Introduction
The concept of artificial intelligence, introduced by McCarthy in 1956 (Cristianini, 2016), has evolved from a theoretical construct to a practical force reshaping educational practice worldwide. In the field of English as a Foreign Language (EFL) teaching, AI-driven tools now support lesson planning, automated grading, personalized feedback, and interactive language practice through chatbots and writing assistants (Chen et al., 2020; Wang et al., 2024). The rapid advancement of generative AI, particularly large language models such as ChatGPT, has accelerated this transformation and placed teachers at the center of a significant pedagogical shift.
Recent systematic reviews indicate that AI integration in English language teaching is expanding, with writing assistance as the most common application, accounting for 54.55% of studies (Daud et al., 2025). AI tools such as grammar checkers, chatbots, and language learning apps have shown promise in improving linguistic accuracy, reducing student anxiety, and offering personalized learning pathways (Du & Daniel, 2024; Yuan & Liu, 2025). However, concerns about academic dishonesty, student overreliance on AI, and diminished critical thinking persist across educational contexts.
While a growing body of research has examined student perceptions and learning outcomes in AI-enhanced classrooms, the teacher perspective remains comparatively underexplored. This gap is significant because teachers are the primary gatekeepers of AI integration. Their beliefs, competencies, and institutional support structures directly shape how AI tools are introduced and function within classrooms (Celik, 2023; Omidvar & Meihami, 2025). Recent evidence indicates that the Technology Acceptance Model (TAM) is the most commonly used theoretical framework in studies of teacher AI adoption, with perceived usefulness and perceived ease of use identified as primary determinants of acceptance (Xue et al., 2025). However, emerging research also highlights the need for education-specific acceptance models that account for factors unique to teaching contexts, such as pedagogical beliefs, ethical concerns, and the relational nature of instruction.
The integration of AI into EFL teaching is further complicated by contextual factors. Teachers in resource-constrained settings may face additional barriers related to infrastructure, digital literacy, and access to professional development (OECD, 2018). Recent work from the Western Balkans describes this shift as a “quiet transformation” in which generative AI becomes an invisible yet pervasive presence in students’ academic lives, while many educators encounter a widening digital divide, ethical dilemmas, and unequal access to AI tools (Pireci Sejdiu & Sejdiu, 2025). In the Western Balkans, and in Kosovo in particular, recent institutional initiatives have begun to explore how AI can be embedded into higher education teaching and learning, with an emphasis on developing clear guidelines for responsible use. A recent study of Kosovan university students found that while 85.9% found AI tools easy to use, only 34.1% trusted their feedback, revealing a “trust-usability paradox” that underscores the complexity of AI acceptance (Braha, 2026). However, no study has yet examined how EFL teachers in these contexts perceive AI tools, what opportunities and challenges they identify, and what professional support they require.
This study addresses these gaps by investigating EFL teachers’ perceptions of AI tools in the English classroom, with a particular focus on Kosovo as an understudied, post-conflict educational context (OECD, 2018; Xue et al., 2024). The present study provides an exploratory account of EFL teachers’ reported AI practices, perceived educational opportunities, concerns, and professional support needs in an underrepresented context.
Given the small convenience sample and exploratory questionnaire, the study aims to provide an initial, context specific description. It does not test theoretical relationships or seek to produce broadly generalisable conclusions.
Three research questions guide the investigation:
•
RQ1: What are EFL teachers’ current AI tool usage practices, including tool preferences, frequency of use, and perceived benefits for language instruction?
•
RQ2: What challenges and concerns do EFL teachers experience or anticipate when integrating AI tools into their teaching?
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RQ3: What professional development needs and support structures do EFL teachers identify as necessary for effective AI integration?
Together, these questions provide an exploratory account of EFL teachers’ reported AI practices, perceived benefits, concerns, and professional support needs.
Contribution statement
This article makes an empirical and practical contribution by providing exploratory evidence about EFL teachers’ AI practices, perceived educational opportunities, concerns, and professional support needs in an underrepresented educational context. Conceptually, TAM draws attention to participants’ perceptions of the educational value of AI, while AI-TPACK provides a perspective for considering the pedagogical, ethical, and professional knowledge associated with AI integration. The study does not propose or test an extension of either framework. Its findings identify issues that may inform professional development and guide larger studies using validated instruments.
Literature review
AI integration in EFL education
The integration of AI into language education has produced a robust and rapidly growing body of research. Systematic reviews indicate that AI tools are most commonly used for writing instruction, grammar correction, vocabulary development, and automated assessment (Lo et al., 2024; Memarian & Doleck, 2023). Daud et al. (2025) conducted a PRISMA guided systematic review and found that 54.55% of studies focused on writing assistance, while challenges such as academic dishonesty and overreliance on AI were reported in 27.27% of the reviewed articles. These findings highlight AI’s current pedagogical role in text-based language skills and signal ongoing concerns about its broader educational impact.
AI chatbots and large language models have been shown to serve as “non-threatening” practice partners that reduce speaking anxiety and increase willingness to communicate (Du & Daniel, 2024; Zhang et al., 2024). Experimental studies have demonstrated significant gains in writing performance when AI tools are used for formative feedback. For example, Mahapatra (2024) reported a large effect size (η2 = 0.902) when comparing experimental and control groups in an AI-assisted writing intervention, while Song and Song (2023) found statistically significant post-test differences in a randomized controlled trial. At the same time, comparative studies indicate that human feedback still provides superior qualitative nuance, supporting a “hybrid” model that combines AI efficiency with teacher expertise (Steiss et al., 2024; Zeevy Solovey, 2024).
Teacher perceptions of AI in language teaching
Research on teacher perceptions of AI has grown substantially since 2023, though it remains less developed than research focused on students. Teachers report that AI reduces their workload and improves instructional efficiency, with lesson planning and material creation identified as the most common applications.
