y  

Wang, X., Gao, Y., Wang, Q., & Zhang, P. (2025). Fostering Engagement in AI-Mediate Chinese 

EFL Classrooms: The Role of Classroom Climate, AI Literacy, and Resilience. European 

Journal of Education, 60(1), e12874. https://doi.org/10.1111/ejed.12874  

Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning 

achievement, L2 motivation, and self-regulated learning. Front. Psychol. 14:1261955. 

http://doi: 10.3389/fpsyg.2023.1261955  

Xu, Q. (2024). Action research plan: a methodology to examine the impact of artificial intelligence 

(AI) on the cognitive abilities of university students. Discov Educ 3, 224. 

https://doi.org/10.1007/s44217-024-00330-4  

Zawacki-Richter, O., Marín, V. I., Bond, M. & Gouverneur, F. (2019). Systematic review of research 

on artificial intelligence applications in higher education – where are the educators? 

International Journal of Educational Technology in Higher Education, 16(1), 1–27. 

https://doi.org/10.1186/s41239-019-0171-0 

Zhou, R., He, X., Fan, Q., Li, Y., Li, Y., Xiao, X., & Fang, J. (2025). Exploring ChatGPT-Facilitated 

Scaffolding in Undergraduates' Mathematical Problem Solving. Journal of Computer Assisted 

Learning, 41(4), e70077. https://doi.org/10.1111/jcal.70077  

https://doi.org/10.1080/07908318.2024.2362326
https://doi.org/10.1057/s41599-025-04787-y
https://doi.org/10.1111/ejed.12874
https://doi.org/10.1007/s44217-024-00330-4
https://doi.org/10.1186/s41239-019-0171-0
https://doi.org/10.1111/jcal.70077

	1. Introduction
	2. Method
	2.1. Participants
	2.2. Data Collection Technique
	2.3. Data analysis

	3. Finding and Discussion
	3.1 Findings
	1) Gender
	2) International vs. Local Students
	3) Academic Major
	4) Year-of-Study

	3.2. Discussion
	3.3. Limitations

	4. Conclusion
	Acknowledgment


