1. Introduction
The integration of artificial intelligence (AI) tools like ChatGPT in education has garnered significant attention, particularly for its transformative potential in enhancing teaching and learning experiences. While AI technologies such as ChatGPT have shown promise across various disciplines, their application in English as a Foreign Language (EFL) education remains a developing area of research. ChatGPT, developed by OpenAI, is especially notable for its ability to generate human-like language and provide personalized interactions, making it a versatile tool for supporting language acquisition, academic writing, and interactive feedback (Costello, 2024; Dalalah & Dalalah, 2023; Lim et al., 2023; Monica & Suganthan, 2024).
Although there is growing interest in how ChatGPT can be used in educational settings, existing research on its pedagogical implications in EFL education has predominantly relied on qualitative methods such as case studies and surveys. These studies have explored ChatGPT’s ability to engage students, provide personalized feedback, and assist in writing tasks (Ali et al., 2023; Li et al., 2024; Won et al., 2023), but there remains a significant gap in quantitative, data-driven approaches to understanding its full potential in educational contexts. Additionally, while concerns about ethical issues and technology access disparities have been raised, these aspects have not been the primary focus of current research on ChatGPT’s use in language education (Al-khresheh, 2024; Baskara, 2023; Costello, 2024; Dalalah & Dalalah, 2023).
This study seeks to address these gaps by adopting a data-driven approach to systematically analyze research articles on the use of ChatGPT in EFL education. By leveraging machine learning (ML) algorithms and natural language processing (NLP) techniques, this study will explore patterns, trends, and relationships in existing literature, offering a nuanced, quantitative understanding of ChatGPT’s applications, opportunities, and challenges in teaching English writing.
The research questions guiding this study are as follows:
1. What are the most frequently used words associated with ChatGPT in EFL language learning literature? This question aims to identify the key themes and areas of focus in the existing research by analyzing the frequency of words related to ChatGPT.
2. What are the semantic relationships between ChatGPT and other related terms in the context of language learning? This question addresses the role of ChatGPT in EFL education by examining the word similarity analysis, highlighting the terms most commonly associated with ChatGPT.
3. How can ChatGPT-related terms be classified into distinct clusters based on their semantic similarity? This question explores the use of Principal Component Analysis (PCA) and K-means clustering to group related terms, offering insights into the different ways ChatGPT is utilized in EFL education, such as in writing, feedback, and research.
The findings from this study are expected to provide valuable insights for educators, researchers, and policymakers seeking to integrate ChatGPT effectively in EFL classrooms. This research will contribute to the growing body of literature on AI in language education by presenting a data-driven analysis that highlights best practices, identifies gaps in current understanding, and proposes strategies for the effective use of ChatGPT in EFL settings. Through this approach, the study aims to deepen the understanding of ChatGPT’s role in education and its potential to enhance the language learning process.
2. Literature Review
Artificial intelligence (AI) and its impact on education is a topic of growing interest and concern among researchers. Several studies demonstrate the complexity of using AI chatbots like ChatGPT in education and highlight the need for further research to ensure their safe and responsible adoption. For example, Tlili et al. (2023) conducted a qualitative instrumental case study that examines the use of ChatGPT in educational settings. Their findings indicate enthusiasm regarding ChatGPT’s potential despite concerns associated with cheating, privacy, and manipulation. Costello (2023) raises questions about the efficacy of AI chatbots in teaching and learning and argues that ChatGPT can be used for assessment and surveillance. Rospigliosi (2023) emphasizes ChatGPT’s potential in interactive learning environments and the importance of asking questions and exploring its role. The author also acknowledges concerns about ChatGPT misuse, such as students using it to write essays, and calls for further inquiry into the ethical implications and potential risks associated with its use. Rejeb et al. (2024) also identified issues related to academic integrity, such as plagiarism and cheating, arising from the use of AI-powered tools like ChatGPT in education. These findings bring ethical concerns to the forefront, particularly around responsible AI use and data privacy, emphasizing the importance of institutions establishing clear guidelines and policies for integrating AI tools into educational practices.
In English language education, ChatGPT has emerged as a transformative tool, particularly in the context of English as a Foreign Language (EFL) teaching. Generative AI tools like ChatGPT have redefined digitalized writing practices, combining traditional approaches with advanced technologies to enhance engagement and efficiency. Barrot (2023) highlights that ChatGPT provides instant feedback, tailored prompts, and iterative writing support, enabling EFL learners to refine their writing skills systematically. Strobl et al. (2019) note that digitalized writing integrates multimedia elements and interactive platforms, creating dynamic learning environments. ChatGPT supports these efforts by assisting students at various stages of the writing process, from brainstorming and drafting to revising and editing, while also promoting linguistic accuracy and creativity (Zhao et al., 2025).