However, teachers’ perceptions are not uniformly positive. Studies have identified concerns about academic integrity, student overreliance, diminished creativity, and the limitations of AI in fostering critical thinking (Daud et al., 2025; Xue et al., 2025). A qualitative study of Indonesian EFL teachers found that while most view AI as a supportive tool, they emphasize that it cannot replace the human role in teaching, particularly in building rapport, responding to emotional cues, and adapting to real-time classroom dynamics (Firdaus et al., 2025).
TAM and AI-TPACK as complementary perspectives
The Technology Acceptance Model (TAM), developed by Davis (1989), remains the most widely used framework for studying teacher AI adoption. According to TAM framework, perceived usefulness and perceived ease of use are central determinants of technology acceptance. Recent extensions of TAM for AI in education have added constructs such as trust, self-efficacy, and hedonic motivation (Cai et al., 2024; Tram et al., 2024).
However, several scholars have argued that TAM alone is insufficient for understanding AI adoption in educational settings, particularly when pedagogical, ethical, and institutional factors are considered (Al-Abdullatif, 2024; Celik, 2023; Xue et al., 2025). The Technological Pedagogical Content Knowledge (TPACK) framework, originally proposed by Mishra and Koehler (2006), provides a complementary perspective by emphasizing the intersection of technological, pedagogical, and content knowledge. Celik (2023) expanded TPACK into the “Intelligent-TPACK” framework, incorporating ethical assessment as a key dimension. This expanded model demonstrated strong fit indices (RMSEA = 0.037, CFI = 0.980) and showed that ethical assessment positively influences technological pedagogical knowledge.
TAM and AI-TPACK address different but related aspects of educational technology use. TAM focuses primarily on teachers’ evaluations of a technology, including whether they consider it useful and manageable. AI-TPACK focuses on the professional knowledge required to select, evaluate, adapt, and use AI tools in relation to pedagogy and subject content. Its AI specific formulations also draw attention to ethical evaluation. Positive perceptions of AI therefore do not necessarily indicate that teachers possess the professional knowledge and judgement required for effective classroom integration.
The conceptual connection lies in the distinction between acceptance and pedagogical integration. TAM helps explain why teachers may be inclined to use AI, whereas AI-TPACK helps identify the knowledge and judgement required to use it responsibly in a specific educational context. For example, a teacher may regard AI as useful for generating materials but may still require support to evaluate the accuracy of its outputs, adapt materials to learner needs, protect academic integrity, and align AI use with learning objectives.
Previous studies have combined TAM and TPACK variables within explanatory models. However, the present study does not test such a model. TAM and AI-TPACK are used as complementary interpretive perspectives: TAM helps contextualise teachers’ evaluations of AI, while AI-TPACK helps contextualise their pedagogical concerns and professional support needs.
Recent work has further integrated TAM and TPACK. A study by Firdaus et al. (2025) involving 436 Indonesian preservice EFL teachers found that TPACK significantly impacts both AI behavioral intention (β = 0.350) and actual AI use (β = 0.557), while perceived ease of use drives perceived usefulness (β = 0.674). These studies demonstrate that TAM and TPACK variables can be examined together in explanatory research. In the present exploratory survey, however, they are used only to organise the literature and interpret descriptive findings.
Teacher professional development for AI integration
The literature consistently identifies professional development as a critical enabler of effective AI integration. A TPACK-based professional development program for novice EFL teachers demonstrated significant improvements in knowledge of AI-powered tools, teaching skills, and self-efficacy (Zulianti et al., 2024). Similarly, Moorhouse et al. (2024) found that structured training helped teachers recognize the need to tailor AI-generated content to student levels, moving beyond uncritical adoption.
Despite these promising interventions, studies consistently report that most teachers lack adequate preparation. Wan et al. (2025) found that even among teachers who actively use AI, institutional clarity and perceived control over student learning significantly influence their integration practices. In the Western Balkans and Kosovo, discussions about AI integration in higher education are emerging, but published evidence remains limited (Pireci Sejdiu & Sejdiu, 2025).
Research gap
While the literature has expanded rapidly, several gaps remain. Most teacher perception studies originate from East and Southeast Asian contexts, with limited representation from the Western Balkans and under-resourced educational settings.
Although research on teachers’ acceptance of AI has expanded, an important conceptual issue remains. Acceptance oriented studies generally focus on perceived usefulness, perceived ease of use, and intentions to adopt technology. These concepts help explain favourable evaluations of AI but provide limited insight into whether teachers possess the pedagogical knowledge, ethical awareness, and contextual judgement required to integrate it appropriately into EFL teaching. In contrast, AI-TPACK research focuses on teachers’ professional knowledge and pedagogical integration, but it does not always examine how these requirements coexist with teachers’ perceived value of AI.
The conceptual gap therefore concerns how teachers’ positive perceptions of AI coexist with their pedagogical concerns, ethical reservations, and perceived professional support needs. This issue remains insufficiently documented in the Western Balkans, including Kosovo. The present exploratory survey addresses this contextual and conceptual gap by describing EFL teachers’ AI practices, perceived educational opportunities, concerns, and support needs. TAM and AI-TPACK provide complementary concepts for interpreting these patterns, but the study does not test relationships between the frameworks.
TAM explains how perceived usefulness, perceived ease of use, and related attitudinal constructs shape teachers’ intentions to use AI tools in their practice (Cai et al., 2024; Liu and Ma, 2024). AI-TPACK highlights the knowledge, ethical awareness, and pedagogical judgment needed to integrate these tools meaningfully into specific teaching contexts, including teachers’ capacity to evaluate AI outputs and make context-sensitive decisions about their use (Celik, 2023). By bringing these perspectives together, the study captures both attitudinal drivers of AI adoption and the broader pedagogical and ethical conditions under which EFL teachers in Kosovo and similar settings can use AI in responsible and educationally productive ways (Omidvar & Meihami, 2025).
