Predictors of Pre-Service EFL Teachers’ Predisposition Towards AI Adoption in Language Teaching

Abstract

The increasing integration of artificial intelligence (AI) in education is creating new challenges and posing new requirements for English language teacher training, especially since AI applications provide numerous opportunities for language acquisition and practice. This study aimed to explore the interplay between three factors—the actual learning of AI, behavioural intention to use AI, and perceived self-efficacy—in order to determine their effect on the readiness of future teachers to implement AI technologies in their prospective pedagogical practices. In total, 56 pre-service English language teachers in Bulgaria took part in an online survey, revealing that the actual learning of AI was a strong determinant of their behavioural intention and perceived self-efficacy to engage with AI. Furthermore, it was also established that the student teachers’ knowledge of AI and competence in delivering AI-supported lessons were strong predictors of their behavioural disposition to integrate AI in their L2 classrooms. The results indicate the necessity of updating teacher training curricula so that consistent AI literacy in future foreign language teachers is fostered; a step forward that has to be supported by policymakers, university programme designers, and teacher training providers.

1. Introduction

Artificial intelligence (AI) has swiftly gained popularity in performing tasks that have traditionally required human intellect and capacities such as reasoning, learning, and interacting. Over the 60 years of their existence, AI technologies have evolved significantly from models that can make predictions and decisions based on the input data analysed to generative applications capable of creating new text, images, music, videos, or even code (e.g., ChatGPT-5, Gemini, and DALL-E, among others). Since generative AI-powered tools are “trained” to use algorithms in order to respond to questions and produce or customise existing content within a limited amount of time, they are increasingly contributing to challenges in the field of education. Their successful implementation rests upon claims of improved efficiency, customised learning experiences, and enriched student engagement (Merino-Campos, 2025; Abrar et al., 2025; Benek, 2025). Still, it is teachers’ pedagogical expertise, as well as attitudes towards the value and feasibility of AI technologies, that can determine their successful integration as practical educational tools (Yim & Wegerif, 2024; Guan et al., 2025).

Empirical data from recent research on teachers’ views of AI highlight that teachers view AI applications as tools that can improve their efficiency in lesson planning, learner assessment, and administrative duties, rather than as co-creators of pedagogical scenarios (Cheah et al., 2025). This perception reflects teachers’ concerns regarding AI integration in educational settings due to doubts about AI’s educational value, ethical issues, and fears that automation may reduce teacher–student interaction. It also illustrates the fact that teachers’ engagement with AI remains at the level of awareness (i.e., knowing about AI’s potential) and rarely progresses to pedagogical readiness (i.e., integrating AI meaningfully to enhance learning experiences). Cheah et al. (2025) attribute this to limited knowledge and hands-on experience with the instructional application of Gen AI and a lack of clear policies and guidance for using Gen AI responsibly in school systems. Other studies (e.g., Kim, 2024; Yue et al., 2024), however, underscore a more persistent challenge: in-service teacher training programmes must ensure the acquisition of pedagogical competencies necessary for practical, ethical, and reasonable teaching with AI. Without such training, teachers risk either avoiding AI or depending on it in ways that may reduce teacher autonomy and learner creativity.

These challenges are not confined only to experienced teachers. They also emerge during teacher education, when future teachers develop foundational AI competencies and attitudes. Pre-service teachers are cognizant of the transformative power of AI applications in education. Nevertheless, they have limited knowledge and pedagogical experience with their integration (Lucas et al., 2025; Ayanwale et al., 2024). The latter indicates that positive attitudes alone do not automatically lead to readiness for full AI implementation in the classroom (Zhang et al., 2025). This is not surprising, since pedagogical readiness to engage meaningfully and ethically with AI, as defined by Lucas et al. (2025), involves more than a positive predisposition towards innovative digital technologies. It demands a combination of personal confidence in one’s AI knowledge and skills, along with solid institutional support manifested as structured teacher education programmes fostering critical training on AI integration. Moreover, pre-service teachers tend to underestimate key ethical concerns linked to AI, including data privacy, the risk of losing core teaching skills, and becoming too dependent on AI (Hsu et al., 2024). These gaps in knowledge could be addressed by teacher education curricula that not only develop the technological competence of future teachers but also foster their ability to apply AI tools critically and conscientiously in various educational settings (Yetkin & Özer-Altınkaya, 2024).

The readiness to independently incorporate AI becomes particularly significant in foreign language education, where these technologies are increasingly becoming an inseparable component of teaching and learning. Though empirical research on the impact of AI technologies on L2 teachers’ preparation and continuous professional development is still in its infancy, previous studies have shed light on the discrepancies between teacher preparation and actual AI use. A British Council global survey of 1348 English teachers from 118 countries (Edmett et al., 2023) disclosed a stark reality: while many teachers use AI tools (e.g., language apps, chatbots, speech recognition software) for lesson planning, material creation, and assessment, only 20% feel adequately prepared to incorporate AI effectively. This finding resonates with Pokrivcakova’s data (Pokrivcakova, 2023), which highlighted the failure of undergraduate teacher training programmes to develop necessary AI skills in nearly half of the 137 approached Slovak pre-service EFL teachers. The barriers to sufficient AI knowledge and skills in prospective and practising EFL teachers include the lack of formal training, limited access to AI resources, and slow pace of educational reforms, which do not prioritise the AI literacy of educators (Edmett et al., 2023). In addition, L2 teachers’ readiness to sustainably adopt AI technologies is affected by their concerns that AI could replace human teachers (Pokrivcakova, 2023), reduce their employment opportunities (Sütçü & Sütçü, 2024), diminish the quality of teacher–student interaction (Molina-Garcia et al., 2024), and hinder students’ creativity and critical thinking (Söğüt, 2024).