Despite its potential, integrating ChatGPT into EFL instruction presents challenges. Baskara (2023) underscore ethical concerns such as over-reliance on AI, academic dishonesty, and data privacy risks. Educators must strike a balance between leveraging ChatGPT’s capabilities and fostering foundational writing skills. Phuong (2024) argues that AI literacy is essential in this context, encompassing critical thinking, ethical considerations, and the ability to evaluate AI-generated outputs effectively. This highlights the importance of equipping both educators and learners with the skills necessary to navigate AI-enhanced educational tools responsibly.
Similarly, Tang (2024) reviewed existing studies on the role of ChatGPT in English writing within EFL environments and revealed that integrating artificial intelligence (AI) into education is a continuous process that significantly influences how EFL students learn to write. While ChatGPT offers an innovative approach to improving writing skills through instant feedback, Teng also argued that challenges such as dependency on AI and the need to develop critical thinking skills persist. Therefore, the study emphasized the importance of a balanced approach to integrating AI tools like ChatGPT into writing curricula and highlighted the need for practical implementation by teachers and students in this direction.
Meanwhile, ChatGPT’s applications extend beyond English education into fields like medical education, where it has gained attention for its potential in clinical practice and research. Cascella et al. (2023) explore ChatGPT’s performance in healthcare settings, including its support for clinical reasoning and public health discussions. They demonstrate its accuracy in addressing specific medical queries, while Huh (2023) finds that ChatGPT’s reasoning capabilities remain below those of Korean medical students. These findings underscore the need for careful integration and clear boundaries when using AI in specialized educational contexts.
In scholarly publishing, ChatGPT’s ability to transform content creation has been widely discussed. Curtis (2023) emphasizes its potential for academic writing, while Fernandez (2023) addresses controversies such as biases and misinformation. Hu (2024) highlights the difficulty of distinguishing AI-generated content in academic papers, and Perkins (2023) calls for updated integrity policies to address the growing use of AI tools. Tang (2024) suggests that academic journals specify the percentage of NLP-generated content in submissions to ensure transparency and uphold ethical standards.
ChatGPT’s role in assessment has also raised concerns. Studies by Graham (2022) and Al-khresheh (2024) highlight the challenges posed by ChatGPT’s ability to generate high-quality text, which complicates the detection of plagiarism and academic dishonesty. Dalalah and Dalalah (2023) emphasize the need for educating students and faculty on responsible AI use to maintain academic integrity. Ali et al. (2023) found ChatGPT to be a useful tool for motivating reading and writing improvements in foreign language learning, though attitudes toward its impact on listening and speaking were neutral. These findings suggest a pressing need for updated assessment methods and institutional policies that address the evolving landscape of AI in education.
In summary, the literature highlights ChatGPT’s transformative potential in education, particularly in enhancing writing skills and engagement in EFL contexts. However, challenges related to ethical implementation, academic integrity, and equitable access necessitate updated pedagogical strategies and policies. These insights provide a foundation for further research into ChatGPT’s role in teaching, learning, and evaluation practices.
3. Method
This study utilizes a systematic data-driven approach to analyze 20 research articles on the applications of ChatGPT in English writing education. The methodology involves several stages, including data preparation, text processing, machine learning-based analysis, and visualization, all aimed at extracting meaningful patterns, trends, and insights from the literature.
3.1. Data Preparation
The dataset for this study consists of 20 academic articles selected from Google Scholar using the search term “ChatGPT applications in English learning.” The search was conducted in January 2025, and only articles published in English between 2022 and 2024 were considered, as this time frame corresponds to the period following ChatGPT’s public release and widespread adoption.
A total of 36 articles were initially retrieved. To ensure relevance, the initial screening involved a review of titles and abstracts to identify studies focusing on English writing education, reducing the pool to 29 articles. This was followed by a full-text review to confirm alignment with the study’s objectives. The inclusion criteria were as follows:
(1) the article must directly address the use of ChatGPT in English writing instruction or learning;
(2) it must present empirical findings or theoretical analysis relevant to the topic; and
(3) it must be published in a journal indexed in academic databases such as Web of Science (WoS), Scopus, or ERIC.
After this filtering process, 20 articles were selected. Each was converted into plain text format to enable further text processing in the subsequent stages of analysis, using a combination of manual copying by the present researcher and OCR (via Google Docs), followed by a cleaning step to remove formatting inconsistencies.
