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Abstract

The latest developments in artificial intelligence (AI) have paved the way for new AI-integrated pedagogical skills and competencies in language teaching. AI literacy and AI competency are related terms; however, they differ in educational contexts. AI literacy refers to the skills to understand and evaluate AI technologies, as well as to engage with them. In contrast, AI competency extends beyond conceptual proficiency and is related to the practical application of AI knowledge in educational domains. Considering the significance of effective and ethical AI use in language teaching, this study focused on the role of AI literacy training in teacher education programs and aimed to investigate pre-service EFL teachers’ perceptions of their AI literacy and competency as well as their experiences regarding AI literacy training in EFL teacher education programs. A small-scale exploratory approach was adopted in this qualitative study, which was conducted at the English Language Teaching Department at a state university in Türkiye. The participants were 15 pre-service EFL teachers. Semi-structured interviews were utilized to gather qualitative data, which was analyzed using thematic analysis. The study results revealed that pre-service EFL teachers’ perceptions of AI use in language teaching were positive; however, they were unconfident about their AI knowledge and skills. Also, the study enlightened a lack of exposure to AI education, including AI knowledge, practices, and ethical issues in EFL teacher education programs. Based on pre-service EFL teachers’ needs and expectations, the results of this study may provide beneficial insights for teacher educators, practitioners, and policymakers.

Keywords

AI literacy AI competency ChatGPT pre-service EFL teachers English language teacher education

Introduction

The swift introduction of artificial intelligence (AI) in education (An et al., 2023) has emerged diverse issues regarding AI-supported language education. Therefore, understanding language learners’ and teachers’ AI literacy and competency appears to be a pre-eminent step to embrace language teaching with AI. Initially, literacy and competency are related terms, but they may differ in educational contexts (Chiu et al., 2024), which requires reframing these two concepts. AI literacy refers to the core theoretical knowledge and abilities for the comprehension, application, and evaluation of AI technologies (Southworth et al., 2023), whereas AI competency is defined as the capacity to efficiently and constructively apply AI knowledge (Zhou et al., 2025). AI competency also includes student confidence and self-reflective mindsets to evaluate how AI tools and technologies work (Chiu et al., 2024). Recent emerging technologies may call for the need for the elicitation and adaptation of new pedagogical approaches, thereby leading-edge guidelines to improve essential skills are required for AI-integrated language teaching (Abdelrady & Akram, 2022; Popenici & Kerr, 2017). At this juncture, researchers appear to be unanimous that teachers can leverage AI to facilitate students’ learning and to improve the efficiency of their teaching (Chiu & Chai, 2020; Lesia Viktorivna et al., 2022; X.-F. Lin et al., 2022; Pokrivcakova, 2019). On the other hand, AI literacy and competency can be pivotal considerations for teachers who need to carry on instructional activities in a setting affected by the renewal of technological resources. The lack of essential AI knowledge, practices, and competencies may pose some barriers to tackle for a successful integration of emerging technologies (Geng et al., 2021). Therefore, improving AI literacy of pre-service language teachers stands as an impending endeavor, including a wide range of skill sets from the understanding level of key AI concepts to ethical considerations.

This strand of AI literacy research in educational settings has accrued a substantial body of data on in-service teachers’ perceptions of AI literacy (Lérias et al., 2024; Park et al., 2023; Zhai et al., 2024). However, research concentrating on pre-service teachers’ AI literacy and competency, particularly in the EFL context, remains restricted. The lack of research on the above-mentioned issues has been the motivation for this study, which aims to examine pre-service EFL teachers’ perceptions regarding their AI literacy and competency. Also, the current study attempts to undertake an inquiry into the potential of AI literacy and competency training in EFL teacher education programs from the perspectives of pre-service language teachers. Within the scope of this study, it is crucial to clarify the difference between AI as a general technological field and generative AI tools, particularly ChatGPT as a representative example throughout this research. Although AI literacy comprises a broader set of skills and understandings, this study attempts to conceptualize pre-service EFL teachers’ AI literacy and competence through the incorporation of generative AI tools in English language teaching (ELT). Similarly, this study is grounded on technological pedagogical content knowledge (TPACK), which can provide a theoretical framework for the understanding of pre-service EFL teachers’ perceptions of their knowledge and competencies regarding AI use in language classrooms. In pursuit of these specified research goals, the research questions are constructed as follows:

Research questions

What are pre-service EFL teachers’ perceptions of AI literacy?

What are pre-service EFL teachers’ perceptions of their AI competence?

What are pre-service EFL teachers’ perceptions of using AI-ChatGPT and such tools in their language instruction?

What are pre-service EFL teachers’ perceptions of the integration of AI literacy education into EFL teacher education programs?

Literature Review

Theoretical Framework

This research is informed by TPACK, which was first introduced by Mishra and Koehler (2006). TPACK offers a broad and inclusive model to explain the complex relationships among technological knowledge (TK), pedagogical knowledge (PK), and content knowledge (CK) for quality teaching. This framework suggests that the successful incorporation of technology into education necessitates a thorough understanding of these fundamental components in educational domains (Falloon, 2020). Considering swift developments in AI and the significance of the incorporation of AI literacy into teaching, a solid mastery of the relationships among technology, pedagogy, and content knowledge can be essential for teacher education programs. This study explores pre-service teachers’ knowledge and competencies for integrating AI-supported tools into their classrooms, with special attention to TK. TK plays an important part in TPACK and highlights the awareness of technology-related skills and difficulties. Teachers with high levels of TK can apply technological skills in their instructional tasks. Ayanwale et al. (2024) suggest that TPACK enables researchers to explore pre-service teachers’ AI literacy. In this respect, the TPACK framework functions as an effective approach to explore how pre-service EFL teachers conceptualize their knowledge and competencies regarding AI use in language classrooms, and to shed light on the niches in their technological preparedness, and thereby was employed as a theoretical framework in this study.

Artificial Intelligence Literacy and Competency

Research on digital technology has steadily confirmed that artificial intelligence (AI) in education is a far-reaching issue with its potential benefits and challenges as well as diverse impacts and consequences (Akram & Abdelrady, 2025; Eguchi et al., 2021; Zhao et al., 2022). The integration of artificial intelligence into education has led to the discussion of the essential skills, knowledge, and mindset to efficiently use AI-based tools such as ChatGPT in teaching and learning environments (Celik, 2022). Therefore, it is paramount for practitioners and scholars to illuminate the theoretical understanding of AI as a starting point.

AI was first proposed by Turing (1950), who developed a modern computational model to simulate the reasoning and thinking process of humans. This endeavor has been the basis for early AI research. Drawing from Turing’s perspective, AI refers to “computing systems that can engage in human-like processes such as learning, adapting, synthesizing, self-correction, and use of data for complex processing tasks” (Popenici & Kerr, 2017, p. 2). Extending this definition, AI enables digital tools to simulate and extend human intelligence. The knowledge of AI includes the comprehension of basic concepts and AI algorithms and the adaptation of these algorithms into educational settings (Kong et al., 2025). Among diverse subfields of AI, machine learning, deep learning, speech recognition systems, intelligent tutoring systems, and natural language processing are the technologies that uphold AI. The swift developments of AI remarkably influence all aspects of life (Chiu et al., 2024). These various tools and systems display the multidimensional structure of AI in different fields. Although this diversity is acknowledged and creates the need for more inclusive AI literacy and competence, this study specifically centers on generative AI tools in ELT due to the growing interest and accessibility of these AI tools in educational settings. In the relevant literature, many studies endeavored to conceptualize AI literacy through the use of generative AI tools such as ChatGPT (Azap, 2025; Liu, 2025).

Similarly, the increasing interest in the use of AI tools for educational purposes has resulted in the need for inquiry into AI literacy. In a broad sense, AI literacy is defined as “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace” (Long & Magerko, 2020, p. 2). Beyond understanding codes, AI literacy is a more comprehensive concept that describes the competencies starting from basic knowledge of the working systems of AI and evaluation of these systems, extending to the effective use of AI tools in different contexts. Therefore, the basic components of AI can be identified as knowledge, evaluation, collaboration, contextualization, autonomy, and ethics (Allen & Kendeou, 2024).