Although the available empirical literature illustrates the mixed feelings of L2 teachers towards the embedding of AI-powered tools into English language instruction and acknowledges their demand for consistent formal AI training, research on the perspectives of EFL pre-service teachers is scarce. With the exception of Pokrivcakova (2023) and Yetkin and Özer-Altınkaya (2024)—who concentrate on exploring the psychological factors affecting the intention of pre-service English language teachers within two different educational contexts (Slovakia and Türkiye, respectively) to use AI—to date, we are not aware of any previous research tackling the same issue. Our study, therefore, attempts to address this prominent gap by examining the interplay between the current level of AI knowledge, competencies, and skills of 56 Bulgarian pre-service English language teachers and their prospective behavioural intention to use AI technologies in their future professional endeavours.

2. Research Questions and Hypotheses

The present study poses the following research questions (RQs):

RQ1: What is the self-assessed level of AI knowledge, competencies, and skills of the pre-service EFL teachers?

RQ2: To what extent does perceived self-efficacy regarding AI use predict pre-service EFL teachers’ behavioural intention to implement AI in their future L2 classrooms?

RQ3: What is the relationship between perceived training needs and pre-service EFL teachers’ self-efficacy and behavioural intention to implement AI?

To answer these three questions, we employed a research model that rests upon the Theory of Planned Behaviour (Ajzen, 2011) and the Technology Acceptance Model (Davis, 1989), two frameworks consistently used in prior research aimed at understanding pre-service teachers’ acceptance of AI technologies (Sanusi et al., 2024; Zhang et al., 2023). These frameworks imply that technology adoption is affected not only by knowledge, perceived competence, and behavioural intentions, but also by motivational factors such as attitudes and perceptions of control over the implementation process. Drawing upon these perspectives, we examine four key variables associated with pre-service teachers’ planned behaviour to promote AI: (1) actual learning of AI (AL) (refers to AI knowledge and competencies that enable pre-service teachers to deploy AI tools in their teaching); (2) perceived self-efficacy (PSE-SE) (encompasses pre-service teachers’ beliefs in their capacity to utilise AI technologies successfully); (3) perceived training needs (PSE-TN) (refers to the perceived gaps between current competencies and the knowledge, skills, or support required to effectively use AI in language teaching); and (4) behavioural intention (BI) (the willingness to adopt AI technologies in future L2 instruction).

Given the fact that prior empirical research indicates that pre-service teachers’ actual learning of AI (AL) predicts their intention to use AI tools in lesson preparation and teaching (Sanusi et al., 2024; Zhang et al., 2023), it can be assumed that pre-service EFL teachers’ knowledge of AI and competencies for embedding AI apps in the educational process will positively affect their intention to apply AI technologies in future L2 settings. Therefore, we hypothesise the following:

H1. 

Pre-service EFL teachers’ actual learning of AI (AL) will positively predict their behavioural intention (BI) to deliver AI-supported lessons.

The crucial role of actual AI learning in shaping future teachers’ confidence in incorporating AI applications in their teaching practice has been addressed in several studies. For example, Guan et al. (2025) provide empirical evidence that pre-service teachers’ perceived self-efficacy increases as a result of their more profound understanding of AI and improved technological and pedagogical competencies. Furthermore, Yang et al. (2024) support the assertion that the AI knowledge and experience pre-service teachers gain as part of their formal training considerably raises their self-efficacy levels, which, in turn, serves as a motivational factor affecting their willingness to adopt AI-based teaching methods. In light of these, it is plausible to expect that the more pre-service EFL teachers actually learn about AI (AL), the higher their confidence about using AI in their language teaching (PSE-SE) will be. Accordingly, we propose the following:

H2. 

Pre-service EFL teachers’ actual learning of AI (AL) will positively affect their perceived self-efficacy (PSE-SE) in using AI for language teaching.

Prior research shows that higher levels of digital or AI-specific knowledge are associated with stronger intentions to integrate technology into teaching (Sanusi et al., 2024; Zhang et al., 2023). When pre-service teachers are confident in their AI skills, they are more likely to visualise AI as a viable component of their future pedagogy. Moreover, contingent upon data from studies on self-efficacy (PSE-SE) as a factor influencing pre-service teachers’ perception of their own pedagogical abilities (Kula, 2022; Aybek & Aslan, 2019), it is logical to presume that a higher level of self-efficacy will be associated with stronger behavioural intentions to create AI-supported English language lessons. Hence, the following hypothesis is posed:

H3. 

Perceived self-efficacy (PSE-SE) in AI implementation will have a positive effect on pre-service EFL teachers’ behavioural intention (BI) to use AI technologies in their future L2 classrooms.

The existing literature conceptualises behavioural intention as a variable that is determined by factors such as AI knowledge, perceived usefulness, and self-efficacy (Li et al., 2024; Sanusi et al., 2024; Acquah et al., 2024; Zhang et al., 2023). Contrary to this, some theoretical frameworks, such as the Theory of Planned Behaviour (Ajzen, 2011) and self-regulated learning models (e.g., Zimmerman, 2002; Pintrich, 2000), envisage behavioural intention as a driving force that motivates individuals to improve their knowledge or expertise further. In the context of initial teacher education, this means that if a pre-service teacher plans to use AI in their teaching, their willingness to utilise AI applications in their pedagogical practice might motivate them to learn more about AI and develop their pedagogical expertise actively. To the best of our knowledge, this proposal has not found empirical validation yet. Addressing this gap, we hypothesise that behavioural intention (BI) itself may act as a predictor of actual AI learning (AL) among pre-service EFL teachers:

H4. 

Pre-service EFL teachers’ behavioural intention (BI) to use AI technologies will positively predict their actual learning of AI (AL).

Perceived training needs (PSE-TN) represent an important complementary factor that reflects pre-service EFL teachers’ awareness of the deficiencies in their knowledge and skills related to AI integration. A high perception of training needs does not necessarily indicate low confidence, as it could be a sign of an opportunity-focused mindset and readiness to strengthen one’s competencies. Recognising one’s own training needs is often linked to motivation for professional growth and the belief that improvement is achievable (Bandura, 2010; Calaguas & Consunji, 2022).