Methodology
Research design
This study adopted an exploratory descriptive survey design. Data were collected through a single questionnaire containing demographic and AI use questions, ten Likert items, and three open ended questions. The study was designed to describe participants’ reported practices, perceptions, concerns, and support needs. It was not designed to test hypotheses, estimate relationships among theoretical constructs, or evaluate a technology acceptance model.
The closed ended responses provided descriptive patterns through frequencies, percentages, and response distributions. The open ended responses provided brief explanations and examples that helped contextualise these patterns. Because the open ended component consisted of three brief questions within the same questionnaire, the study is presented as a descriptive survey with open ended responses rather than as a fully developed mixed methods study.
Theoretical framework
The study is based on an integrated framework that combines the Technology Acceptance Model with the AI-TPACK perspective on teacher knowledge (Celik, 2023; Davis, 1989). TAM structures the attitudinal components of the questionnaire, especially items related to perceived usefulness, perceived ease of use, confidence, and concerns about AI replacing teaching tasks, which are central constructs in recent AI acceptance studies in language education (Liu & Ma, 2024). These constructs help explain why teachers choose to adopt, adapt, or limit AI use in their practice.
At the same time, the AI-TPACK perspective informs the interpretation of items related to professional development, ethical concerns, and perceived readiness. This framework emphasizes that effective AI integration requires coherent combinations of technological, pedagogical, and content knowledge, along with the ability to evaluate AI-based decisions critically and ethically (Celik, 2023). Viewing the findings through this combined lens allows for moving beyond a narrow focus on acceptance to consider how teachers’ knowledge, values, and institutional conditions shape their AI practices and perceived needs for support (Moorhouse et al., 2024; Omidvar & Meihami, 2025).
Participants
Twenty-eight EFL teachers participated in the study, as shown in
Table 1, representing diverse educational contexts. The sample included teachers from primary (28.6%), secondary (39.3%), undergraduate (17.9%), graduate (10.7%), and IELTS Academic (3.6%) levels. Gender distribution was relatively balanced, with 57.1% female and 42.9% male participants. Age distribution was concentrated in the 25–34 range (42.9%), followed by 35–44 (28.6%) and 45–54 (21.4%). Teaching experience was well distributed, with 32.1% reporting 15 or more years, 28.6% reporting 8–14 years, 21.4% reporting 0–2 years, and 17.9% reporting 3–7 years. The majority (89.3%) reported familiarity with AI tools used in education, while 10.7% were unsure.
Table 1. Participant demographics.
| Characteristic | Category | n |
% |
|---|
| Gender | Female | 16 | 57.1 |
| Male | 12 | 42.9 |
| Age | Under 25 | 1 | 3.6 |
| 25–34 | 12 | 42.9 |
| 35–44 | 8 | 28.6 |
| 45–54 | 6 | 21.4 |
| 55+ | 1 | 3.6 |
| Teaching level | Primary | 8 | 28.6 |
| Secondary | 11 | 39.3 |
| Undergraduate | 5 | 17.9 |
| Graduate | 3 | 10.7 |
| IELTS Academic | 1 | 3.6 |
| Experience | 0–2 years | 6 | 21.4 |
| 3–7 years | 5 | 17.9 |
| 8–14 years | 8 | 28.6 |
| 15+ years | 9 | 32.1 |
| AI familiarity | Yes | 25 | 89.3 |
| Not sure | 3 | 10.7 |
Data collection instrument
A semi-structured questionnaire was developed with four sections. Section 1 collected demographic information (age, gender, teaching level, and years of experience). Section 2 assessed AI familiarity, tool usage, and frequency of use. Section 3 included 10 Likert scale items on a five-point scale (strongly disagree to strongly agree) organised into two broad content domains for descriptive reporting: (a) perceived educational value of AI (five items addressing writing improvement, lesson planning efficiency, learning personalization, feedback utility, and student engagement) and (b) teacher attitudes and concerns (five items addressing confidence, role anxiety, training adequacy, content accuracy concerns, and task replacement beliefs). Section 4 included three open-ended questions inviting teachers to describe (a) the most positive impact of AI, (b) the most challenging or concerning aspect, and (c) the type of training or support needed.
Items on perceived usefulness and ease of use were aligned with validated TAM formulations, while items on training, ethical concerns, and role anxiety were based on emerging frameworks for teacher AI readiness and professional competence (Chiu et al., 2024; Moorhouse et al., 2024). The draft questionnaire was piloted informally with three EFL teachers to check clarity and relevance; minor wording changes were made before full distribution.
The first content domain concerned the perceived educational value of AI and included five items addressing writing improvement, lesson planning, learning personalisation, feedback, and student engagement. The second concerned teacher attitudes and concerns and included five items addressing confidence, training adequacy, content accuracy, task replacement, and the teacher’s professional role. These domains were used to organise item level reporting and should not be interpreted as validated latent constructs or composite scales.
The questionnaire topics were informed by previous research on technology acceptance, AI-TPACK, teacher competence, and responsible AI use. However, the instrument did not operationalise the complete TAM or AI-TPACK frameworks. It did not contain complete multi item measures of perceived usefulness, perceived ease of use, behavioural intention, actual adoption, or the separate technological, pedagogical, and content knowledge dimensions of AI-TPACK. The theoretical frameworks therefore informed the selection of questionnaire topics and the interpretation of the findings but were not directly measured or tested.