3.2. Text Processing
In the first stage of text processing, tokenization was performed to break the text into smaller, manageable units called tokens, such as words or phrases. For example, the sentence “ChatGPT enhances writing feedback efficiency” would be tokenized as [ChatGPT, enhances, writing, feedback, efficiency].
Following tokenization, lemmatization was applied to reduce words to their base or root form. This step is essential for maintaining consistency in analysis by ensuring that word variations are treated as a single item. For example, “studying” is lemmatized to “study,” and “supports” to “support.” This process was conducted using AntConc 4.3.1, leveraging its tokenization options and lemmatization criteria.
Next, stop words—common words that provide little analytical value—were removed from the corpus. These include words like “the,” “is,” and “of,” as well as context-specific stop words like “www” and “https.” The NLTK (Narural Language Toolkit) stop word list was used as a reference for this process, and custom filters were applied to exclude any additional irrelevant terms specific to the research context.
3.3. Analysis and Information Extraction
Once the text was processed, word frequency analysis was conducted to identify the most commonly occurring words in the corpus. This analysis revealed key themes related to ChatGPT in the literature, such as “student,” “learn,” “education,” and “AI.” The frequency analysis was performed using Python and the NLTK library, which enabled the extraction of frequency lists and insights into the central topics discussed.
In addition, a cosine similarity analysis was carried out to measure the semantic relationships between words in the corpus. Words that frequently co-occurred or were contextually related were grouped based on their proximity in the text. This allowed for the identification of clusters of words that are closely associated with ChatGPT’s role in English learning. The similarity analysis was executed using Python and Scikit-learn, which provided tools for computing cosine similarity based on word embeddings generated by Word2vec.
Finally, clustering and classification techniques were employed to group related terms into meaningful clusters. PCA was used to reduce the dimensionality of the data, while K-means clustering was applied to categorize the most frequent terms into distinct clusters, such as “AI in Education,” “Writing and Feedback,” and “Language Learning.” This clustering process was performed using Python and additional custom scripts for statistical analysis.
3.4. Visualization
To better interpret the results, visualizations were created. A word cloud was generated from the frequency analysis results, with larger words representing higher frequency terms. Additionally, scatter plots were used to visualize the clustering results, showing how terms related to ChatGPT were grouped based on semantic similarity.
In sum, for the initial text processing tasks, tokenization, lemmatization, and stop word removal were performed based on AntConc 4.3.1 lemmatization criteria and the NLTK stop word list. The subsequent analysis, including word frequency analysis, cosine similarity, and clustering, was conducted using Python and related libraries. The following tools were used in this study: Python (For performing word frequency analysis, similarity analysis, clustering, and PCA); NLTK (For stop word removal and custom filtering); Scikit-learn (For clustering analysis and PCA); Matplotlib and Seaborn (For data visualization, including word clouds and scatter plots).
4. Results
4.1. Most Frequent Words
The frequency of commonly used words in the processed text data is depicted in Figure 1, with ChatGPT emerging as the most frequently occurring word, followed by students, learning, AI (artificial intelligence), and education. These terms reflect the central themes of the document, emphasizing the role of ChatGPT in educational settings.
A key observation from this analysis is the prominence of terms related to technological integration and student support. For instance, the frequent occurrence of the words “student” and “learning” suggests that ChatGPT is closely associated with enhancing educational experiences. Similarly, the frequent use of “tool” highlights ChatGPT’s perception as a resource for improving academic performance, particularly in the context of language learning, writing, and classroom engagement.
Moreover, the strong presence of terms such as “language,” “writing,” and “teacher” suggests that ChatGPT is actively positioned as a collaborative aid in English language education, rather than merely a passive tool. This reflects a shift toward integrating AI not just as a technological supplement, but as a participant in instructional practices—particularly in writing support and language development.
Additionally, terms like technology and AI underscore the focus on integrating artificial intelligence tools into education. ChatGPT’s advanced capabilities have enabled its use by students and educators to streamline learning processes and foster academic development. These findings align with the growing recognition of AI as a transformative force in education, providing personalized assistance for tasks such as essay writing, research, and language acquisition.
The prevalence of words such as “potential” suggests that while ChatGPT has already made significant inroads into education, its applications are still evolving. This points not only to its current usefulness but also to ongoing opportunities for innovation in leveraging ChatGPT to enhance educational practices.
Finally, a word cloud (Figure 1) is provided to offer a broader visual representation of the frequently occurring terms. Larger and bolder words in the word cloud indicate higher frequency, capturing the overall focus of the document on ChatGPT’s educational impact.