Although there is a lack of direct studies examining the link between pre-service teachers’ perceived training needs and their self-efficacy or behavioural intention, related evidence suggests that the perceived competence and usefulness of AI tools influence engagement in training. For example, pre-service teachers may attend AI-related courses because they already rate their technological and pedagogical capacities to use AI applications highly or because they consider AI tools useful (Runge et al., 2025). However, high perceived training needs indicate a skills gap that may undermine future teachers’ self-assurance in their abilities to engage with AI technologies competently. This is consistent with social cognitive theory, which posits that self-efficacy is significantly affected by perceived capability (Bandura, 2010). Accordingly, we propose that higher perceived training needs (PSE-TN) will be a result of lower levels of perceived self-efficacy (PSE-SE) among the pre-service teachers regarding AI use:

H5. 

Perceived training needs (PSE-TN) will negatively predict pre-service EFL teachers’ perceived self-efficacy (PSE-SE) regarding AI use.

3. Materials and Methods

3.1. Study Participants

The study participants included 56 bachelor’s and master’s students from a state Bulgarian university who were preparing to become future teachers of English as an L2 at the primary or lower-secondary school level. The majority of them—75% (N = 42)—were undergraduate students, while 25% (N = 14) were enrolled in a postgraduate teacher training programme.

The study sample comprised 52 (92.86%) female and 4 (7.14%) male pre-service teachers with an age range from 20 to 49 years (mean age of 24.91; SD = 6.667). Regarding the male participants, one was in a bachelor’s programme, and three were enrolled in master’s programmes. The distribution of the participants in terms of their year of study was as follows: 32% (N = 18) were in their first year of study, 25% (N = 14) were in their second year of study, 20% (N = 11) were in their third year, and 23% (N = 13) were in their fourth year. The ratio between the undergraduate and postgraduate students according to the year of study showed that 55.6% of the first-year participants were enrolled in a bachelor’s programme and 44.4% were in a master’s programme, while 57.1% of the respondents were bachelor’s course students and 42.9% were master’s course students. In contrast, all of the third- and fourth-year students (100%) were enrolled in the bachelor’s programme.

3.2. Instrument

The main research instrument employed in the study was an online Google Forms questionnaire written in Bulgarian by the researcher based on a review of the data collection tools utilised in past studies examining the different factors that affect pre-service teachers’ attitudes towards and behavioural intentions regarding AI-mediated instruction. The questionnaire format was in compliance with the Knowledge, Attitude and Practices (KAP) Survey model, which was adapted to the context of pre-service teacher education by Pokrivcakova (2023). It allowed for the collection of quantitative data regarding the current state of knowledge, beliefs, and intended practices of prospective EFL teachers regarding AI technologies in foreign language education.

The questionnaire was piloted in March 2023 with 60 pre-service EFL teachers and refined in light of the feedback obtained and tests of its validity and reliability. Out of the initial 18 questions, 14 were rephrased for clarity, while 4 were deleted, thereby leading to a final version of the questionnaire containing 14 questions.

Although the Google Forms survey was not formally divided into sections, the items in it can be grouped into three thematic sections: (1) demographic information and participants’ level of English proficiency (questions 1–6), which mainly consisted of single items with fixed response options; (2) participants’ experience with AI (questions 7–11), also comprising primarily single items; (3) items addressing the three variables under scrutiny, i.e., actual learning of AI (AL; question 12 containing 8 Likert-scale items (AL01–AL08), e.g., “I am familiar with the characteristics and functionalities of a variety of AI tools that can be used in the L2 classroom (AL01)”); perceived self-efficacy (PSE-SE; question 13 with 2 Likert-scale items (PSE01–PSE02), e.g., “I am sure I understand the principles underlying AI technology (PSE01)”); perceived training needs (PSE-TN; question 13 with 2 items (PSE03–PSE04), e.g., “I believe I need additional training to master AI-supported L2 teaching and learning (PSE03)”); and behavioural intention (BI; question 14 with 4 Likert-scale items (BI01–BI04), e.g., “I plan to further develop my AI literacy in the longterm”). The items in the last three questions (questions 12, 13 and 14) were rated on a 5-point Likert scale (1 = “Strongly disagree” to 5 = “Strongly agree”).

The four constructs and their respective items, in both Bulgarian and their English translations, are fully provided in the Appendix A for reference.

The original questionnaire was created in Bulgarian and later translated into English to support dissemination in an international academic setting. The translation was carried out by the author, who has expertise in English language teaching, with assistance from a colleague fluent in both Bulgarian and English. This team effort aimed to ensure the semantic accuracy and conceptual equivalence of the translated items compared to the original. Although a formal back-translation process was not used, careful attention was paid to maintaining the meaning and clarity of each item in the English version.

The psychometric properties of the final survey variables were analysed to evaluate their validity and reliability. First, we checked the sample adequacy through the Kaiser–Meyer–Olkin (KMO) measure and then the sample sphericity through Bartlett’s test. Then, we performed exploratory factor analysis and assessed the internal consistency of each variable by calculating the Cronbach’s α for multi-item constructs (e.g., AL and BI) and the Spearman–Brown formula for two-item constructs (e.g., PSE-SE and PSE-TN).

3.3. Data Collection Procedures and Data Analysis

Data collection started in the middle of May 2025 when the researcher sent the link to the Google Forms questionnaire via email to a total of 81 Bulgarian pre-service teachers of English who were asked to fill in the questionnaire within two weeks. The students were informed about the questionnaire’s purpose and about the fact that their participation in it was voluntary. In total, 56 of the students approached (69.14%) completed the questionnaire. Participation was voluntary, and although the questionnaire collected limited demographic information such as the participants’ ages and genders, the responses remained anonymous because no other personally identifiable data such as names and e-mail addresses were collected. Informed consent was obtained from all subjects involved in the study.