Three EFL teachers reviewed the preliminary questionnaire for clarity and relevance before data collection. Their comments resulted in minor wording revisions. This process constituted an informal clarity check rather than formal validation. Reliability coefficients were not calculated because the items addressed heterogeneous aspects of educational value, confidence, training, professional role, and concern, and no composite scale scores were created. The questionnaire should therefore be regarded as exploratory, and the closed ended findings are reported at the individual item level.
Data analysis
Quantitative data were analyzed using descriptive statistics, including frequencies, percentages, and response distributions. Qualitative responses from the three open-ended questions were analyzed using inductive thematic analysis following Braun and Clarke’s (2006) six-phase approach. Responses were first read and reread for familiarization, then systematically coded. Initial codes were collated into potential themes, which were then reviewed, refined, and named.
The two forms of evidence were merged during interpretation through narrative integration. Themes identified in the open ended responses were compared with conceptually related closed ended findings. For example, comments concerning lesson preparation were considered alongside the item addressing lesson planning efficiency, while comments concerning content reliability and professional support were considered alongside the corresponding closed ended items. The open ended responses were used to elaborate, contextualise, or qualify the descriptive patterns. They were not treated as an independent source of confirmation or as evidence of methodological triangulation.
Ethical considerations
The study followed institutional ethical guidelines for research involving human participants. A written informed consent statement was obtained from all examinees. Before the distribution of the questionnaires, the examinees were advised on: (1) the information obtained from the questionnaires would be used for scientific purposes only‚ (2) their identity and privacy would be kept fully confidential‚ and (3) they were free to withdraw from the study at any time. Participant privacy was protected through anonymized data collection, and no personally identifying information was stored. Participation was voluntary, and respondents were informed of their right to withdraw at any time.
Results
AI tool usage practices
Regarding frequency of use presented in
Table 2, the largest group of teachers reported using AI “sometimes” (46.4%, n = 13), followed by “often” (35.7%, n = 10), “rarely” (14.3%, n = 4), and “always” (3.6%, n = 1). No participant reported never using AI tools. Combining “often” and “always,” 39.3% of teachers were frequent users, while 60.7% used AI less frequently.
Table 2. Frequency of AI tool use.
| Frequency | n |
% |
|---|
| Always | 1 | 3.6 |
| Often | 10 | 35.7 |
| Sometimes | 13 | 46.4 |
| Rarely | 4 | 14.3 |
| Never | 0 | 0.0 |
Tool preferences in
Table 3 showed a clear dominance of ChatGPT, used by 89.3% (n = 25) of participants. AI-based quiz and test generators were the second most popular category (57.1%, n = 16), followed by Grammarly (50.0%, n = 14) and Quillbot (39.3%, n = 11). Text-to-speech and speech-to-text tools were used by 28.6% (n = 8) of participants. Other tools, including Gemini, NotebookLM, Canva, Flippity, and Perplexity, received isolated mentions.
Table 3. AI tools used by teachers.
| AI Tool | n |
% |
|---|
| ChatGPT | 25 | 89.3 |
| AI based quiz/test generators | 16 | 57.1 |
| Grammarly | 14 | 50.0 |
| Quillbot | 11 | 39.3 |
| Text to speech/speech to text | 8 | 28.6 |
| Other (Gemini, Canva, Perplexity, etc.) | 5 | 17.9 |
Perceived educational value of AI
Likert scale responses in
Table 4 regarding the educational value of AI revealed a nuanced pattern. Teachers showed the strongest agreement with learning personalisation, followed by lesson planning efficiency, while agreement was lower for writing improvement and student engagement.
Table 4. Teachers’ perceived educational value of AI (Likert Scale Responses).
| Statement | SA | A | N | D | SD |
|---|
| AI makes lesson planning more efficient | 12 (42.9%) | 7 (25.0%) | 5 (17.9%) | 1 (3.6%) | 3 (10.7%) |
| AI can help personalize learning | 9 (32.1%) | 11 (39.3%) | 4 (14.3%) | 0 (0.0%) | 4 (14.3%) |
| AI provides useful feedback for learners | 5 (17.9%) | 12 (42.9%) | 6 (21.4%) | 5 (17.9%) | 0 (0.0%) |
| AI encourages student engagement | 7 (25.0%) | 8 (28.6%) | 8 (28.6%) | 3 (10.7%) | 2 (7.1%) |
| AI helps students improve writing | 2 (7.1%) | 9 (32.1%) | 13 (46.4%) | 3 (10.7%) | 1 (3.6%) |
Learning personalisation received the highest combined agreement (71.4%), followed by lesson planning efficiency (67.9%), feedback utility (60.7%), student engagement (53.6%), and writing improvement (39.3%). Writing improvement also produced the highest proportion of neutral responses (46.4%). This pattern suggests that teachers view AI as more useful for behind-the-scenes planning and individualization than for directly improving specific language skills.
Teacher attitudes and concerns
The second set of Likert items in
Table 5, captured teachers’ confidence, anxieties, and beliefs about AI’s impact on their professional role.
Table 5. Teacher attitudes and concerns (Likert Scale Responses).
| Statement | SA | A | N | D | SD |
|---|
| I feel confident using AI tools | 6 (21.4%) | 13 (46.4%) | 6 (21.4%) | 3 (10.7%) | 0 (0.0%) |
| I am concerned about AI content accuracy | 5 (17.9%) | 13 (46.4%) | 5 (17.9%) | 2 (7.1%) | 3 (10.7%) |
| I have received enough training | 5 (17.9%) | 4 (14.3%) | 6 (21.4%) | 10 (35.7%) | 3 (10.7%) |
| AI could replace some teaching tasks | 3 (10.7%) | 9 (32.1%) | 7 (25.0%) | 6 (21.4%) | 3 (10.7%) |
| I worry AI will reduce my role | 3 (10.7%) | 1 (3.6%) | 6 (21.4%) | 14 (50.0%) | 4 (14.3%) |
Most teachers (67.9%) expressed confidence in using AI tools, consistent with the high familiarity rate (89.3%). A substantial majority (64.3%) were concerned about the accuracy of AI-generated content. Only 32.1% agreed that they had received sufficient training, while 46.4% disagreed. These item level responses indicate a descriptive discrepancy between reported confidence and perceived training adequacy. They do not constitute a validated measure of overall AI readiness. Regarding professional displacement, 64.3% of teachers were not concerned that AI would reduce their role, suggesting resilience in professional identity. However, 42.9% agreed that AI could replace some teaching tasks, indicating a pragmatic acknowledgment of AI’s potential rather than an existential threat.