4.2. Word Similarity
The word similarity analysis in Table 1, based on cosine similarity, provides insights into the terms most closely associated with “ChatGPT” in the dataset. This analysis highlights ChatGPT’s role and applications in educational and technological contexts.
<Table 1>
Word Similarity (top 20 most frequent terms)
The analysis reveals strong associations between “ChatGPT” and terms like “student”, “learning”, and “education”. These terms indicate that ChatGPT is widely utilized to support students in their educational journeys, particularly in language learning and personalized instruction. This highlights its significance as a tool for fostering engagement and improving academic outcomes.
Furthermore, terms such as “writing”, “skill”, and “feedback” emphasize ChatGPT’s practical applications in education. These associations suggest that ChatGPT is frequently employed for academic writing assistance, skill development, and providing interactive, tailored feedback to learners. Its role in improving communication and writing abilities is particularly notable.
Additionally, the presence of terms like “AI” and “technology” reflects ChatGPT’s identity as an advanced AI tool. These terms underscore its technological foundation and its growing integration into modern educational practices. ChatGPT is not just a tool for students but also a resource that educators and researchers can leverage for innovative teaching and research methodologies.
Lastly, terms such as “research”, “study”, and “language” demonstrate ChatGPT’s versatility in academic contexts. It supports both learners and researchers, aiding in tasks such as language acquisition, scholarly research, and data-driven studies.
In conclusion, the word similarity analysis showcases ChatGPT’s multifaceted contributions to education. From enhancing writing skills to providing personalized feedback and supporting research, ChatGPT is emerging as a transformative tool in modern teaching and learning environments. Its ability to seamlessly integrate technology into education highlights its potential to revolutionize traditional methods and promote innovative learning experiences.
4.3. PCA Word Classification
This visualization shows the results of a PCA-based dimensionality reduction of word vectors, followed by K-means clustering applied to the top 25 most frequent terms. The scatter plot presents these words in a 2D space, with each word indicating the cluster it belongs to. The classification results are illustrated in Figure 2, with the first principal component represented on the horizontal axis and the second principal component on the vertical axis. Using the PCA model, the results can be categorized into four main groups.
4.3.1. AI and Technology in Education
This cluster includes words like “ai”, “technology”, “tool”, and “chatgpt”, highlighting the integration of AI technologies in English learning. These words reflect how AI-driven tools like ChatGPT are transforming educational practices, enhancing learning experiences, and providing new ways for educators and students to engage with technology. The terms in this cluster emphasize AI’s growing role in reshaping teaching methodologies and promoting innovative approaches in education.
4.3.2. Research and Data Utilization
Words such as “research”, “study”, “data”, and “base” appear in this cluster, signifying the academic and research-focused applications of ChatGPT. These terms illustrate how ChatGPT can be used as a resource in academic contexts, helping researchers gather information, analyze data, and generate new insights. The presence of terms like “study” and “data” suggests that ChatGPT is valued for its ability to support data-driven research and knowledge discovery in educational settings.
4.3.3. Writing and Feedback
In this cluster, words such as “writing”, “learner”, “feedback”, and “skill” are grouped together, emphasizing ChatGPT’s role in enhancing writing abilities and providing feedback. These terms show that ChatGPT is frequently used to assist students with writing tasks, offering suggestions, corrections, and guidance. The cluster also reflects the tool’s potential in helping learners improve their writing skills and providing a personalized learning experience in educational environments.
4.3.4. Language Learning and Education
This cluster includes terms such as “language”, “learning”, English”, and “student”, pointing to ChatGPT’s significant role in language learning. These words indicate that ChatGPT is often used to support English language learners, offering tools for vocabulary enhancement, language comprehension, and writing practice. The cluster underscores ChatGPT’s value in language acquisition, particularly in educational settings where language proficiency is essential.
The PCA and K-means clustering analysis demonstrates that ChatGPT’s applications in education are diverse, ranging from AI-enhanced learning tools to language learning and academic research. Each cluster highlights a distinct facet of its utility, suggesting that ChatGPT serves as an indispensable resource across different educational contexts. Its ability to assist in writing, provide personalized feedback, and support data-driven research further exemplifies its transformative potential in reshaping modern education.
5. Conclusion
This study has examined ChatGPT’s transformative role in education through the lens of word frequency, similarity analysis, and clustering based on PCA and K-means clustering. The results illustrate that ChatGPT is not just a tool for students but a multifaceted resource that significantly enhances various aspects of the educational process, from language learning and writing support to academic research and student engagement. Its integration into educational environments marks a clear shift toward more personalized, technology-enhanced learning experiences.