The collected data were analysed, starting at the end of May 2025. It was performed by the researcher using SPSS26 software and the summary statistics extracted from Google Forms. Descriptive statistical analysis was used to gain an overview of the participants’ demographic characteristics, to assess the degree of dispersion of the data sets in each variable, and to check the reliability and validity of the data obtained. Inferential statistics were implemented in order to assess the relationships between the analysed variables. Bivariate Pearson correlation analyses were applied to assess the strength and direction of relationships between the perceived self-efficacy (PSE-SE), behavioural intention (BI), actual learning (AL), and perceived training needs (PSE-TN). These correlations provided the basis for testing the proposed hypotheses regarding the interdependence of the four variables.

4. Results

4.1. Validity and Reliability of the Instrument

The suitability and stability of each of the four variables for factor analysis were assessed with the help of the Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests (

Table 1. Assessment of the survey variables’ suitability for factor analysis.

Variable	No. of Items	KMO Measure of Sampling Adequacy	Bartlett’s Test
			χ2	df	p
AL	8	0.737	145.761	28	0.000
PSE-SE	2	0.500	24.835	1	0.000
PSE-TN	2	0.500	25.719	1	0.000
BI	4	0.807	150.603	6	0.000

).

The KMO measure signified adequate sampling adequacy for most variables (Hair et al., 2019), with a range from 0.500 (for PSE-SE and PSE-TN) to 0.737 (for AI) and 0.807 (for BI). Bartlett’s test of sphericity revealed strong correlations between the constructs (p < 0.001), which provided a strong basis for the subsequent factor analysis (Field, 2018).

The exploratory factor analysis results are summarised in

Table 2. Psychometric properties of the survey variables and items.

Variable	Item Code	Factor Loading	Corrected Item-Total Correlation	Cronbach’s Alpha If Item Deleted	Scale Mean If Item Deleted	Scale Variance If Item Deleted
AL	AL01	0.830	0.475	0.808	23.27	17.945
	AL02	0.905	0.337	0.823	23.09	19.610
	AL03	0.642	0.470	0.808	22.96	18.835
	AL04	0.629	0.532	0.800	23.43	17.486
	AL05	0.729	0.607	0.789	23.50	16.873
	AL06	0.781	0.655	0.781	23.61	16.097
	AL07	0.725	0.602	0.789	23.64	16.670
	AL08	0.785	0.613	0.788	23.38	16.748
PSE-SE	PSE01	0.897	0.609	—	3.39	0.752
	PSE02	0.897	0.609	—	3.48	0.763
PSE-TN	PSE03	0.899	0.618	—	3.89	0.788
	PSE04	0.899	0.618	—	3.71	1.008
BI	BI01	0.831	0.714	0.905	11.45	6.543
	BI02	0.921	0.848	0.858	11.82	5.458
	BI03	0.918	0.845	0.859	11.75	5.900
	BI04	0.865	0.761	0.889	11.70	6.470

. The factor loadings were above the acceptable minimum of 0.50 (Hair et al., 2019), while Cronbach’s alpha coefficients of the variables AL and BI exceeded the recommended threshold of 0.7 (DeVellis, 2003). The corrected item-total correlation and item deletion analyses supported the homogeneity of the survey items, except for two of the AL variable’s items—AL02 and AL03 (corrected item-total correlation value of 0.377 and 0.470, respectively). The latter had a moderate but acceptable positive correlation (between 0.30 and 0.50), which contributed to the overall quality of the AL construct (Kline, 2015).

The two-item variables—PSE-SE and PSE-TN—showed excellent reliability, with Spearman–Brown coefficients of 0.757 (PSE-SE) and 0.764 (PSE-TN).

4.2. Descriptive Statistics

Descriptive statistics were used to measure the central tendency and variability of the collected data (i.e., mean (M), standard deviation (SD), variance, Kurtosis, and Skewness) along with a calculation of the reliability coefficient (Cronbach’s alpha) to check the internal consistency of the variables (

Table 3. Descriptive statistics of the study variables.

Variable	Mean	Std. Deviation	Skewness		Kurtosis		Cronbach’s Alpha (α)
	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
AL	26.964	4.72487	0.093	0.319	0.061	0.628	0.819
PSE-SE	6.8750	1.56161	−0.171	0.319	−0.398	0.628	0.757
PSE-TN	7.6071	1.70218	−0.362	0.319	−0.644	0.628	0.760
BI	15.5714	3.24097	−0.360	0.319	−0.692	0.628	0.906

).

The values of the standard deviation (SD) range from 1.562 to 4.725, which reflects the normal distribution of the data. This claim is also supported by the Skewness values for all of the variables: they fall within an acceptable range between −1 and +1 (Hair et al., 2022, p. 66) and their distribution is approximately symmetrical and close to normal. The Kurtosis value for the first variable (AL) is positive, while the Kurtosis values of the remaining three variables (PSE-SE, PSE-TN, and BI) are negative. Regardless, all of these values fall within the acceptable range of −2 to +2, which proves the data have a normal univariate distribution. The reliability coefficient (Cronbach’s α) for all variables exceeds the minimum threshold of 0.70, which points to a high internal consistency. Two of the variables—AL and BI—demonstrate high reliability (α = 0.819 and α = 0.906, respectively), whereas both PSE-SE and PSE-TN display acceptable reliability (α = 0.757 and α = 0.760).

4.3. Self-Assessed Level of Actual Learning of AI (AL)

The first research question (RQ1) focused on determining the self-assessed level of AI knowledge, competencies, and skills of the pre-service EFL teachers participating in the current study. It was assessed based on their answers to Question 12 (items AL01–AL08) in the Google Forms questionnaire (Figure 1).

The participants’ responses to the items on the AL scale indicated a generally moderate to positive self-assessment of their knowledge and capacity to integrate AI tools in the teaching and learning of foreign languages. More than half of the future EFL teachers (53.57%) affirmed their familiarity with the characteristics of a rich spectrum of AI applications (AL01), while only 5.36% felt their knowledge of AI technologies was inadequate. These results aligned with the array of answers given to the second item (AL02), which focused on the capacity of the pre-service teachers to use AI to develop learners’ English language skills. Here, 58.93% of the respondents agreed and 5.36% strongly agreed with that statement, whereas 8.93% disagreed. These results signified a relatively high level of confidence of the pre-service EFL teachers in applying AI tools in instructional activities targeted at the development of learners’ linguistic capacities.