To examine whether confidence varied by career stage, teaching experience was cross-tabulated with confidence in using AI tools.
To examine whether AI confidence clustered at specific career stages, a simple cross-tabulation of teaching experience by confidence in using AI tools was created (
Table 6). The results showed that high confidence (agree or strongly agree) was distributed relatively evenly across all experience groups, ranging from 55.6% among teachers with 15 or more years of experience to 100% among those with 3–7 years of experience. Notably, teachers with 15 or more years of experience reported confidence levels comparable to those with 0–2 or 3–7 years of experience, indicating that willingness to work with AI is not limited to early career teachers. Given the small sample size (N = 28), no inferential tests were conducted, but this descriptive pattern aligns with studies reporting limited or inconsistent demographic effects on teacher AI acceptance (Alharbi, 2024; Slamet, 2024).
Table 6. Teaching experience by confidence in using AI tools.
| Years of teaching experience | Low confidence (SD/D) | Neutral (N) | High confidence (A/SA) | Total |
|---|
| 0–2 years | n = 0 | n = 2 | n = 4 | n = 6 |
| 3–7 years | n = 0 | n = 0 | n = 5 | n = 5 |
| 8–14 years | n = 1 | n = 2 | n = 5 | n = 8 |
| 15+ years | n = 2 | n = 2 | n = 5 | n = 9 |
| Total
| n = 3 | n = 6 | n = 19 | N = 28 |
Reported challenges
The multiple-choice question on challenges revealed four main concerns (
Table 7). Ethical issues (cheating, misinformation) and student overreliance on AI were reported by 64.3% of teachers. Limited access to appropriate technology was cited by 46.4%, and lack of training by 42.9%. Time constraints in lesson planning or adaptation were mentioned by 14.3%.
Table 7. Challenges reported by teachers.
| Challenge | n |
% |
|---|
| Ethical concerns (cheating, misinformation) | 18 | 64.3 |
| Student over reliance on AI tools | 18 | 64.3 |
| Limited access to appropriate technology | 13 | 46.4 |
| Lack of training | 12 | 42.9 |
| Time constraints in lesson planning | 4 | 14.3 |
Qualitative findings: positive impacts
Thematic analysis of the open-ended question on positive impacts identified four primary themes. Teacher responses showed a clear trend in the positive impacts that teachers associated with AI in the classroom.
Theme 1: Time Efficiency in Lesson Planning.
The most frequently mentioned benefit was saving time on lesson preparation and materials development. It was identified by 14 teachers, representing 51.9% of the respondents. Teachers described AI as a tool that significantly reduces the workload of creating lesson plans, tests, worksheets, and differentiated activities. One teacher stated, “Fast ways to prepare lesson plans, tests, etc.” Another explained, “I do not use AI in the classroom, except rarely, but I use it in the preparation of my lesson plans. And this is incredibly time-saving.” A third noted, “Reducing lesson planning time and having more time to focus on students and activities.”
Theme 2: Personalization and Differentiation.
Teachers valued AI’s ability to tailor content to different student needs and levels. It was mentioned by 9 teachers (33.3%). One respondent wrote, “AI has helped personalize student learning activities. It allows me to tailor lessons to different learning styles and provide immediate feedback.” Another stated, “Having the opportunity to create different tasks to meet the needs of various students, especially those with learning needs or neurodivergent students.”
Theme 3: Student Engagement.
Several teachers noted that AI increased engagement and motivation. It was mentioned by 5 teachers (18.5%). One described how AI “helps teachers come up with creative ideas that make learning fun and an unforgettable experience for students.” Another mentioned “better engagement” as the primary impact, while a third cited “personalized learning paths, instant feedback and support, and increased student engagement.”
Theme 4: Creative Instructional Ideas.
A smaller but notable group of teachers valued AI as a source of instructional creativity. This theme was reported by 6 teachers (22.2%). One teacher noted that AI “provides creative and interactive ideas,” while another described using AI for “specific lesson planning and more creative approaches in practice sessions.”
Qualitative findings: challenges and concerns
Thematic analysis of the open-ended question on challenges revealed five themes.
Theme 1: Student Over reliance and Loss of Critical Thinking.
The most prominent concern was that students become excessively dependent on AI tools, which erodes their capacity for independent thinking and authentic language production. It was mentioned by 10 teachers (37.0%). One teacher stated, “The most concerning issue is students’ overreliance on AI-generated content. Many are becoming too dependent on these tools, which affects their creativity, critical thinking, and attention span.”Another teacher emphasized the need to prevent student overreliance on AI while still fostering independent critical thinking.”
Theme 2: Accuracy and Trustworthiness of AI Content.
Teachers expressed concern about the reliability of AI generated information and the challenge of verifying its accuracy. They were mentioned by 6 teachers (22.2%). “Teachers reported that they spend considerable effort verifying the accuracy of AI generated information and adapting responses for pedagogical use.”
Theme 3: Erosion of Creativity.
A distinct theme emerged regarding the concern that AI diminishes both student and teacher creativity. It was identified by 7 teachers (25.9%). “Teachers expressed concern that AI may diminish both student and teacher creativity.” They also expressed fear that AI is “making us passive teachers.”