The frequency analysis revealed that ChatGPT is central to discussions around modern education, frequently associated with key terms such as “student,” “learning,” “education,” and “AI.” These findings emphasize ChatGPT’s role in supporting educational practices by offering personalized learning experiences, streamlining tasks like essay writing, language acquisition, and research. Moreover, terms like “tool” and “feedback” suggest that ChatGPT is perceived as a critical resource in helping students improve their academic performance, especially in areas where personalized attention is often limited.
The word similarity analysis further highlighted ChatGPT’s significant contributions to education. Strong associations with terms such as “writing,” “skill,” and “feedback” underscore its role in supporting academic writing and skill development, with a particular focus on language learning and writing proficiency. In addition, the links to terms like “research,” “study,” and “data” point to ChatGPT’s role in assisting with data-driven tasks, academic research, and scholarly inquiry. These findings show that ChatGPT’s utility extends beyond writing assistance and enters the realm of academic support, offering value to both students and educators engaged in more research-oriented tasks.
Through the PCA and K-means clustering analysis, the study identified four distinct clusters that reflect different areas of ChatGPT’s utility in education. The first cluster, “AI and Technology in Education,” emphasizes the growing role of artificial intelligence in reshaping teaching and learning. ChatGPT, as an AI-driven tool, facilitates personalized learning, enhances classroom engagement, and transforms how educators deliver content. The second cluster, “Research and Data Utilization,” highlights ChatGPT’s effectiveness in assisting with academic research, data analysis, and knowledge generation. This cluster underscores its potential to assist researchers in navigating large datasets and uncovering insights that might otherwise be overlooked. The third cluster, “Writing and Feedback,” reflects ChatGPT’s essential role in improving students’ writing skills by providing real-time feedback, guidance, and support for writing tasks. The fourth cluster, “Language Learning and Education,” shows the critical role ChatGPT plays in supporting language acquisition, particularly for English language learners, by providing tools for vocabulary enhancement, comprehension, and practice.
Personalized learning represents another promising direction for future research. ChatGPT’s potential to offer adaptive learning experiences based on individual progress, preferences, and learning styles could be further explored. By tailoring its feedback and suggestions to suit the specific needs of each student, ChatGPT could provide even more effective educational support. Researchers could also explore how ChatGPT can be integrated with other educational tools, such as learning management systems (LMS) and virtual classrooms, to create a more holistic and adaptive learning ecosystem.
Despite these promising findings, there are several areas that warrant further exploration to fully understand ChatGPT’s impact on education. Future research should consider its long-term effects on student outcomes. Studies could explore whether consistent use of ChatGPT improves academic performance over time, specifically in writing, language proficiency, and critical thinking. Another area for further investigation is the role of ChatGPT in different academic disciplines. While this study focused on its general applications, research could explore its specific contributions to fields like business education, STEM, and the humanities. This would provide deeper insights into how ChatGPT can be tailored to support various learning contexts and subject-specific needs.
Ethical considerations and pedagogical challenges also require attention. As AI tools like ChatGPT become more integrated into educational practices, questions about data privacy, academic integrity, and the potential for AI to reinforce biases must be addressed. Future research could explore how educators can use ChatGPT effectively while maintaining the integrity of educational practices, promoting independent learning, and avoiding over-reliance on AI. Additionally, further investigation into how ChatGPT can support diverse learning needs and adapt to different learning styles will be essential to ensuring that it serves all students equitably.
In addition to these areas, the study also raises important questions regarding how ChatGPT can be effectively integrated to support diverse learning styles and promote an inclusive, personalized educational experience. The presence of terms like “writing”, “skill”, and “feedback” in the analysis suggests that ChatGPT can serve a variety of learners by providing customized content and real-time support. However, there may still be limitations in its application, particularly in dealing with more complex, open-ended inquiries, which warrants further investigation.
Furthermore, the increasing use of ChatGPT in education opens up a broader discussion about how AI-driven tools, like ChatGPT, can be used responsibly. Future research should focus on developing strategies to address these challenges, ensuring that AI tools are used ethically and that their integration into educational settings does not undermine core educational values. It is essential to understand how AI-driven tools can complement and enhance traditional teaching methods while preserving academic integrity and supporting critical thinking.
In conclusion, ChatGPT has proven itself to be a valuable tool in enhancing educational practices. Its ability to support writing, provide personalized feedback, assist with academic research, and facilitate language learning positions it as a powerful resource in modern education. However, as its use in educational settings grows, further research will be essential to maximize its potential, address the challenges it presents, and ensure its ethical and effective integration into learning environments. Through continued exploration and adaptation, ChatGPT can play a central role in shaping the future of education, making learning more accessible, personalized, and innovative.
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