Chiu et al. (2024) emphasize the significance of a self-reflective mindset to AI literacy for keeping up to date with new AI technologies, defining AI competency which is “an individual’s confidence and ability to clearly explain how AI technologies work and impact society, as well as to use them in an ethical and responsible manner and to effectively communicate and collaborate with them in any setting” (p. 4). Literacy and competence are related terms, but recent studies reconceptualized these two terms differently in teacher education programs (Chiu et al., 2024; Falloon, 2020). Whereas literacy is related to knowledge and skills in using tools and systems, competency involves the application of knowledge efficiently, showcasing both confidence and self-reflective mindsets. Self-reflection in an AI context can be defined as “confidence and ability to self-reflect on their AI understanding for further learning” by Chiu et al. (2024, p. 4). Based on this definition, it can be beneficial to focus on pre-service EFL teachers’ AI competency since self-reflective mindsets are considered significant in AI education (Chiu et al., 2024). While previous studies have predominantly concentrated on pre-service teachers’ AI literacy (Ayanwale et al., 2024; Pokrivcakova, 2023; Söğüt, 2024), there is an absence of research addressing pre-service teachers’ perceptions of their confidence and abilities to self-reflect on their continuous AI understanding. Nevertheless, Falloon (2020) calls for a need to integrate competency into teacher education programs to reframe the outcomes of these programs with more varied knowledge and abilities required for future teachers. Considering this call and addressing the void in the relevant literature, this study extends beyond AI literacy in EFL teacher education programs and aims to explore pre-service EFL teachers’ perceptions of AI competency as well as AI literacy, since confidence and self-reflective mindsets can be crucial in integrating AI into their EFL teaching.

Similarly, this research underscores perceived AI literacy and competency rather than practiced literacy and competence to specifically understand how pre-service EFL teachers personally evaluate their AI skills, confidence, and self-reflective mindsets as well as AI teaching practices in EFL teacher education programs. This is because their perceptions of their AI literacy and competency may shape their engagement and motivation to learn AI skills and competencies. Their willingness to learn how to use AI in their future classrooms may encourage teacher educators to integrate AI literacy education into EFL teacher education programs, particularly when considering pre-service EFL teachers with limited practical AI experience in their teacher education programs. Also, understanding pre-service EFL teachers’ perspectives can be important to highlight pedagogical concerns posed by the rapid integration of AI into English classrooms.

AI-ChatGPT and English Language Teaching

In recent years, the swift development of AI has revolutionized the educational realm worldwide (Shaikh et al., 2023), not only promoting teaching practices but also providing a more interesting learning environment for students (Akram et al., 2022; Taskiran & Goksel, 2022). The release of ChatGPT, from these emerging technologies, has provoked increasing attention among scholars, practitioners, and researchers (Glaser, 2023). The motivation for the research on ChatGPT in educational contexts comes from a growing interest among students who use ChatGPT as a personalized learning tool (Lancaster, 2023) and among teachers using this tool either to create teaching plans and activities or to grade students’ work (Grassini, 2023). ChatGPT is a text generation tool that is a natural language processing (NLP) model. It was developed by OpenAI and was publicly launched on November 30, 2022. It gained immediate popularity with 1 million users in 5 days and 100 million users in 2 months (Hsu & Ching, 2023). ChatGPT is different from the other large language models (LLMs) with its conversational interface, which allows users to ask follow-up questions (Rawas, 2023). Also, the uniqueness of ChatGPT stems from its output quality (Lambert & Stevens, 2024), being freely available and easy to use for users from different levels of digital competence (Adeshola & Adepoju, 2024). The capabilities of ChatGPT made it one of the most impressive tools in language teaching.

ChatGPT can be applied to language studies in a variety of forms and can provide a number of opportunities for language students. For instance, it can offer individualized learning content to learners’ unique needs, expectations, and preferences. Also, language learners may benefit from ChatGPT, as an interactive tool, in receiving real-time feedback and immediate responses in their individualized learning process, since ChatGPT, with its conversational interface, can engage language learners in English conversations. Meanwhile, the demerits are apparent. With its numerous potential benefits and disadvantages, ChatGPT, beyond question, entitles ongoing discussion and research in the language teaching field, thereby warranting considerable research investigating the use of ChatGPT in language teaching from various perspectives (Alenezi et al., 2023; Javier & Moorhouse, 2023; Kovačević, 2023). Confirming this, as per Bin-Hady et al. (2023), language teachers and researchers are stimulated by ChatGPT as a contemporary digital advance to use technology for more engaging, supporting, and effective language teaching. Providing evidence for the benefits of ChatGPT in ELT, Meniado (2023) prioritizes the importance of proper AI training to effectively use the tool for their tasks and responsibilities. Kohnke et al. (2023) support the necessity for digital skills and competencies for the ethical and beneficial use of ChatGPT. Despite the potential of the integration of ChatGPT into ELT, this process may be filled with many challenges. Language teachers’ concern, which generally pops up, is whether the integration of technology into their classrooms would be compatible with the pedagogy (Al-Khresheh, 2024). For example, Akram et al. (2021) conducted a study investigating technological competencies of faculty members and found that their technological knowledge was lower than content knowledge. However, given that language teachers use ChatGPT to support and improve their instruction, it is of paramount importance to train them about the set of skills required for the ethical and responsible use of ChatGPT in language teaching (Kostka & Toncelli, 2023). In this study, ChatGPT is considered a widely used AI tool in education based on a rich body of research on ChatGPT. On the other hand, it is crucial to note that ChatGPT represents only one functional and readily available example of generative AI, whereas AI, as a broad domain, embraces numerous technologies and applications. With a focus on ChatGPT, this study intended to demonstrate a practical and representative use of AI in educational contexts.

AI Literacy in Teacher Education

Teacher education programs provide pre-service and in-service teachers with the practices to equip them with the essential pedagogical skills and competencies for their future professions (Korte et al., 2024). Considering that the swift developments of digital technologies have led language teachers to evolve toward a more technology-assisted teaching process, it is undoubtedly important to improve pre-service teachers’ AI literacy for the effective use of AI tools in education (Salas-Pilco et al., 2022). Recently, a substantial body of research has focused on AI and AI literacy in educational settings. In this popularly investigated construct, many researchers have frequently centralized their examination on pre- and in-service teachers’ perceptions of AI in education (Chounta et al., 2022; Kim & Kim, 2022), integration of AI in language teaching and learning (An et al., 2023; Karataş et al., 2024), teachers’ AI literacy and competency (Celik, 2023; Ng et al., 2023), and the like. By way of example, Jatileni et al.’s (2024) study investigated in-service teachers’ knowledge, perceptions, and tendencies toward AI for designing more effective teacher training programs. The researchers used a scale, including seven AI-related factors such as AI anxiety, readiness, relevance, and confidence, for 159 in-service teachers. The results revealed notable contributions to the understanding of the relationship between the perceptions of AI relevance, confidence, and motivation to use AI in their teaching. Teachers who found AI relevant to their teaching practices and were confident in their AI knowledge had positive perceptions of AI use in the classroom.