Theme 4: Academic Integrity.
Several teachers raised ethical concerns about cheating and plagiarism. It was reported by 4 teachers (14.8%). One teacher noted, “Students use ChatGPT to write papers assigned as homework.” Another identified concerns about academic integrity and cheating, overreliance and eroded critical thinking, and equity and access disparities.
Theme 5: Infrastructure and Access Barriers.
Teachers in some contexts reported practical barriers. They were mentioned by 3 teacher (11.1%). One teacher stated, “Sometimes we do not have access to the internet.” Another cited limited resources as a primary obstacle.
Qualitative findings: training and support needs
Thematic analysis of the training question identified four themes.
Theme 1: Hands on, Practical Training.
The most common request was for practical, experiential training sessions. This theme was identified by 13 teachers (46.4%). Teachers asked for “hands-on training with clear, practical guidelines,” “workshops regarding the use of AI,” and training that takes a “hands-on approach to training for specific AI tools.”
Theme 2: Ethical and Responsible Use Training.
Teachers sought guidance on navigating ethical dimensions. It was mentioned by 5 teachers (17.9%). One respondent requested “training related to ethical considerations of AI use” along with “an overview of different tools for different purposes.” Another asked for “practical integration strategies (lesson planning and curriculum alignment), prompt engineering skills, ethical use, and policy development.”
Theme 3: Tool specific and Contextualized Training.
Teachers expressed a need for training tailored to specific tools and local contexts. This theme was mentioned by 8 teachers (28.6%). One teacher asked for guidance on how to use websites like MagicSchool AI and Quizziz AI to generate materials and resources. Another requested training that considers the needs of local-level learners.
Theme 4: Foundational Technology Training.
Six teachers (21.4%) identified a need for more basic technology integration skills before advancing to AI. One teacher stated, “We as teachers need basic training on incorporating technology in the classroom first, and then we can expand our goals to AI tools.”
Discussion
AI as a lesson planning tool: pragmatic adoption
The descriptive findings are broadly consistent with recent research documenting teachers’ pragmatic adoption of AI tools. The dominance of ChatGPT reflects global patterns reported in recent systematic reviews, where ChatGPT and similar generative AI tools have become the primary entry point for educators exploring AI-assisted instruction. The emphasis on lesson planning efficiency as the top perceived benefit aligns with findings in studies from other EFL contexts, where teachers consistently identify materials development and lesson preparation as the most valued applications of AI.
This descriptive pattern is compatible with TAM’s concept of perceived usefulness because participants particularly valued AI for practical professional tasks (Davis, 1989). Teachers in this study clearly found AI useful for reducing workload and saving time, both direct indicators of perceived usefulness. The relatively lower agreement on writing improvement compared to lesson planning suggests that teachers view AI as more effective for their own professional tasks than for directly improving students’ language skills, a distinction with important implications for how AI is positioned in teacher training programs.
However, the study did not administer a complete TAM instrument or examine relationships among TAM constructs. This pattern should therefore be interpreted as descriptive evidence of perceived educational value rather than as confirmation of TAM.
Reported confidence and perceived training adequacy
The findings show a descriptive contrast between participants’ confidence in using AI tools and their assessments of the training they had received. One possible interpretation is that some participants developed familiarity with AI through independent experimentation or informal learning. However, this interpretation remains tentative because the questionnaire did not investigate how confidence was developed, assess actual competence, or measure readiness using a validated scale. The result is therefore interpreted as a confidence and training discrepancy rather than as evidence of a readiness gap.
The open ended responses provide additional context because participants requested practical workshops, guidance on specific tools, support for pedagogical integration, and training in responsible use. These requests suggest that reported confidence in operating AI tools does not necessarily remove the perceived need for structured professional support.
TAM and AI-TPACK as interpretive perspectives
The descriptive findings can be considered through TAM and AI-TPACK, but they do not provide a test of either framework. Participants’ positive perceptions of AI for lesson planning, personalisation, and materials development are compatible with TAM’s concept of perceived usefulness. Their concerns about content accuracy, ethical use, and professional training can be considered through an AI-TPACK perspective, which directs attention to the knowledge and judgement required for pedagogically appropriate AI integration. However, the study did not administer complete TAM or AI-TPACK scales, test relationships among theoretical constructs, or evaluate an extended model. The frameworks are therefore used only to organise and interpret the descriptive findings.
Coexisting perceived benefits and concerns
Participants reported both practical benefits and concerns regarding AI. They valued AI for lesson preparation, materials development, differentiation, and creative instructional ideas. At the same time, they expressed concerns about content accuracy, student dependency, academic integrity, creativity, and critical thinking. These findings suggest that favourable perceptions of AI can coexist with reservations about its classroom use.
This coexistence should not be interpreted as a new theoretical construct or as evidence of a stable professional identity. The findings are based on a small sample and an exploratory questionnaire. They identify issues that could be examined more closely through interviews, classroom observations, and validated measures.
Ethical concerns as a primary barrier
Ethical concerns, such as cheating and misinformation, and student overreliance emerged as the most frequently cited challenges, each reported by 64.3% of teachers. This finding is consistent with the international literature. A systematic review of EFL teacher perspectives found that cheating and plagiarism are among the most frequently cited concerns worldwide (Daud et al., 2025). Similarly, Daud et al. (2025) reported that overreliance on AI was identified as a challenge in above-mentioned reviewed studies. These concerns are also evident in broader higher education debates, where educators question whether they are grading students or grading ChatGPT, and note that conventional plagiarism detection tools are increasingly ineffective against sophisticated AI-generated prose (Pireci Sejdiu & Sejdiu, 2025).