The highest level of agreement was observed for lesson planning with AI applications (AL03), with 62.50% expressing confidence in this area. However, the self-reported competence levels were lower for more complex pedagogical applications of AI (AL04–AL08). For instance, only 39.29% felt sufficiently trained to use AI to respond to learners’ individual needs (AL04), whereas a substantial proportion—33.93%—remained neutral and 21.43% expressed disagreement. These results reflect the study participants’ uncertainty about applying AI in more personalised teaching contexts. Similarly, 30.36% expressed confidence in using AI for corrective feedback (AL05), compared to 39.29% who were undecided and 23.22% who disagreed.

The responses to the items related to assessment (AL06) and monitoring L2 speakers’ progress (AL07) showed that approximately 60% were neutral or disagreed, whereas less than one-third of the pre-service EFL teachers declared overall satisfaction with their level of proficiency in these areas. This pattern signalled a marked hesitancy among the future L2 teachers to implement AI tools in evaluative aspects of language teaching. The participants’ self-assessment about their capacities to critically evaluate AI tools (AL08) was more evenly distributed: 46.43% agreed or strongly agreed, 30.36% remained neutral, and 23.21% disagreed. This distribution highlighted a limited capacity for reflective and critical engagement with AI technologies among the pre-service EFL teachers.

To support the answer of RQ1, we investigated the dependencies between pre-service EFL teachers’ self-assessed level of actual learning (AL), their perceived self-efficacy (PSE-SE) and behavioural intention (BI) to implement AI technologies in their prospective professional practice. Specifically, we tested the following hypotheses: H1 (AL positivey predicts BI), H2 (AL positively predicts PSE-SE) and H4 (BI positively predicts AL).

The regression analysis provided postitive support for all three hypotheses (

Table 4. Regression analysis results testing for H1, H2, and H4.

Hypothesis	Predictor → Outcome	R	R2	F	p-Value	β (Beta)	Sig. (p)	Result
H1	AL → BI (positive)	0.547	0.299	23.058	0.000	0.547	0.000	Supported
H2	AL → PSE-SE (positive)	0.594	0.352	29.374	0.000	0.594	0.000	Supported
H4	BI → AL (positive)	0.574	0.299	23.058	0.000	0.574	0.000	Supported

).

The identical R² and F-values for H1 and H4 point to a potential bidirectional relationship between AL and BI, which is explored further in the Section 5.

4.4. Perceived Self-Efficacy and Its Effect on Pre-Service Teachers’ Behavioural Intention to Use AI

The second research question (RQ2) addressed the interplay between two of the variables: PSE-SE and BI. A simple linear regression analysis, where BI was the dependent variable and PSE-SE was the predictor, was performed to give an answer to RQ2 and test H3 (

Table 5. Linear regression coefficients predicting behavioural intention to use AI.

Predictor	B	SE	β	t	p
(Constant)	10.292	1.849	—	5.566	0.000
PSE-SE	0.768	0.262	0.370	2.927	0.005

Note. Dependent variable is behavioural intention to use AI (BI).

). It revealed that PSE-SE significantly predicted BI to integrate AI (F(1.54) = 8.57, p = 0.005). The regression model explained 13.7% of the variance in BI (R² = 0.137), indicating a modest but statistically meaningful relationship. The unstandardised coefficient for PSE-SE was B = 0.77 (SE = 0.26), and the standardised coefficient was β = 0.37, p = 0.005 (Table 5). These results suggest that higher levels of self-efficacy are associated with pre-service EFL teachers’ stronger intentions to use AI in their prospective careers and thus confirm H3.

4.5. Perceived Training Needs and Their Impact on the Pre-Service Teachers’ Behavioural Intention to Use AI

The third research question focused on discovering how the pre-service EFL teachers’ perceived training needs (PSE-TN) related to their perceived self-efficacy (PSE-SE) and behavioural intention to implement AI (BI) in L2 classrooms. In relation to this question, we tested H5, hypothesising that PSE-TN negatively influences PSE-SE.

The Pearson correlation coefficients revealed no significant relationship between PSE-SE and PSE-TN (r = −0.005, p = 0.970), which suggested that the participants’ confidence in using AI was not associated with their perceived need for further training. This result did not support H5.

However, a moderate yet statistically significant positive correlation was found between PSE-TN and BI (r = 0.503, p < 0.001). This result provided evidence that the study respondents who expressed a strong intention to use AI also reported higher training needs.

A multiple regression analysis (PSE-TN as the dependent variable; PSE-SE and BI as predictors) confirmed the following findings: F(2.53) = 11.10, p < 0.001, R² = 0.295, Adjusted R² = 0.269 (

Table 6. Multiple regression analysis of respondents’ perceived training needs (PSE-TN).

Predictor	B	SE	β	t	p
BI	0.307	0.070	0.585	4.36	<0.001
PSE-SE	−0.241	0.136	−0.222	−1.78	0.080

Note. R² = 0.295, adjusted R² = 0.269, F(2.53) = 11.10, p < 0.001.

).

Behavioural intention (BI) emerged as a significant positive predictor of the study sample’s perceived training needs (PSE-TN) (B = 0.307, β = 0.585, p < 0.001), which was in compliance with the correlation data of these two variables. In contrast, PSE-SE showed no significant predictive effect on PSE-TN (B = −0.241, β = −0.222, p = 0.080). This result signified that the pre-service EFL teachers’ confidence in engaging effectively with AI did not contribute significantly to variations in their perceived training needs.