Existing studies collectively expounded the applicability of AI in language learning and teaching, providing noteworthy insights into the potential benefits, challenges, and risks of AI technologies (Alenezi et al., 2023; Boudouaia et al., 2024; Jiang, 2022; Y. Lin & Yu, 2025). However, except for a few reports that examine AI literacy and competency in the context of teacher education (Salas-Pilco et al., 2022; Sperling et al., 2024) and more specifically in language teacher education (Moorhouse & Kohnke, 2024), the literature seems not to frequently enclose such accounts. Focusing on the impacts of generative AI on language teacher education, Moorhouse and Kohnke’s (2024) study can be emblematic in this respect, in which teacher educators’ perceptions of the integration of generative AI into initial language teacher education (ILTE) were explored through in-depth interviews. Despite teacher educators’ lack of confidence and competence in AI use, they believed that generative AI tools may have substantial effects on ILTE curriculum, instruction, and assessment. There have been efforts to understand AI literacy in teacher education to develop teachers’ skills and improve the quality of teacher education programs (Al-Zyoud, 2020; Jamal, 2023; Tunjera & Chigona, 2023), Similarly, Ayanwale et al.’s (2024) study is worth mentioning since it specifically delved into pre-service teachers’ perceptions of AI literacy, surveying to unveil the factors influencing their AI literacy. The study provided noteworthy insights for stakeholders in education and suggestions for improving pre-service teachers’ AI literacy. However, the shortcoming of Ayanwale et al.’s (2024) study can be noticed in the absence of qualitative data to obtain detailed information about pre-service teachers’ perception of AI literacy in teacher education. A comprehensive review of the relevant literature can display a paucity of empirical studies specifically investigating EFL teachers’ self-perceptions of their AI literacy and competency, and the integration of AI literacy education into EFL teacher education programs. Therefore, this endeavor attempts to present an explanation for pre-service language teachers’ knowledge and awareness about AI literacy and their AI competency, and their perceptions of the importance of AI literacy education for their future careers.

Method

Research Design

This study investigated pre-service EFL teachers’ perceptions of AI literacy and competency and the integration of AI education into EFL teacher education programs and employed a qualitative research design. The main purpose of in-depth interviews is to obtain comprehensive and detailed data regarding participants’ views, experiences, and perspectives about new or under-researched topics. Therefore, a small-scale exploratory approach was adopted in the current study. Since the area of AI literacy in EFL teacher education has not been extensively investigated, this research design helps to gain exploratory insights and establish key themes, which may lead to further larger-scale investigations. Based on these considerations, a small-scale exploratory approach aligned well with the purposes of this study.

Research Context and Participants

This research was carried out at the English Language Teaching Department at a state university in Türkiye, at the end of the spring semester of the 2023 to 2024 academic year. The university supports the integration of technology and AI with workshops and seminars. The ease of access to the participants was the reason for the selection of this university, since convenience sampling was used in the current study. The participants were 15 Turkish pre-service EFL teachers since this number was acceptable to reach data saturation, which is a commonly accepted standard to determine sample size in qualitative studies conducting interviews (Guest et al., 2006). Data saturation was reached when no new ideas and information emerged. All participants were Turkish speakers and aged between 21 and 28. Nine female and six male students participated in the study. English competence levels of the respondents extended from intermediate (B1) to advanced (C1) according to the assessment by the placement test of the institution. Fourth-grade English Language Teaching (ELT) students were purposefully selected since they had more experience and teaching practices as pre-service EFL teachers in their teacher training program. All of them undertook a teaching practicum course in their faculty. In their teaching education programs, they also received almost all the courses in the curriculum, and thereby they were more appropriate students to provide more precise and thorough data about the curriculum of the EFL teacher education program.

Instruments

Semi-structured interviews were utilized to collect qualitative data. The interview guide (see Appendix 1) consists of 12 open-ended questions, which were designed by the researcher based on a thorough literature search and review of the relevant literature. The questions were aimed to provide detailed data about the participants’ perceptions and experiences regarding AI literacy and competency, the use of AI tools like ChatGPT in language teaching, and AI literacy training in EFL teacher education programs. Based on their focus, the questions can be grouped into categories. For example, “Have you received any training on using AI tools in education? If yes, what was it like?,”“Does your teacher education program prepare you to use AI tools like ChatGPT in your teaching?,” and “What kind of training or support would you need to use AI tools in your teaching?” addressed the place of AI literacy instruction in teacher education programs. A flexible approach was adopted, and follow-up questions were used to generate ideas. For instance, the researcher explored participants’ AI knowledge by asking, “What are the main components of AI literacy?” and a follow-up question, “Which of these components can be the most important for language teachers?” was used to generate their ideas in detail.

The questions were piloted with two participants to ensure relevance and clarity. The questions were also checked by two experienced professionals in the field of English language teaching and one expert in the field of research methodology to ensure the relevance and quality of the interview questions. They were asked to evaluate the questions in terms of clarity, appropriateness, and thoroughness. All three experts provided feedback, which was used to make adjustments to improve the efficiency of the questions. The expert validation process assured that the interview questions were appropriate to obtain detailed data regarding the participants’ perceptions of AI literacy and competency, as well as the practices regarding AI literacy training in EFL teacher education programs.

Data Collection Procedure

Considering the methodological and ethical issues, it was ensured that all ethical protocols were followed before collecting data. Ethics committee approval was attained from the university’s ethics committee. The respondents were informed about confidentiality, voluntary participation, and the purposes of the study. The researcher prepared a consent form including information about the research aims, their rights, and how their data would be used. This form was completed by the participants. They were informed that they could withdraw from the study at any time. The interviews were conducted via ZOOM version 5.16.10, which facilitated secure recording and accurate documentation of responses. Online interviews were preferred to allow students to join from any location, making their participation more convenient. The interviews were employed in Turkish, the respondents’ native language, to enable participants to express their thoughts and feelings more fluently and accurately. Before the interview, participants were asked to check their internet connection and audio settings to minimize potential disruptions. The interviews, which lasted about 20 min, were video-recorded to capture the data more efficiently for analysis. All the interview recordings were safely stored in password-protected files, which were accessed only by the researcher.

Data Analysis

Thematic analysis was used for the analysis of the qualitative data in this study. Thematic analysis was a good match for the purposes of this study since it provides a functional and effective framework, which has six steps developed by Braun and Clarke (2006). Thematic analysis focuses on the identification of codes and the interpretation of themes extracted from these codes (Maguire & Delahunt, 2017). The steps include familiarity with the data, generation of initial codes, searching for themes, reviewing and defining themes, and writing. The thematic analysis allows researcher to adopt a more flexible approach to analyze the data by allowing an in-depth and nuanced exploration of emerging themes in the dataset (Braun & Clarke, 2006). Therefore, this flexible approach was chosen to produce richer and more comprehensive interpretations in line with the research aims of this study. An inductive approach was adopted to identify themes in the dataset. In this approach, themes emerge directly from the data itself (Patton, 1990), rather than being based on pre-existing frameworks or the researchers’ prior assumptions.

Data analysis in this study included an ongoing movement back and forth among the coded extracts in the dataset. Writing began from the first stage with the note-taking of the preliminary ideas and potential coding frameworks, and continued throughout the entire process of the analysis. First, all the video-recorded interviews were rigorously transcribed verbatim by the researcher after each session. Manual transcriptions were employed to guarantee the accuracy of subjects’ responses, and meanings were constructed.

As the first step suggested by Braun and Clarke (2006), the transcripts were meticulously read several times to familiarize oneself with the data corpus and identify potential patterns by taking notes and highlighting ideas for coding. The second step of analysis included manual coding of the data by identifying notable and repeated extracts within the dataset. All related extracts were ensured to be coded and grouped under relevant codes. Adhering to the guidelines provided by Braun and Clarke (2006), given that certain patterns might become relevant or important at a later phase of analysis, as many potential patterns as possible were coded and collated. In the third step, by analyzing different codes to generate meaningful thematic patterns, related codes were organized, the overarching themes were formed, and relevant data extracts were collated under each theme. The fourth step involved the review and refinement of the coded extracts under identified themes to ensure that meaningful and coherent patterns were generated and that the themes represented the entire data set. Therefore, the dataset was read again, which revealed additional relevant codes. To generate concise names for the themes, all themes were meticulously analyzed and defined by determining their essence. As the last step, a concise and logical report reflecting a comprehensive and detailed account of the data was written based on the data extracts, which displayed the themes clearly. The codes and themes are illustrated in Appendix 2.