Ethical concerns were prominent in participants’ responses, particularly in relation to academic integrity, misinformation, content accuracy, and student overreliance. However, the present study did not test whether these concerns predicted or moderated teachers’ AI use. Ethical concerns should therefore be understood as reported perceptions rather than as variables with an empirically established moderating effect. Future research could use validated measures and an appropriate explanatory design to examine whether ethical concerns influence the relationship between teachers’ perceptions of AI and their classroom use.
Convergence and divergence with student perceptions
The findings of this teacher study can be considered alongside evidence from Kosovan EFL students, who also report intensive use of AI tools for language learning and a complex mix of enthusiasm and concern (Braha, 2026). Both groups identify grammar, writing, and vocabulary as the main skill domains supported by AI tools, and both emphasize time savings, instant feedback, and ease of access as central benefits. In this sense, teachers and students share a broadly pragmatic orientation toward AI, treating it as a convenient assistant for structurally focused tasks rather than a transformative replacement for human instruction.
However, there are also notable differences between teachers’ and students’ beliefs. Students report a pronounced trust–usability paradox: 85.9 percent find AI tools easy to use, yet only 34.1 percent trust the feedback they receive (Braha, 2026). Teachers in the present study, by contrast, show comparatively high confidence in using AI tools but focus their distrust on downstream pedagogical risks, such as student overreliance, academic integrity, and the erosion of creativity, rather than on their own immediate interaction with the tools. While students frame trust primarily in terms of the reliability of error correction and content accuracy, teachers extend trust concerns to broader questions of ethical responsibility, classroom control, and the long-term development of critical thinking.
These differences suggest that trust operates at two distinct levels in AI-mediated language education. At the learner level, trust concerns focus on the correctness and usefulness of specific feedback episodes. At the teacher level, trust concerns encompass systemic issues, including assessment validity, equitable access, and the preservation of human roles and pedagogical values. Integrating these perspectives indicates that future theoretical models should treat trust not as a single construct but as a layered phenomenon, with separate components for micro-level interactional trust in feedback and macro-level institutional and pedagogical trust in AI’s role within the curriculum.
Alignments and gaps in teacher–student perceptions
To clarify these convergences and divergences,
Table 8 summarizes key similarities and differences between Kosovan EFL teachers in the present study and Kosovan EFL students reported in Braha (2026) regarding tools used, purposes, perceived benefits, concerns, and trust–usability patterns.
Table 8. Alignment and differences between Kosovan EFL teachers and students.
| Dimension | EFL teachers (present study) | Kosovan EFL students (Braha, 2026) |
|---|
| Main AI tools used
| ChatGPT dominant (89.3%); AI based quiz and test generators; Grammarly; Quillbot. | Google Translate and ChatGPT equally dominant (68.2% each); Grammarly and Quillbot widely used. |
| Primary purposes
| Lesson planning, materials creation, quiz and test generation, preparation of differentiated tasks. | Vocabulary, grammar, and writing support; translation; exam and assignment preparation; feedback on mistakes. |
| Most valued benefits
| Time efficiency in lesson planning; workload reduction; support for personalization and differentiation; creative ideas for activities. | Instant feedback and correction; time efficiency and accessibility; personalized and adaptive learning; vocabulary expansion; enjoyment and engagement. |
| Main concerns
| Student over reliance on AI; loss of creativity and critical thinking; academic integrity and cheating; accuracy of AI content; infrastructure and access barriers. | Accuracy and reliability of feedback; overdependence on AI; limited contextual and cultural understanding; ethical and integrity issues. |
| Trust and usability pattern
| High confidence in using AI; strong concerns about accuracy and ethical use in the classroom; fear of pedagogical deskilling (“passive teachers”). | Very high perceived ease of use (85.9%) but low trust in AI feedback (34.1%); empirically documented trust–usability paradox. |
| Perceived role of AI in teaching/learning
| Viewed as powerful support tool for planning and differentiation, not a replacement for human teaching; anxiety about role erosion is limited but present. | Viewed as helpful assistant for language improvement, but not a substitute for teachers, especially for ideas, experiences, and oral communication skills. |
Implications for professional development
The findings indicate that many teachers already use AI tools for lesson planning, materials development, and differentiation, yet report limited formal training and ongoing uncertainty about ethical and pedagogical use. This pattern suggests that professional development should go beyond basic tool demonstrations toward systematic development of AI literacy and AI-TPACK. Recent studies show that targeted programs can improve teachers’ knowledge of AI-powered tools, their ability to design AI-supported lessons, and their self-efficacy in using these tools (Enhancing Novice EFL Teachers’ Competency in AI-Powered Tools Through a TPACK-Based Professional Development Program, 2024; Effectiveness of a Professional Development Program for AI Literacy, 2024). These results reinforce the need for structured, sustained training rather than leaving teachers to experiment individually.
In this study, teachers explicitly requested practical, context-sensitive support, including concrete examples of prompts, guidance on integrating AI into existing curricula, and strategies for addressing academic integrity and overreliance among students. These needs align with emerging frameworks that conceptualize professional generative AI competence as a combination of technological proficiency, pedagogical alignment, ethical awareness, and preparation of students for AI-rich environments (Chapelle, 2025; Moorhouse et al., 2024). Professional development in the Kosovan and broader EFL context would therefore benefit from combining hands-on workshops focused on specific teaching scenarios with explicit discussion of ethical issues, local policy expectations, and ways to maintain creativity and critical thinking when AI is present. Designing professional development around authentic teacher tasks, such as co-creating writing prompts with AI, revising AI-generated materials, or modeling critical evaluation of AI outputs in class, can help teachers develop integrated AI-TPACK rather than isolated technical skills.