5. Discussion

Analysing the data for the first research question (R1) provided valuable insights into the actual level of knowledge, competencies, and skills of the pre-service EFL teachers (AL) based on their self-assessment. The majority of the study participants reported familiarity with a range of AI applications and expressed confidence in using these tools for lesson planning and supporting learners’ English language skill development. These findings comply with the results of the British Council Global Survey on how AI is used by English language teachers (Edmett et al., 2023), which acknowledged that teachers relied on AI technologies for tasks such as material creation, improvement of students’ L2 mastery, and lesson planning. Although said study deals with the experience of practising English language school teachers with AI technologies, it supports the assertion that both current and trainee EFL teachers tend to rely on AI tools for content creation, language practice, and lesson design, while showing more hesitation towards their integration in English language assessment, L2 error correction, and personalised instruction. This hesitation is understandable since lesson planning and content design are much more straightforward tasks, and pre-service EFL teachers can also rely on the support of readily available AI tools, while AI-supported assessment requires higher levels of pedagogical and technological mastery linked to fairness and validity, areas in which future teachers feel unprepared (Cheah et al., 2025; Hsu et al., 2024). A plausible explanation for this cautiousness among the study participants may also lie in the fact that they are still developing their pedagogical competencies in utilising emerging digital technologies, adding these to their teaching repertoire. This corresponds with previous studies suggesting that pre-service EFL teachers feel unprepared to make pedagogically sound decisions about technology use overall or in complex areas such as assessment, personalised learning, and error correction (Pokrivcakova, 2023; Chung & Jeong, 2024; Yim & Wegerif, 2024; Tondeur et al., 2012; Chai et al., 2013). Though not explicitly stated by the respondents, we suggest that they would benefit from more structured and consistent formal training opportunities. These opportunities would allow them to enrich the scope of their pedagogical strategies and interactions with AI, thus enhancing their AI literacy.

The statistical analysis of the relationship between PSE and BI, in answer to the second research question (RQ2), disclosed that the perceived self-efficacy (PSE-SE) is a powerful antecedent of the future EFL educators’ behavioural intention (BI) to use AI-powered English language teaching and learning tools. This corroborates with previous studies, which have shown that the perceived self-efficacy has an effect on the behavioural intention of candidate teachers to function competently in AI-rich settings (Suardewa et al., 2024; Zhang et al., 2023; Sanusi et al., 2024). Furthermore, our results support empirical evidence from studies investigating pre-service teachers’ acceptance of AI technologies based on the impact of their prior engagement with AI applications (Alejandro et al., 2024; Pei et al., 2025). Not surprisingly, practical training that allows future teachers to obtain hands-on experience in real-world educational settings is among the primary factors impacting their confidence and positive attitude towards adopting these technologies in future educational contexts. Drawing upon this, we can conclude that the perceived usefulness of AI tools and pre-service EFL teachers’ beliefs in their skills to apply AI applications effectively in L2 instruction have a pivotal impact on their behavioural intentions to design and deliver AI-supported L2 lessons. Therefore, we recommend the inclusion of practice-oriented AI curricula in university teacher training programmes in order to raise pre-service teachers’ awareness of the pedagogical potential of AI applications. Therefore, we recommend the inclusion of practice-oriented AI curricula in university teacher training programmes that combine hands-on experience with AI tools, simulations of AI-supported classroom scenarios, and specific pedagogical models for AI use. Such an approach is likely to raise pre-service teachers’ awareness of the pedagogical potential of AI applications and translate this into stronger behavioural intentions. This recommendation, however, needs to be further investigated, as not all studies on pre-service teachers’ readiness to adopt AI confirm a direct link between the perceived self-efficacy and behavioural intention (e.g., Yao & Wang, 2024). This inconsistency might be attributed to differences in the conceptualisation of self-efficacy (e.g., general teaching vs. AI technology-specific), the unique characteristics of the teaching population (e.g., special education teachers), or contextual factors such as the availability of AI training opportunities. Addressing each of these aspects experimentally could account for more nuanced and population-specific initial teacher training approaches, which could contribute to a better understanding of the mechanisms that foster pre-service EFL teachers’ behavioural intentions regarding AI utilisation in L2 instruction.

With regard to the third research question (RQ3), which examined the interrelation between pre-service EFL teachers’ PSE-TN, PSE-SE, and BI to integrate AI in their teaching, our study discovered that there is no association between two of the variables: PSE-SE and PSE-TN. No prior research, to our knowledge, has examined the interplay between these two constructs. The results reject our last hypothesis (H5), as they justify the idea that student EFL teachers’ confidence in their skills does not necessarily imply recognition of the need for additional training. In essence, the majority of the pre-service EFL teachers who feel self-assured in their AI competencies and skills are likely to underestimate both the potential gaps in their knowledge or capabilities to engage pedagogically with AI applications, as well as the areas in which they require further development. While the increased sense of digital AI-related competence in the study subjects could be explained through Bandura’s social cognitive theory (Bandura, 2012), which states that self-efficacy shapes human behaviour and boosts individual self-conviction in performing specific tasks, the results also suggest that strong beliefs in one’s capabilities may create a false sense of readiness to adopt AI technologies. In consideration of this, we could interpret the pre-service EFL teachers’ perceived training needs through the lens of another construct that has found extensive treatment in AI-related research: subjective norms. The reason underlying this choice is associated with the fact that subjective norms reflect the extent to which individuals are inclined to act when pressed by social expectations exerted by peers, mentors, and professional communities (Bock et al., 2005). In the context of initial L2 teacher training, it could be expected that perceived social pressure to develop AI-related teaching competencies could lead to increased motivation to seek training opportunities. This could subsequently improve pre-service EFL teachers’ self-efficacy to incorporate AI tools in their pedagogical environments. These claims are supported by existing studies showing that pre-service teachers’ subjective norms have a positive impact on their perceived self-efficacy for AI integration in education (Sanusi et al., 2024; Hoya et al., 2024). These study’s findings, however, support the presence of a relationship between the two constructs, which does not align with the findings of our study. Still, building upon this evidence, we could expect such a dependency and therefore have grounds for formulating H5.