Results

This study aimed to explore EFL pre-service teachers’ perceptions of their AI competency and the use of AI tools such as ChatGPT in their language teaching and thereby the integration of AI education into EFL teacher education programs. In this regard, the interviews were conducted with 15 EFL pre-service teachers, and the analysis of the interviews yielded four themes with their codes. The themes determined based on the qualitative data are “AI literacy knowledge,”“AI self-competence,”“AI -ChatGPT use in language teaching,” and “ AI training in teacher education programs.”

Theme 1: AI Literacy Knowledge

The first theme of this study revealed EFL pre-service teachers’ perceptions of their knowledge about AI and AI literacy. Regarding their familiarity with the concepts of AI and AI literacy, a large majority of the participants (67%) expressed that they have limited knowledge about AI and AI literacy. Although they all heard of AI and AI literacy before, no participants stated they were AI professionals.

The first code of this theme explained how participants defined AI. This code includes participants’ conceptual understanding of AI. Nine out of 15 participants mentioned AI as a conceptual construct. The most commonly occurring extracts for the definition of AI were human intelligence and data processing. Six participants (P1, P2, P4, P5, P9, P10) defined AI as a simulation and extension of human intelligence, narrating AI as the latest development in the digital world. Regarding the description of AI, P1 stated his opinions as follows:

In recent years, the world has witnessed rapid advancements in technology, and AI is another form of human intelligence blended with technology. In other words, it is a way of transferring human intelligence to the technological field. AI is responsible for carrying out the tasks that are commanded by its users. The development of AI increasingly influences our lives and education.

The generation of massive amounts of data in AI applications has been the subject of AI-related responses. Five participants (P2, P3, P8, P9, P14) pointed to large data sets of AI, which make this technology imperative in various areas of people’s lives. Displaying an ever-increasing interest in AI developments, P9 reported that “AI is capable of executing commands with a comprehensive knowledge base,” whereas P14 explained AI as “a system which produces huge quantities of data rapidly,” emphasizing the speed of data construction and processing in different formats such as logs, documents, and video.

Another prominent issue discussed by the respondents has been the description of AI literacy in terms of its application and evaluation, which is the second code under this theme. Considering AI as a technical phenomenon including a variety of subfields, one-third of all participants (P5, P7, P11, P13, P15) defined AI literacy as the interaction with AI tools, while the other important concept has been a critical evaluation of AI technologies, mentioned by three participants. For instance, providing a full understanding of AI literacy, P13 presented a comprehensive definition of AI literacy with the following statements:

In its simplest form, AI literacy is the knowledge of essential concepts and competencies to utilize AI applications. First of all, digital natives should be familiar with the basic AI terms and concepts, and must be capable of using different AI tools and developing AI solutions to the challenges which may occur. Last but not least, they should evaluate the weaknesses, strengths, and reliability of AI.

As the third code in this theme, ethical aspects of AI literacy were another consideration that a number of participants directly or indirectly addressed during the interviews. Six participants specifically associated AI literacy with the awareness of ethical values and responsibilities, such as personal data privacy, bias, fairness, and transparency. Overall, only 3 out of 15 participants stated that they had a lack of knowledge regarding the basic concepts of AI literacy, which shows that the EFL pre-service teachers generally perceive themselves as knowledgeable about AI literacy.

Theme 2: AI Self-Competence

This theme explains pre-service EFL teachers’ perceptions of their competence and readiness for using AI in their teaching. In the context of AI competency, the results displayed that nine participants (60%) felt unconfident and incompetent about the knowledge of AI literacy and application of AI tools, while only four participants (P1, P3, P8, P11) had more positive perceptions of their self-competency in AI use and expressed high confidence. For example, P3 stated, “I feel very confident in using AI; not only my AI knowledge but also skills in using AI are satisfying for me..” However, 11 participants expressed their confidence is low about AI knowledge and application in educational settings. The following extract from interview data shows how P7 described his confidence level:

Although technology is an integral part of every aspect of human life, AI is an unexplored area. Therefore, I feel that my confidence in AI knowledge and use is still very low. AI is a very new technology and needs to be integrated into education cautiously.

The most frequently mentioned reason for the lack of confidence among the participants was the lack of practice and training. Likewise, the participants commonly uttered that AI is a new technology that has brought about remarkable innovations in individuals’ learning and teaching experiences, which may pose challenges in keeping themselves updated with the latest AI tools and applications and developing high-level AI skills. P6 explained the predominant source of lack of confidence with the following statements:

I do not feel confident in using AI tools effectively because it is a new technology that has just been integrated into education. Also, I did not receive any education about using AI. I am trying to search and develop my skills to use AI more efficiently. My friends who need to use AI in their professional lives more frequently are more competent and confident than I am. The more experience we gain, the more confident we will be.

The results revealed that participants’ perceptions of their AI competency were directly affected by their different types of skills, such as basic computer literacy, digital literacy, and programing skills. As an example, P11 stated that

I believe I can efficiently and accurately use AI for language teaching, especially material and activity design, lesson planning, or exams. I possess sufficient knowledge about AI tools and ethical issues. Also, I have improved my basic computer and digital skills. I began using AI abroad, and I have personal and professional experiences. That’s why I feel competent in AI-related issues.

Quoted throughout the interviews and dispersed amongst the various responses were references to the lack of practice as pre-service teachers. Five participants emphasized that they do not have opportunities to practice AI tools. In this regard, P2 articulated that “For now, I am not very good at using AI because I do not use AI for language teaching as a pre-service teacher.” The participant went on to discuss the importance of practice in developing AI skills. Per her, there are various AI tools that teachers may benefit from, and it is essential to practice different tools to be more competent in AI. P7 emphasized the complementary role of AI in developing AI knowledge and skills and the high capacity of AI by saying, “I have only basic knowledge and skills about AI, but AI is a very powerful tool, and even if we are not competent, AI can help us use the tools effectively with its superior capacity.” P9 believed that he is considerably competent in basic knowledge and skills about AI, but not confident in high-level skills, and stated that “I have advanced competencies in AI use and search techniques, but I still lack high-level skills, such as designing and optimizing deep learning and AI systems or models.”

Another important factor for the negative perceptions of AI self-competency, which was articulated by all of the participants, was the lack of instructional courses. The participants claimed that they are encircled by AI, but not in their professional development. They addressed the need for institutional support for increasing AI literacy among English teachers. In this regard, P3 reported that:

English teachers are not competent in capturing the innovative changes in the digital world. However, the swift growth in the accessibility of huge amounts of data increases demand for the integration of AI processes into language learning and teaching. Despite this, teacher education programs still lack a curriculum including AI education.

P5 posited that he did not receive AI training during his university education. He continued, “In our curriculum, there is a course to teach computer skills. Although AI is a superpower in our world, there is no effort for the construction of AI knowledge and skills.” Supporting these views, P8 asserted that “We, as EFL pre-service teachers, were not instructed about efficient and responsible use of AI in our classrooms.” Likewise, P4 reflected that “Existing teacher education predominantly relies on basic computer skills education and ignores AI literacy education. We should have received up-to-date and practical improvement suggestions during our university years, but we didn’t.” To sum up, pre-service EFL teachers expressed they generally feel unconfident and incompetent in AI literacy due to reasons such as lack of basic knowledge and skills, inadequate training and education, and lack of practical experiences. This not only impedes their professional development but also influences their teaching experiences.

Theme 3: AI-ChatGPT Use in Language Teaching

The third theme of this study addresses pre-service EFL teachers’ perceptions of their actual use of AI tools, more specifically ChatGPT, in language teaching practices. The participants generally favored the use of ChatGPT in language teaching due to its output quality, conversational interface, and free availability and ease of use. ChatGPT has been the most popular and most frequently used AI tool among the respondents, while Co-pilot, Gemini, Grammarly, and Bing Image Creator were the other AI tools cited by only five participants. Only one participant (P9) uttered that he rarely uses ChatGPT, explaining the reasons as follows:

I use ChatGPT very rarely. Because the opportunities that ChatGPT provides are not related to my job. I usually use Co-pilot since it helps me to develop algorithms and design models. Also, GPT models are closed source, and their internal workings are not shared with the users.

