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Article

Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model

by
Isabell Runge
1,*,
Florian Hebibi
1 and
Rebecca Lazarides
1,2
1
Department of Educational Sciences, University of Potsdam, 14476 Potsdam, Germany
2
Cluster of Excellence “Science of Intelligence” (SCIoI), Technische Universität Berlin, 10587 Berlin, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(2), 167; https://doi.org/10.3390/educsci15020167
Submission received: 30 October 2024 / Revised: 16 January 2025 / Accepted: 29 January 2025 / Published: 31 January 2025
(This article belongs to the Special Issue Empowering Teacher Professionalization with Digital Competences)
Browse Figures
Figure 1
<p>The hypothesized research model.</p> "> Figure 2
<p>Path model analyses comparing pre-service teachers’ participation in AI-related courses, self-reported AI-TPACK, perceived AI-related usefulness and ease of use, behavioral intention to use AI in future teaching, and AI usage in teacher training. <span class="html-italic">Notes:</span> standardized effects are reported. Straight lines display statistically significant paths. Dashed straight lines display tested but statistically non-significant paths. Dashed curved lines show statistically non-significant correlations. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p> ">
Versions Notes

Abstract

:
Based on the technology acceptance model (TAM), pre-service teachers’ acceptance of artificial intelligence (AI) is crucial in predicting their intentions to use AI in future teaching, as well as for their actual usage of AI. However, current research offers limited insights into the role of factors regarding usage intentions and behaviors. In particular, AI-related teacher training courses and AI-related technological pedagogical content knowledge (AI-TPACK) might be relevant, but are empirically underinvestigated within the TAM. This study addresses these gaps by investigating the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, their perceptions of AI’s usefulness and ease of use, and both their intention and actual usage of AI. Using path models with data from 143 pre-service teachers, the results revealed that participation in AI-related courses related positively to AI-TPACK and perceived AI-related usefulness. Further, AI-TPACK was positively related to perceived AI-related usefulness and ease of use, which in turn positively related to the behavioral intention to use AI in future teaching and the actual usage of AI for profession-related tasks in teacher training. The study results extend the existing research on TAM and highlight the consideration of participation in AI-related courses and AI-TPACK as further factors in understanding pre-service teachers’ AI acceptance.

1. Introduction

The rapid implementation of artificial intelligence (AI) into teaching and learning processes contributes to the transformation of educational processes and practices (Adelana et al., 2024; Celik, 2023; Luckin et al., 2022). AI-based tools, such as generative AI (e.g., ChatGPT and DALL-E), adaptive learning platforms, and intelligent tutoring systems, are considered to lead to substantive changes in education that might enhance teaching and learning (Mikeladze et al., 2024; UNESCO, 2024; Zhang et al., 2023). The greatest potential for the use of AI in teaching is seen as improving adaptivity and interactivity by providing learner-centered, personalized teaching strategies (Celik, 2023; Chiu et al., 2024; UNESCO, 2024). However, these benefits can only be fully realized if pre-service teachers are adequately prepared to integrate AI into their future teaching (Ayanwale et al., 2022; Ayanwale et al., 2024; UNESCO, 2024; Yao & Wang, 2024). Several studies have highlighted that AI acceptance among pre-service teachers is important for their intention to use AI (Islamoglu et al., 2021; Teo et al., 2019; Zhang et al., 2023). To better understand the factors influencing pre-service teachers’ AI acceptance, the technology acceptance model (TAM) (Davis, 1989) provides a valuable theoretical framework. The TAM assumes that user acceptance of new technologies is influenced by users’ beliefs about technology, i.e., its perceived usefulness and perceived ease of use. These beliefs are critical predictors of users’ intention to use a new technology, which in turn is linked to their actual usage behavior (Davis, 1989). However, the application of TAM to the context of AI in teacher education remains limited. Previous studies in the context of AI have highlighted that pre-service teachers’ AI acceptance can also be shaped by their participation in AI-related university courses (Park, 2023). University courses and training programs that focus on AI for pre-service teachers have thus gained significant importance for future AI-enhanced teaching (An et al., 2023; Salas-Pilco et al., 2022; Sun et al., 2023; Zhang et al., 2023). These courses aim to equip future teachers with both a foundational understanding of AI technologies and the pedagogical knowledge required to integrate AI meaningfully into their classrooms (Gregorio et al., 2024; UNESCO, 2024). In the context of AI in education, the pedagogical knowledge is described in the model of AI-related technological pedagogical content knowledge (AI-TPACK) (Celik, 2023; Mishra et al., 2023). The AI-TPACK model builds upon the original TPACK model, extending it to include competencies specifically required for navigating the unique affordances of AI technologies compared to those of conventional digital technologies (Chiu et al., 2024; Jang & Lee, 2023; Meß & Guggemos, n.d.; Mikeladze et al., 2024; Ng et al., 2023), such as human–machine dialogues, automatic assessments, and constructive feedback (Mishra et al., 2023). While AI-related courses aim to foster pre-service teachers’ AI-TPACK, the combined relationship of these factors with key TAM constructs, such as perceived usefulness and ease of use, has not yet been thoroughly examined. Additionally, studies on AI-related TAM variables for pre-service teachers have predominantly focused on intention, with limited exploration of actual AI usage or AI-TPACK. Existing studies on digital technologies generally suggest a relationship between perceived usefulness and actual technology usage (Scherer et al., 2015), but similar findings are lacking, specifically in the context of AI.
Referring to these gaps, the present study applies the TAM framework to the context of AI and examines the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, perceived AI-related usefulness and ease of use, their intention to use AI in future teaching, and their actual AI usage within teacher training. Understanding how pre-service teachers perceive AI and which factors relate to their intention to and their actual usage of AI is critical for designing teacher training. These insights can support teacher educators, curriculum developers, and policymakers in preparing future teachers for integrating AI meaningfully into classrooms. This is particularly relevant as AI technologies are becoming increasingly prevalent in education (An et al., 2023; UNESCO, 2024; Zhang et al., 2023).