A possible explanation for the non-significant relationship between PSE-TN and PSE-SE lies in the fact that pre-service EFL teachers may not perceive AI competencies as a formal requirement clearly imposed by their academic programmes or future employers. Currently, there are no specific national-level requirements in Bulgaria or strict professional guidelines that define the AI knowledge, competencies, and skills essential for future and practising school teachers. The only official resource available is the “Guidelines for the Use of Artificial Intelligence in Education”, which gives a general overview of the essence of AI and offers suggestions on how AI technologies could be used by school teachers, directors, students, and parents. This resource does not provide practical guidance for teacher training or AI competency development (European Education and Culture Executive Agency, 2024). The lack of explicit AI professional standards and institutional expectations most probably diminishes pre-service EFL teachers’ recognition of their own training needs. Furthermore, our study’s participants’ perceived training needs may have been viewed as a reflection of individual competence gaps that are a result of the missing AI competency requirements at the national and institutional levels. This disconnection suggests that pre-service teachers’ recognition of their training needs might be more strongly influenced by external expectations rather than by their individual perceptions of capability. Thus, it is important to consider subjective norms as antecedents of perceived training needs and as a driver towards further AI training (Bock et al., 2005).

Our study established that the correlation between the participants’ perceived training needs (PSE-TN) and behavioural intention (BI) was moderate but statistically significant. The implication behind this result is that the pre-service EFL teachers, who reported a stronger intention to embrace AI technologies for professional purposes, expressed a greater requisite for pedagogically related AI capacity building. On the one hand, this advances the notion that, unlike PSE-SE, BI provides a more accurate reflection of the study participants’ self-assessment of their abilities to engage meaningfully and critically with AI applications in the L2 classroom. On the other hand, it clearly demonstrates that pre-service teachers who consider AI technologies to be integral components of their teaching career are more open and ready to enrich their AI digital literacy and pedagogical strategies. These claims corroborate the results obtained from existing research, which show that pre-service teachers have a generally positive outlook towards AI integration in education, but admit that they need additional training and support. For instance, Guan et al. (2025) identified that although future teachers have a favourable disposition towards AI, there are gaps in their digital literacy and knowledge of the ethical and pedagogical aspects of AI utilisation in the teaching and learning process. In addition, Uysal and Yüksel (2024) reported the mixed feelings of future L2 teachers towards the incorporation of AI in lesson planning. While the candidate teachers approached AI-supported lesson planning with enthusiasm, they soon admitted their lack of pedagogical skills in how to implement AI technologies in planning lessons that address different learning needs and foster personalised instruction patterns. Similar concerns were raised in empirical studies that stress the need for introducing teacher training programmes aimed at equipping pre-service EFL teachers with practical skills for AI-assisted L2 teaching (Özer-Altınkaya & Yetkin, 2025; Taşçı & Tunaz, 2024). The latter illustrates the growing consensus that university-level teacher education curricula should be revised to include training modules and courses that enhance the practical experience of prospective EFL teachers with AI tools in L2 settings, along with their critical thinking, pedagogical potential, and ethical knowledge regarding AI use in contemporary education. Recent research that reinforces the idea of taking action in this direction provides firm support for the positive effect of AI-focused training programmes on the technological and pedagogical skills of pre-service teachers (Younis, 2024).

The results of testing the five hypotheses demonstrated that there was a positive association between AL and BI (H1), and between AL and PSE-SE (H2), which supported the first two of our formulated hypotheses (H1 and H2). This was not unexpected, as previous research examining the congruity between the variables in these two sets proved that pre-service teachers’ knowledge and skills in customising AI educational tools in L2 instruction were a strong predictor of their behavioural intention and self-efficacy in making pedagogically sound use of AI technologies (Acquah et al., 2024; Sanusi et al., 2024; Bautista et al., 2024; Gao et al., 2025). These findings also comply with the underpinnings of the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003), which proposes that one of the factors effecting an individual’s predisposition to deploy technology in a specific institutional context is their past experience with that technology. Considering this, we believe that pre-service EFL teachers’ actual learning of AI acts as a trigger of their behavioural intention to employ AI tools in prospective educational contexts.

Empirical support was also provided for hypothesis 3 (H3), which tested the dependency of the pre-service EFL teachers’ PSE-SE on their BI to utilise AI in L2 classrooms. The association between these two variables was moderate but statistically significant. This implies that pre-service teachers who feel confident in their abilities to use AI tools are much more ready to interact with these digital tools in their future careers. The idea that PSE-SE functions as a precondition for pre-service L2 teachers’ readiness to implement AI applications in English language instruction has also been expressed in past research works. Suardewa et al. (2024) reported that pre-service EFL teachers’ self-efficacy emerges as a key determinant of their willingness to integrate AI tools in their writing skills classes. Likewise, Guan et al. (2025) confirmed that self-efficacy is a critical factor affecting pre-service teachers’ readiness to use AI technologies in their prospective careers, since it shapes their perceptions, capabilities, and attitudes towards AI adoption. Thus, one possible way to increase prospective EFL teachers’ self-assurance in their ability to implement AI technologies in L2 education is to increase their skills in this area while preparing them for their future jobs. This can only be achieved if universities introduce teacher training programmes that provide opportunities for pre-service EFL teachers to interact pedagogically with AI tools. This would enable future L2 teachers to not only gain solid experience with AI technologies, but also develop a sense of competence in their own abilities to plan and deliver AI-enhanced language lessons.

The validation of the fourth research hypothesis (H4), which confirmed that BI was a strong antecedent of AL, suggests that the pre-service EFL teachers’ intention to integrate AI tools in their future professional settings influences their actual learning of AI. This result suggests that candidate teachers’ aptitude for certain professional behaviours has a prominent role in acquiring AI-related pedagogical knowledge and skills. It is necessary to state that the direction of influence—BI as a predictor of AL—is not consistently explored in existing empirical research, which tends to tackle the relationship with the opposite direction of influence (as stated in our H1). However, previous studies on in-service EFL teachers’ behavioural intention to use AI in schools suggest that AI technological–pedagogical knowledge has no direct effect on the motivation and willingness of practicing teachers to engage with AI tools in their L2 classrooms (An et al., 2023; Yao & Wang, 2024). Although this result contradicts our own findings, it brings to light the importance of investigating the impact of other factors that may have a more prominent predictive effect on the BI of future L2 teachers—attitudes, perceived usefulness, social influence, and perceived ease of use (Guan et al., 2025; Yang et al., 2024).