They explained their reasons for using ChatGPT in their teaching practices in terms of the potential benefits and challenges of ChatGPT. Ten participants (66%) supported the use of ChatGPT for a variety of benefits, such as generating lesson plans and receiving feedback for the quality and effectiveness of their lesson plans. P14 found ChatGPT time-saving by reflecting “Teachers can use ChatGPT for their lesson plans and related materials such as worksheets, presentations, and classroom activities.” P6 criticized the excessive use of ChatGPT by stating, “ChatGPT is a useful tool if it is used appropriately, but it is important to know the difference between using ChatGPT as a medium or a replacement for human teachers. Teachers must benefit from ChatGPT as a supportive tool.” P9 reported different suggestions with the following insights:

It would be better to use AI tools such as eye-tracking systems and speech recognition systems to implement during task stages rather than directly integrating AI applications into the course content. In this way, AI tools can be supportive in language teaching. The control should be in the hands of teachers, not AI tools.

Unsurprisingly, a number of challenges of AI tools, particularly ChatGPT, could be listed based on the analysis of the interviews. Although the participants generally acknowledged AI tools’ significance, they also approached the use of AI tools in terms of their limitations and threats (six participants). They expressed their concerns and fears about AI use. A striking fear stated by two participants was job displacement. They believed that AI might replace EFL teachers, which may cause depreciation in the teachers’ profession. Technology intimidation was another fear mentioned by three participants (P4, P7, P12). Inequality (e.g., P1, P2), cheating and plagiarism (e.g., P4, P5, P8), over-reliance (with the highest percentage, 73.33%), reliability (e.g., P7, P12) were among the most commonly uttered limitations of AI-ChatGPT. Emphasizing the inability of chatbots to understand and acknowledge feelings and emotions, P6 uncovered the limitations of AI tools:

Such tools may encounter challenges in mimicking human-like interaction and responding to emotional cues. That’s why they tend to robotize students. For example, they cannot understand if somebody is angry, happy, or upset. This situation can result in robotic and impersonal interactions.

Supporting P6, P8 underlined over-reliance as one of the most critical issues in AI use in language teaching by noting that:

AI is both useful and intimidating because these tools may take all the responsibility off human teachers’ hands. Thus, ChaptGPT should be used only to promote teachers’ deficiencies, not replace them. If not, this may result in a lack of self-confidence and creativity in teachers.

In alignment with P8, P4 mentioned various challenges of AI-ChatGPT, as apparent in her response, “ChatGPT might decrease teachers’ creativity, leading them to be lazier. They may shirk their responsibilities, which is not ethical. Also, the reliability of the data provided by AI tools must be evaluated; sometimes they can provide inaccurate information.” The negative effects of AI tools on academic discipline and quality (P3, P6, P7, P8, P11) and privacy (P2, P6) were perceived as limitations by the subjects in this study, though not many in number. All in all, pre-service teachers acknowledged the benefits of ChatGPT in EFL teaching environments, not ignoring its limitations and weaknesses. The interviews, however, reflected a broad consensus among the participants about the responsible and ethical use of ChatGPT.

Theme 4: AI Training in Teacher Education Programs

The fourth theme of this study displayed the role of AI training in teachers’ professional expertise. Pre-service EFL teachers shared their perceptions of the current issues related to AI training in teacher education programs. The subjects were unanimous about the lack of AI training, its negative effects on their teaching, and the necessity of training support. Based on the participants’ views about the lack of AI training discussed in the second theme, they generally suggested changes in university policy (four participants; e.g., P1, P11), AI integrated courses (three participants; e.g., P4, P5), expert academicians (four participants; e.g., P10, P14), and usage practices (three participants; e.g., P9, P12). For example, P1 addressed the necessity of policy changes by saying, “Pre-service teachers are ready to develop their AI skills, and university authorities should realize this and support their learning.”

Pointing to the responsibilities of the university, P4 noted, “There are no arrangements by the university administration. Only teachers individually try to guide students about efficient AI use.” He further stated that “AI training must be included in the teacher education curricula.” This view was supported by many participants (66,6%). Reflections on curriculum recommendations also underlined the importance of the inclusion of AI training into other technology-related courses (60%). As an example, P10 reiterated the need for both an AI-specific lesson and AI-integrated department lessons and added “Teacher instructors can be role models for us by using AI applications in their classrooms. To do this, the academicians need to be expert AI users.” As in this quote, teacher instructors’ expertise was considered an important step for the advancement of pre-service teachers’ AI literacy and competency according to P1, P3, P4, and P14. Extending this issue, P4 asserted, “Teacher instructors can be responsive to our needs and expectations and create opportunities for us to practice AI tools when we complete course-related tasks. In this way, we can be more familiar with AI use in teaching practices.” P15 found academicians biased about students’ AI use in the courses and added “Teacher instructors should encourage students to use AI for teaching purposes. They must eliminate their bias and should regard AI use as cheating.” Four participants held similar views and attitudes toward activities such as workshops and seminars to increase awareness about ethical and responsible AI use among pre-service teachers.

An in-depth analysis of the interviews revealed that 53,3 of the participants agreed on the necessity of content regulations of AI training courses. Training pre-service teachers about ethical issues in AI was an inseparable part of AI training courses for many participants (53,3%). Overall, the last theme of this study showed that EFL teachers’ professional expertise can be strengthened by AI-oriented teaching. Therefore, pre-service teachers should be offered AI training in teacher education programs.

Discussion

This study extends the strand of research in AI literacy in EFL teacher education programs with the students’ viewpoints by revealing the necessity of integrating AI literacy training into these programs, which is also supported by several studies (Ayanwale et al., 2024; Celik, 2022; Ng et al., 2023; Tunjera & Chigona, 2023). Ayanwale et al. (2024) emphasized the priorities for inclusive instruction regarding AI principles and functions in teacher education programs. Ma and Lei (2024), in a similar vein, suggest that increasing the AI literacy of teachers can foster their teaching performance since teachers can grasp AI functionality. With the growing attention to pre-and in-service teachers’ attitudes toward AI use, Salas-Pilco et al. (2022) highlight the importance of professional development training with a focus on improving AI literacy and competence. This necessity might arise from the deficiencies of basic AI skills and knowledge that language teachers should retain for more effective language courses integrated with AI tools. Many participants in this study reported that the reason for the perceived lack of AI competency is the insufficient AI use and practice in their programs. They underlined the importance of teacher educators’ attitudes toward EFL pre-service teachers’ AI use. This finding was confirmed by Hur’s experimental study (2024) in which pre-service teachers’ concerns and confidence regarding AI use were investigated. Hur (2024) concluded that pre-service teachers lacked AI knowledge, and thereby they felt unconfident before AI lessons; however, after they received AI training, their AI awareness increased, and they felt more confident about using AI in their teaching. One potential explanation for these perceptions can be academicians’ biases about AI incorporation into language teaching. Another reason can be the shortage of expert academicians to train pre-service teachers for efficient AI integration into their teaching. This inference has also been made by Aşık et al. (2020), who assert that teacher educators play an important part in technology-integrated education since they are considered role models for pre-service teachers, which is a consistent notion with the results of our study. Expert academicians in AI use can assist EFL pre-service teachers in enhancing their AI literacy and competency, which showed parallelism with the results of the study conducted by Ma and Lei (2024). Similarly, students’ perceptions about the lack of expert academicians align with the findings of this exploratory study, which found that teacher educators are not self-confident AI users. Based on this finding, it can be deduced that, given the lack of essential skills and knowledge of teacher educators, they are somehow learners like pre-service teachers.