The contrast between reported confidence and perceived training adequacy suggests that professional development should account for teachers’ existing experience with AI and their stated support needs. The cross-tabulation showed that high confidence in using AI tools was not limited to early-career teachers, indicating that both novice and experienced teachers can be leaders in AI integration. International research on AI readiness and AI literacy among teachers indicates that confidence, perceived usefulness, access to resources, institutional support, and clear guidelines interact in shaping adoption and responsible use (Al-Abdullatif, 2024; Luckin et al., 2022). Institutions should therefore design multi-level professional learning pathways that combine foundational AI literacy with advanced modules on assessment, feedback, and student guidance, and recognize teachers as co-designers of responsible AI use in language education rather than passive recipients of technology.
Limitations and future research
This study has several limitations that should be considered when interpreting the findings. First, the teacher sample was obtained through convenience sampling and was relatively small, which limits statistical power and the ability to detect more fine-grained patterns in attitudes and practices. Participants were self-selected EFL teachers with prior familiarity with AI tools, likely overrepresenting early adopters and those interested in experimenting with new technologies. Given the convenience sample and small sample size, the findings should be interpreted as context specific. Ethical concerns were identified descriptively through closed and open ended responses. The study did not test whether they predicted, mediated, or moderated AI use.
An important limitation concerns the exploratory questionnaire. Although its content was informed by relevant theoretical and empirical literature, the study did not establish content validity, construct validity, or reliability through formal psychometric procedures. The questionnaire did not operationalise the complete TAM or AI-TPACK frameworks, and no composite scale scores were calculated. The results should therefore be interpreted as item level descriptive evidence rather than as validated measurements of technology acceptance, AI-TPACK, or teacher readiness.
The small convenience sample and descriptive design prevent model testing, causal interpretation, moderation analysis, and broad generalisation. The three open ended questions generated useful explanatory comments, but their brevity limited the depth normally associated with a substantial qualitative component. The findings should therefore be regarded as preliminary and context specific.
Second, the study is situated in Kosovo, an educational context shaped by post conflict reconstruction and limited resources, which is both a strength and a limitation. It provides much-needed evidence from a setting largely absent in current AI in language education research, but the specific institutional conditions, digital infrastructure, and policy environment may differ significantly from those in other regions (OECD, 2018; Xue et al., 2024). At the same time, Kosovo is part of a broader regional conversation in which generative AI is viewed as both a disruptor and an enabler in higher education, with calls for inclusive policies and critical AI literacies that balance innovation and integrity (Pireci Sejdiu & Sejdiu, 2025). Therefore, transferability of the findings to other countries should be based on reasoned comparison rather than direct generalization, considering similarities and differences in teacher education systems, access to AI tools, and national regulations.
Third, the design captures teacher perceptions at a single point in time and relies on self-report questionnaire data, without classroom observations or objective measures of AI use. Self-report data are vulnerable to social desirability and recall bias, and cross sectional snapshots cannot show how teachers’ AI practices and levels of trust evolve as tools, policies, and training opportunities change (Memarian & Doleck, 2023; Yang & Li, 2024). The study also analysed teacher and student data (Braha, 2026) in separate projects, which allowed for a detailed focus in each article but limited the ability to examine teacher–student dynamics within the same institutional settings.
Future research
Future research should address these limitations in three main ways. First, larger scale studies using stratified or probability based sampling are needed to examine whether the patterns identified here hold across different regions, school levels, and institutional types, both within Kosovo and in other Western Balkan and post conflict contexts (Jiang, 2022; Kundu & Bej, 2025). Such designs would permit more robust modelling of how factors such as institutional support, AI literacy, and subject specialization shape teachers’ AI practices and concerns. Second, mixed teacher–student designs that collect data from both groups in the same institutions would make it possible to analyse how teacher attitudes, classroom practices, and institutional messages interact with student acceptance patterns such as the trust–usability paradox documented in Braha (2026). This type of design would extend the comparative insights outlined in the present Discussion and offer a more systemic view of AI mediated EFL ecologies.
Third, longitudinal and intervention studies are needed to trace how AI related perceptions and competencies develop over time as teachers receive targeted professional development or as institutional policies change. Longitudinal research could examine whether structured professional development changes teachers’ confidence, competence, and perceptions of training adequacy over time. Combining survey data with classroom observations, artefact analysis of AI assisted materials, and possibly learning analytics would further strengthen the evidence base for designing responsible AI integration in EFL teaching across diverse contexts.
Conclusion
This exploratory descriptive survey provides context specific evidence about how a small sample of EFL teachers use and perceive AI tools. Participants particularly associated AI with learning personalisation, lesson planning, materials development, and time efficiency. They also reported concerns about content accuracy, academic integrity, student overreliance, creativity, and critical thinking. The findings further show a descriptive discrepancy between relatively high reported confidence and less positive assessments of training adequacy.
These patterns identify practical priorities for teacher support, including experience with specific AI tools, critical evaluation of AI generated content, pedagogical adaptation, academic integrity, and responsible classroom use. TAM and AI-TPACK help situate these issues conceptually, but neither framework was tested or extended. Given the small convenience sample, exploratory instrument, and descriptive analysis, the results should be treated as preliminary. Larger studies using validated measures are required to investigate relationships among AI acceptance, teacher knowledge, ethical concerns, institutional support, and classroom practice. Study offers a detailed account of EFL teachers’ perceptions of AI tools in the English classroom, revealing a complex picture of pragmatic adoption tempered by significant concerns. Teachers overwhelmingly value AI for lesson planning efficiency and learning personalization, with ChatGPT serving as the dominant tool across educational levels. However, ethical concerns, student overreliance, accuracy issues, and insufficient training create substantial barriers to deeper integration.
Ethical approval statement
This study received ethical approval from the Research Ethics Committee of the University “Ukshin Hoti” Prizren, Kosovo, under reference number 02–253.