The observed bidirectional relationship between AI and BL can be theoretically explained by the concept of reciprocal determinism (Bandura, 1978), which implies that elements such as human cognition, behaviour, and environmental factors constantly shape and reshape each other. For initial teacher training, this suggests that pre-service EFL teachers’ initial intentions to implement AI for pedagogical purposes may motivate them to actively engage with AI. Consequently, the AI-related pedagogical competencies and skills they gain may reinforce their inclination to apply AI technology in future L2 classrooms. This reciprocal feedback loop highlights the mutual reinforcement of motivation and skill development. Moreover, the professional development literature supports the idea that teachers’ attitudes and behavioural intentions, and actual learning are intertwined, dynamic, and evolving (Coldwell, 2017; Schindler et al., 2021). This is why understanding AI tools may increase users’ self-efficacy, which in turn may solidify their intentions to adopt AI technologies, and vice versa.

6. Limitations

Although the current study provides insights into the interplay between the actual learning, perceived self-efficacy, perceived training needs, and behavioural intention of pre-service EFL teachers in using AI applications in future teaching contexts, certain limitations exist. First, the research sample is relatively small (N = 56) and geographically limited, since undergraduate and postgraduate EFL student teachers were recruited from only one state university in Bulgaria. This restricts the broader applicability of the results. The small sample size reflects the specific institutional context of the university where the study was performed: a limited number of EFL teacher training programmes with a low number of available places. The university offers one bachelor-level program for primary school English language teachers enrolling approximately 20 students per year across a four-year cycle, and two master’s programs (one for primary and one for lower-secondary school English language teachers) with a total enrollment of no more than 10 students across a 1.5-year cycle.

Furthermore, the findings should be viewed within the broader context of Bulgaria’s educational landscape. While national policies such as the “Digital Bulgaria 2025 National Programme” (Republic of Bulgaria, Ministry of Transport and Communications, 2019) and action plans such as the “Concept for the Development of AI in Bulgaria until 2030” (Republic of Bulgaria, Ministry of Transport, Information Technology and Communications, 2020) demonstrate a strong political commitment for AI-driven modernisation of education (including teacher training and professional development), the implementation of AI in the higher education system remains fragmented. Although digital training is an integral component of initial teacher education, the focus on AI is still limited (Simeonov et al., 2024; Beloev et al., 2024). This, along with the absence of explicitly defined AI teacher competences and development levels, has likely influenced the perceptions and behaviours of the study participants. Therefore, the current results should not be generalised beyond this context.

Follow-up research could employ a larger group of participants across higher education institutions within the country in order to improve the external validity of the data collected and their representativeness. Second, our study relied on the pre-service teachers’ perceptions of their own knowledge, skills, and competencies in AI implementation in L2 classrooms, as well as with their training needs, which may introduce bias or inaccuracies due to the self-assessment required. Third, the cross-sectional design of the study provides a snapshot of the current capabilities and self-assessment insights of the pre-service EFL teachers regarding their mastery of AI and its pedagogical implementation in education. However, this also prevents us from establishing causal relationships or temporal sequences of effect between the two variables: BI and AL. Intervention-based or longitudinal studies could shed light on the bidirectionality of the observed effects and their possible development over time. Longitudinal research could also be used to gain a more accurate representation of how pre-service teachers’ perceptions and behavioural intention regarding AI implementation evolve during their university training. Obtaining a clear picture of the shift in pre-service EFL teachers’ perceptions and readiness to integrate AI technologies in foreign language teaching and learning will help improve teacher education programmes and curricula in line with the technological and pedagogical requirements for future teachers.

7. Conclusions

This study highlights the relationship between four constructs effecting pre-service EFL teachers’ readiness to engage with AI technologies in their future careers: the actual learning of AI (AL), perceived self-efficacy (PSE-SE), perceived training needs (PSE-TN), and behavioural intention (BI). The results provide empirical evidence that the actual learning of AI and perceived self-efficacy are strong antecedents of the behavioural intention of future L2 teachers to use AI applications in educational contexts. These findings converge with a rich spectrum of previous research examining the factors affecting pre-service teachers’ predisposition towards the integration of AI technologies in the teaching and learning of English as a foreign language. A particularly interesting finding was the positive bidirectional association between the behavioural intention of the prospective EFL teachers and their actual learning of AI. This implies that the motivation and willingness to implement AI tools in education can also be important drivers of continuous professional development for pre-service EFL teachers. In light of this, examining the pre-service EFL teachers’ perceived training needs provided an important insight: the perceived training needs and self-efficacy are not dependent. This suggests that pre-service EFL teachers who feel confident in their technological and pedagogical competence to implement AI successfully in the school system might be more aware of the training they need to become more proficient in AI tools. The identification of future L2 teachers’ training needs is therefore a key determinant underlying the design of well-structured teacher training programmes that equip prospective EFL teachers with adequate knowledge and skills to competently apply AI tools in their workplaces.

The practical implications for educational stakeholders and teacher training providers stemming from these results are mainly related to the importance of integrating AI education into initial teacher training. Pre-service teacher training programmes should not only build technical skills but also foster critical, reflective, and ethical approaches to AI implementation. This includes supporting future EFL teachers in questioning the pedagogical value of AI tools, understanding potential biases, and developing a teaching style that balances innovation and responsibility. This would ensure the development of future EFL teachers who are not only competent users of AI technologies in diverse educational environments but also dedicated professionals who are ready to experiment with such tools in their L2 classrooms. Future research could build upon these findings by exploring the longitudinal development of pre-service EFL teachers’ perceptions of AI tools and their evolving intentions to use them in their teaching practice.