The results yielded that the participants’ descriptions of AI and AI literacy showed similarities in terms of their key concepts, such as computer, human intelligence, and digital learning, and basic components, including knowledge, application, and evaluation. Their knowledge accumulation may not be at the desired level; however, their awareness is relatively high about the emergence of AI as a crucial factor in the transformation of educational teaching methods. This finding can be explained by the principles of the TPACK, which emphasize the holistic relationship between TK and PK. These findings can be interpreted as encouraging in a way that language teachers can go beyond the traditional methods in language teaching by increasing and bolstering AI use. Extending this discussion, Moorhouse and Kohnke (2024) advocated that AI tools can enhance the effectiveness of existing approaches; therefore, university policies should guide the integration of AI into teacher education.

Pre-service teachers appear to show a willingness to design their instructional practices using AI tools in their future careers. Another outcome that is striking in the results is the reflection of ChatGPT use in language teaching, since it provides a more comfortable teaching environment by facilitating the design of teaching material and activities. Mondal et al. (2023), in a similar vein, ascertained the potential benefits of ChatGPT use for teaching purposes. The findings, however, revealed a lack of familiarity with the AI tools except ChatGPT. This implies the necessity of training on different AI tools for teacher development to enable the teachers to notice the benefits and drawbacks of each AI tool. To comprehend the foundational factors behind this necessity, we turn to the TPACK theory, focusing on the competencies required to foster subject learning.

Another significant result worth mentioning was pre-service EFL teachers’ perceived lack of confidence in the effective integration of AI tools into their teaching. Their limited AI competence, from their perspectives, might be the result of the lack of AI training and insufficient utilization of AI in the courses of teachers’ education programs. This scenario displays concerns about ethical issues regarding AI use. This study provided significant insights into teachers’ inadequate knowledge about ethical issues, wherein the participants noted a need to deliver such instructional modules to improve an ethical mindset against the risks and conflicts of AI, such as personal data privacy and bias in data processing. Pre-service teachers’ concerns and fears were among the striking findings of this study. The fear of reducing teachers’ roles in classrooms, overreliance on the content generated by AI, and decline in teachers’ creativity were expressed by pre-service EFL teachers. These concerns were also reported by Pokrivcakova (2023) and Hur (2024). Similarly, this study provided harmonious results with two studies conducted by Korte et al. (2024) and Salas-Pilco et al. (2022). Bringing emotional and persuasive contexts to the forefront, Ayanwale et al. (2024), correspondingly, accentuate that educators with high levels of AI knowledge and advanced skills tend to manage ethical considerations more efficiently. This suggests a curriculum rich in AI literacy to equip pre-service teachers with AI-infused pedagogical skills. The awareness about fears and concerns of pre-service teachers may shed light on the needs and expectations for AI training, focusing not only on theoretical and applicable competence but also on ethical and emotional aspects of AI use, which necessitates such a curriculum to foster their evaluation skills and thereby ethical and emotional assessment. According to the results of this study, understanding what AI is and the way it works forms the basis of AI literacy and competency. This corresponds with the results of an empirical study (Celik, 2022), which concludes that as pre-service teachers develop technological knowledge about what AI is and how it functions, they can have a better understanding of the ethical issues of AI.

This study underscores the significance of AI knowledge and competencies in shaping quality language teaching. Bearing in mind that integrating new technologies that involve AI tools not only offers a variety of benefits but also a variety of difficulties, teachers should be aware of these benefits and challenges to deliver effective instruction. Therefore, it is necessary to equip pre-and in-service teachers with the capabilities to adopt AI tools. This result is in line with the underpinnings of TK as a significant component of the TPACK theory, which suggests that teachers with advanced TK can integrate AI technologies into their teaching. This perspective fosters their readiness for using emerging technologies and, as a result, facilitates student learning. The prominence of developing a thorough curriculum that includes courses providing fundamental awareness and comprehension of AI principles and ethics can be highlighted since teachers with AI competence can adopt AI-powered technologies to enhance their teaching practices and administration. In this way, pre-service EFL teachers can employ more innovative teaching systems with personalized learning, instant feedback, and interactive and collaborative learning in their future classrooms. Training modules should be organized to enhance pre-service teachers’ skills to critically evaluate the usefulness of different AI tools in various teaching settings and the responsible use of AI. Similarly, teacher educators can support pre-service EFL teachers in the practical application of existing AI technologies in their teaching practices. The content and pedagogy of teacher education courses can provide opportunities for pre-service EFL teachers with special learning needs to experience AI use in responsible and sustainable ways, which would be a model for future classrooms. Efficient implementation of AI literacy curriculum in teacher education programs requires the active involvement of university administration, program coordinators, and the entire teaching faculty in addition to teacher educators. This process should not be considered as the task of only teacher educators. In policy and curriculum revision, a collaborative approach would offer a systematic and organized groundwork for better equipping EFL teachers who effectively use AI technologies in their classrooms.

Conclusions, Limitations, and Future Research

This study aimed to address an important issue related to the use of AI-assisted language teaching. Whereas previous research concerning the role of AI tools, particularly ChatGPT in language teaching, generally identified opportunities and challenges of AI tools in designing language courses, this study addressed pre-service EFL teachers’ AI literacy including the acquisition of AI knowledge, concepts and, skills as well as their needs and expectations to enhance their professional and pedagogical abilities. More specifically, pre-service EFL teachers’ perceptions of their AI competency were highlighted. Their positive attitudes toward the use of AI tools in language teaching underscore the need for extensive AI training in EFL teacher education programs. Since pre-service teachers have negative perceptions of their AI competency, the AI literacy curriculum should not be restricted to technical knowledge and should aim to develop skills for creative and responsible use of AI tools and applications.

Along with these insights, we can also recognize the limitations of this study. First, as a methodological limitation, the data in this study were chosen from a particular demographic and obtained through semi-structured interviews with a small number of students who were from the same cultural and instructional setting. This may offer limited generalizability and can be considered a limitation of this study in terms of data triangulation. For a broader perspective, additional measures such as surveys, document analysis, and observations can be used with a wider sample to gather more generalizable data as well as for the triangulation of the findings. The use of convenience sampling might have limited the generalizability of the findings for alternative settings such as multicultural workplaces and technology-driven sectors. Future research which uses more representative sampling strategies can address this limitation by selecting participants from different populations to represent the variety of perspectives and experiences regarding AI use in alternative settings. Additionally, the results of this study are restricted to the Turkish EFL context, which could be affected by cultural and contextual aspects specific to this setting. Turkish pre-service EFL teachers might have reflected unique features of tertiary education in Türkiye, such as dominant attitudes toward AI, curriculum design, and technology availability. Therefore, subsequent studies can be conducted in diverse cultural and instructional settings for the generalizability of the results to broader educational contexts. Due to the nature of semi-structured interviews, participants “responses may not represent their true perceptions and experiences. Teacher instructors” perceptions, cognitions, and practices concerning pre-service teachers’ AI literacy and competency can be investigated for a broader understanding of the phenomenon. Considering these suggestions, further research attempts may contribute to the understanding of AI literacy from different perspectives and increase educational practitioners’ and policymakers’ awareness regarding the integration of AI literacy training. This study focused on perceived AI competence and literacy rather than practiced one since our main purpose was to investigate pre-service EFL teachers’ perceptions of their AI knowledge and skills as well as their beliefs, experiences, needs, and expectations regarding AI use in language classrooms. The findings can provide insights into the integration of AI training into teacher education programs. However, further studies can be suggested to explore the practiced AI literacy and competence of pre-service EFL teachers. Future research may focus on how AI training programs are designed and implemented. Longitudinal studies can be conducted to understand the long-term effects of AI training in teacher education programs on teachers’ instructional practices as well as students’ learning and engagement. Cross-cultural studies may also shed light on factors influencing AI training integration in teacher education.

Footnotes

Appendix

Appendices

Author Note

This paper was presented in the 2024 European Society for the Study of English (ESSE) Conference on August 28, 2024.

ORCID iD

Canan Karaduman

Ethical Considerations

A study of survey participants: The Social and Human Sciences Ethics Review Committee at Atatürk University approved our interviews (approval: E.88656144-000-2400255968) on 08.06.2024.

Consent to Participate

Respondents were given written consent for review and signature before starting interviews. The consent form included information about research aim, participants’ rights, and how their data would be used. The participants were informed about confidentiality, voluntary participation, the purposes, duration, and procedure of the research, and withdrawal from the study at any time without penalty. They were also informed that their data would be anonymized and used only for academic purposes. Written consent was obtained prior to participation. Participants were not exposed to any physical, psychological, or emotional risk during data collection.

Author Contributions

Canan Karaduman: Conceptualization, Investigation, Formal Analysis, Writing—Original draft, Reviewing and Editing.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data will be made available on request.

References

Abdelrady

A. H.

Akram

(2022). An empirical study of ClassPoint tool application in enhancing EFL students’ online learning satisfaction. Systems, 10(5), 154. https://doi.org/10.3390/systems10050154

Adeshola

Adepoju

A. P.

(2024). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 32, 6159–6172. https://doi.org/10.1080/10494820.2023.2253858

Akram

Abdelrady

A. H.

(2025). Examining the role of ClassPoint tool in shaping EFL students’ perceived E-learning experiences: A social cognitive theory perspective. Acta Psychologica, 254, 104775. https://doi.org/10.1016/j.actpsy.2025.104775

Akram

Abdelrady

A. H.

Al-Adwan

A. S.

Ramzan

(2022). Teachers’ perceptions of technology integration in teaching-learning practices: A systematic review. Frontiers in Psychology, 13, 920317. https://doi.org/10.3389/fpsyg.2022.920317

Akram

Yingxiu

Al-Adwan

A. S.

Alkhalifah

(2021). Technology integration in higher education during COVID-19: An assessment of online teaching competencies through Technological Pedagogical Content Knowledge model. Frontiers in Psychology, 12, 1–11. https://doi.org/10.3389/fpsyg.2021.736522

Alenezi

M. A. K.

Mohamed

A. M.

Shaaban

T. S.

(2023). Revolutionizing EFL special education: How CHATGPT is transforming the way teachers approach language learning. Innoeduca International Journal of Technology and Educational Innovation, 9(2), 5–23. https://doi.org/10.24310/innoeduca.2023.v9i2.16774

Al-Khresheh

M. H.

(2024). Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching. Computers and Education: Artificial Intelligence, 6, 100218. https://doi.org/10.1016/j.caeai.2024.100218

Allen

L. K.

Kendeou

(2024). Ed-Ai Lit: An interdisciplinary framework for AI literacy in Education. Policy Insights from the Behavioral and Brain Sciences, 11(1), 3–10. https://doi.org/10.1177/23727322231220339

Al-Zyoud

H. M.

(2020). The role of Artificial Intelligence in teacher professional development. Universal Journal of Educational Research, 8(11B), 6263–6272. https://doi.org/10.13189/ujer.2020.082265

10.

Chai

C. S.

Zhou

Shen

Zheng

Chen

(2023). Modeling English teachers’ behavioral intention to use artificial intelligence in Middle Schools. Education and Information Technologies, 28(5), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z

11.

Aşık

Köse

Yangın Ekşi

Seferoğlu

Pereira

Ekiert

(2020). ICT integration in English language teacher education: Insights from Turkey, Portugal, and Poland. Computer Assisted Language Learning, 33(7), 708–731. https://doi.org/10.1080/09588221.2019.1588744

12.

Ayanwale

M. A.

Adelana

O. P.

Molefi

R. R.

Adeeko

Ishola

A. M.

(2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179

13.

Azap

(2025). The role of artificial intelligence in language teacher education: EFL learners’ views on the concept of “ChatGPT” through metaphorical analysis. Social Sciences & Humanities Open, 11, 101394. https://doi.org/10.1016/j.ssaho.2025.101394

14.

Bin-Hady

W. R. A.

Al-Kadi

Hazaea

Ali

J. K. M.

(2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech. https://doi.org/10.1108/lht-05-2023-0200

15.

Boudouaia

Mouas

Kouider

(2024). A study on ChATGPT-4 as an innovative approach to enhancing English as a foreign language writing learning. Journal of Educational Computing Research, 62(6), 1289–1317. https://doi.org/10.1177/07356331241247465

16.

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

17.

Celik

(2022). Exploring the determinants of Artificial Intelligence (AI) literacy: Digital divide, computational thinking, cognitive absorption. SSRN Electronic Journal, 1–12. https://doi.org/10.2139/ssrn.4244763

18.

Celik

(2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate Artificial Intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468

19.

Chiu

T. K. F.

Ahmad

Ismailov

Sanusi

I. T.

(2024). What are Artificial Intelligence Literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171

20.

Chiu

T. K. F.

Chai

C. S.

(2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568

21.

Chounta

I.-A.

Bardone

Raudsep

Pedaste

(2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 Education. International Journal of Artificial Intelligence in Education, 32(3), 725–755. https://doi.org/10.1007/s40593-021-00243-5

22.

Eguchi

Okada

Muto

(2021). Contextualizing AI education for K-12 students to enhance their learning of AI literacy through culturally responsive approaches. KI - Künstliche Intelligenz, 35(2), 153–161. https://doi.org/10.1007/s13218-021-00737-3

23.

Falloon

(2020). From digital literacy to digital competence: the teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4

24.

Geng

Chai

C. S.

Jong

M. S. Y.

Luk

E. T. H.

(2021). Understanding the pedagogical potential of interactive spherical video-based virtual reality from the teachers’ perspective through the ACE framework. Interactive Learning Environments, 29(4), 618–633. https://doi.org/10.1080/10494820.2019.1593200

25.

Glaser

(2023). Exploring the potential of ChatGPT as an educational technology: An emerging technology report. Technology Knowledge and Learning, 28, 1945–1952. https://doi.org/10.1007/s10758-023-09684-4

26.

Grassini

(2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692

27.

Guest

Bunce

Johnson

(2006). How many interviews are enough? Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822x05279903

28.

Hsu

Y. C.

Ching

Y. H.

(2023). Generative artificial intelligence in education, part one: The dynamic Frontier. TechTrends, 67(4), 603–607. https://doi.org/10.1007/s11528-023-00863-9

29.

Hur

J. W.

(2024). Fostering AI literacy: overcoming concerns and nurturing confidence among preservice teachers. Information and Learning Sciences, 126(1/2), 56–74. https://doi.org/10.1108/ils-11-2023-0170

30.

Jamal

(2023). The role of Artificial Intelligence (AI) in teacher education: Opportunities & challenges. International Journal of Research and Analytical Reviews, 10(1), 140–146.

31.

Jatileni

C. N.

Sanusi

I. T.

Olaleye

S. A.

Ayanwale

M. A.

Agbo

F. J.

Oyelere

P. B.

(2024). Artificial Intelligence in compulsory level of education: Perspectives from Namibian in-service teachers. Education and Information Technologies, 29, 12569–12596. https://doi.org/10.1007/s10639-023-12341-z

32.

Javier

D. R.

Moorhouse

B. L.

(2023). Developing secondary school English language learners’ productive and critical use of ChatGPT. TESOL Journal, 15(2), 1–9. https://doi.org/10.1002/tesj.755

33.

Jiang

(2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on Artificial Intelligence in the EFL context. Frontiers in Psychology, 13, 1049401. https://doi.org/10.3389/fpsyg.2022.1049401

34.

Karataş

Abedi

F. Y.

Ozek Gunyel

Karadeniz

Kuzgun

(2024). Incorporating AI in foreign language education: An investigation into CHATGPT’s effect on foreign language learners. Education and Information Technologies, 29, 19343–19366. https://doi.org/10.1007/s10639-024-12574-6

35.

Kim

N. J.

Kim

M. K.

(2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, 1–13. https://doi.org/10.3389/feduc.2022.755914

36.

Kohnke

Moorhouse

B. L.

Zou

(2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868

37.

Kong

S.-C.

Korte

S.-M.

Burton

Keskitalo

Turunen

Smith

Wang

Lee

J. C.-K.

Beaton

M. C.

(2025). Artificial Intelligence (AI) literacy – An argument for AI literacy in Education. Innovations in Education and Teaching International, 62, 477–483. https://doi.org/10.1080/14703297.2024.2332744

38.

Korte

S.-M.

Cheung

W. M.-Y.

Maasilta

Kong

S.-C.

Keskitalo

Wang

Lau

C. M.

Lee

J. C. K.

M. M.

(2024). Enhancing artificial intelligence literacy through cross-cultural online workshops. Computers and Education Open, 6, 100164. https://doi.org/10.1016/j.caeo.2024.100164

39.

Kostka

Toncelli

(2023). Exploring applications of ChatGPT to English language teaching: Opportunities, challenges, and recommendations. Teaching English as a Second or Foreign Language, 27(3), 1–19. https://doi.org/10.55593/ej.27107int

40.

Kovačević

(2023). Use of ChatGPT in ESP teaching process [Conference session]. 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1–5). IEEE. https://doi.org/10.1109/INFOTEH57020.2023.10094133

41.

Lambert

Stevens

(2024). ChatGPT and Generative AI technology: A mixed bag of concerns and new opportunities. Computers in the Schools, 41, 559–583. https://doi.org/10.1080/07380569.2023.2256710

42.

Lancaster

(2023). Artificial intelligence, text generation tools and ChatGPT – Does digital watermarking offer a solution? International Journal for Educational Integrity, 19(1), 10. https://doi.org/10.1007/s40979-023-00131-6

43.

Lérias

Guerra

Ferreira

(2024). Literacy in artificial intelligence as a challenge for teaching in higher education: A case study at Portalegre Polytechnic University. Information, 15(4), 205. https://doi.org/10.3390/info15040205

44.

Lesia Viktorivna

Andrii Oleksandrovych

Iryna Oleksandrivna

Nadia Oleksandrivna

. (2022). Artificial intelligence in language learning: What are we afraid of. Arab World English Journal, (8), 262–273. https://doi.org/10.24093/awej/call8.18

45.

Lin

X.-F.

Chen

Chan

K. K.

Peng

Chen

Xie

Liu

(2022). Teachers’ perceptions of Teaching Sustainable Artificial Intelligence: A design frame perspective. Sustainability, 14(13), 7811. https://doi.org/10.3390/su14137811

46.

Lin

(2025). Learner perceptions of artificial intelligence-generated pedagogical agents in language learning videos: Embodiment effects on technology acceptance. International Journal of Human–Computer Interaction, 41, 1606–1627. https://doi.org/10.1080/10447318.2024.2359222

47.

Liu

(2025). Language teacher AI literacy: Insights from collaborations with ChatGPT. Journal of China Computer-Assisted Language Learning, 1–30. https://doi.org/10.1515/jccall-2024-0030

48.

Long

Magerko

(2020). What is AI literacy? Competencies and design considerations [Conference session]. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727

49.

Maguire

Delahunt

(2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. All Ireland Journal of Higher Education, 9(3), 3351–3364.

50.

Lei

(2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94–111. https://doi.org/10.1080/02188791.2024.2305155

51.

Meniado

C. J.

(2023). The impact of ChatGPT on English language teaching, learning, and assessment: A rapid review of literature. Arab World English Journal, 14(4), 3–18. https://doi.org/10.24093/awej/vol14no4.1

52.

Mishra

Koehler

M. J.

(2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. https://doi.org/10.1177/016146810610800610

53.

Mondal

Marndi

Behera

J. K.

Mondal

(2023). ChatGPT for teachers: Practical examples for utilizing artificial intelligence for educational purposes. Indian Journal of Vascular and Endovascular Surgery, 10(3), 200–205. https://doi.org/10.4103/ijves.ijves_37_23

54.

Moorhouse

B. L.

Kohnke

(2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290. https://doi.org/10.1016/j.system.2024.103290

55.

D. T. K.

Leung

J. K. L.

R. C. W.

Chu

S. K. W.

(2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6

56.

Park

Teo

T. W.

Teo

Chang

Huang

J. S.

Koo

(2023). Integrating Artificial Intelligence into science lessons: Teachers’ experiences and views. International Journal of STEM Education, 10(1), 1–22. https://doi.org/10.1186/s40594-023-00454-3

57.

Patton

M. Q.

(1990). Qualitative evaluation and research methods. Sage.

58.

Pokrivcakova

(2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153. https://doi.org/10.2478/jolace-2019-0025

59.

Pokrivcakova

(2023). Pre-service teachers’ attitudes towards artificial intelligence and its integration into EFL teaching and learning. Journal of Language and Cultural Education, 11(3), 100–114. https://doi.org/10.2478/jolace-2023-0031

60.

Popenici

S. A. D.

Kerr

(2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8

61.

Rawas

(2023). CHATGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 29(6), 6895–6908. https://doi.org/10.1007/s10639-023-12114-8

62.

Salas-Pilco

Xiao

(2022). Artificial Intelligence and Learning Analytics in teacher education: A systematic review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569

63.

Shaikh

Yayilgan

S. Y.

Klimova

Pikhart

(2023). Assessing the usability of ChatGPT for formal English language learning. European Journal of Investigation in Health, Psychology and Education, 13(9), 1937–1960. https://doi.org/10.3390/ejihpe13090140

64.

Söğüt

(2024). Generative artificial intelligence in EFL writing: A pedagogical stance of pre-service teachers and teacher trainers. Focus on ELT Journal, 6(1), 58–73. https://doi.org/10.14744/felt.6.1.5

65.

Southworth

Migliaccio

Glover

Reed

McCarty

Brendemuhl

Thomas

(2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127

66.

Sperling

Stenberg

C.-J.

McGrath

Åkerfeldt

Heintz

Stenliden

(2024). In Search of Artificial Intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169. https://doi.org/10.1016/j.caeo.2024.100169

67.

Taskiran

Goksel

(2022). Automated feedback and teacher feedback: Writing achievement in learning English as a foreign language at a distance. Turkish Online Journal of Distance Education, 23(2), 120–139. https://doi.org/10.17718/tojde.1096260

68.

Tunjera

Chigona

(2023). Investigating effective ways to use artificial intelligence in teacher education. European Conference on e-Learning, 22(1), 331–340. https://doi.org/10.34190/ecel.22.1.1625

69.

Turing

(1950). Computing machinery and intelligence. Mind, 59(236), 433–460.

70.

Zhai

Chu

Liu

Wang

(2024). Using deep learning-based artificial intelligence electronic images in improving middle school teachers’ literacy. PeerJ Computer Science, 10, e1844. https://doi.org/10.7717/peerj-cs.1844

71.

Zhao

Luo

(2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), 14549. https://doi.org/10.3390/su142114549

72.

Zhou

Chai

C. S.

Chiu

T. K. F.

(2025). Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 Education from a systematic review. Interactive Learning Environments. 1–23. https://doi.org/10.1080/10494820.2025.2487538

Pre-Service EFL Teachers’ Perceived AI Literacy and Competency: The Integration of ChatGPT Into English Language Teacher Education

Abstract

Keywords

Introduction

Literature Review

Theoretical Framework

Artificial Intelligence Literacy and Competency

AI-ChatGPT and English Language Teaching

AI Literacy in Teacher Education

Method

Research Design

Research Context and Participants

Instruments

Data Collection Procedure

Data Analysis

Results

Theme 1: AI Literacy Knowledge

Theme 2: AI Self-Competence

Theme 3: AI-ChatGPT Use in Language Teaching

Theme 4: AI Training in Teacher Education Programs

Discussion

Conclusions, Limitations, and Future Research

Footnotes

Appendix

Author Note

ORCID iD

Ethical Considerations

Consent to Participate

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References