1.1. Artificial Intelligence in the Technology Acceptance Model

The TAM, originally developed by Davis (1989), is a theoretical framework that explains which factors contribute to users’ acceptance and adoption of new technologies. The model assumes that users’ intention to use new technologies is influenced by their attitudes toward it, which are shaped by specific beliefs about the technologies. At the core of the TAM are two key beliefs that predict technology acceptance: perceived usefulness, which refers to users’ beliefs that a digital technology will enhance their performance, and perceived ease of use, which describes how simple and intuitive they find the tool to operate (Davis, 1989). It is assumed that perceived usefulness and perceived ease of use relate to the intention to use digital technologies as an indicator for the actual use (Davis, 1989). The TAM has been widely applied in educational contexts to investigate how pre-service and in-service teachers accept and adopt digital tools (e.g., Schina et al., 2021; Teo et al., 2019). Some studies have shown that perceived usefulness and perceived ease of use explain pre-service teachers’ intention to use different digital technologies (e.g., Joo et al., 2017). This means that if a digital technology—like AI—is perceived by pre-service teachers as highly useful and user-friendly, pre-service teachers are also more likely to accept and intend to teach with that digital technology. Meß and Guggemos (submitted) show in their systematic literature review that the TAM has already been used to explain AI-related acceptance, and that the rare existing empirical evidence confirms that the perceived AI-related usefulness and perceived ease of use are substantially related to pre-service teachers’ intention to use AI in their future teaching (Ma & Lei, 2024; Vo & Pancratz, 2023; Zhang et al., 2023). However, there are only a few empirical studies that look at AI acceptance with a focus on pre-service teachers (Zhang et al., 2023). Following the argumentation by Zhang et al. (2023), it is essential to further understand pre-service teachers’ AI acceptance due to the increasing importance of AI in teaching and learning processes and its influence on future student learning outcomes. Additionally, previous studies have been primarily limited to behavioral intention to use AI, which is an indicator (Davis, 1989) but not a representation of the actual use of AI. In teacher training, pre-service teachers reported using AI during their studies for academic purposes like writing assistance or time management but also for profession-related tasks like lesson planning for developing and adapting teaching materials or teaching activities (Hsu et al., 2024). There is currently a lack of research examining how TAM constructs—specifically, perceived usefulness and perceived ease of use—relate to this teacher profession-related usage. This is a notable gap, as research on digital technologies in education suggests that, e.g., perceived usefulness, is directly linked to actual use (Scherer et al., 2015). Exploring the relationships between additional TAM constructs and actual AI use among pre-service teachers could offer valuable insights into the factors driving technology adoption in teacher training. The TAM emphasizes the roles of perceived usefulness and ease of use as key predictors of technology acceptance but also points to the influence of further factors on these beliefs (Davis, 1989). In the context of teacher training, factors like pre-service teachers’ participation in AI-related courses and AI-related technological knowledge will be discussed in the following sections.

1.1.1. Participation in AI-Related Courses in Teacher Training

Teacher training at university is the first stage of professional development of pre-service teachers and encompasses practices, strategies, and policies that equip teachers with the knowledge, competencies, and ethical guidance needed to teach effectively (Luckin et al., 2022; Salas-Pilco et al., 2022). In the context of digitalization, teacher training is now facing the challenge of adapting to rapidly changing technologies, such as the use of AI in teaching (Ayanwale et al., 2024; Jang & Lee, 2023; Zhang et al., 2023). A lack of professional development opportunities could result in teachers feeling overwhelmed and unprepared when integrating AI into their teaching practice (Kim & Kwon, 2023). In contrast, supporting pre-service teachers in the pedagogical use of AI can help to create a more effective learning atmosphere for students and facilitate the introduction of AI-enhanced teaching in future classrooms (Gregorio et al., 2024). Therefore, global policymakers, such as those participating in the Standing Conference of the Ministers of Education and Cultural Affairs in Germany (2021), where our study was carried out, are encouraging governments to introduce appropriate regulations and training for educators to ensure a human-centered approach to the use of generative AI in education. To achieve these aims, in regards to the AI competency framework for teachers, UNESCO (2024) emphasizes the importance of preparing teachers to effectively integrate AI tools in education, calling for comprehensive professional development that spans their entire career, from pre-service training to ongoing in-service support. Thus, AI-related courses at university are gaining importance for preparing future teachers to design AI-enhanced teaching (An et al., 2023; Salas-Pilco et al., 2022; Zhang et al., 2023). Current studies are focusing primarily on in-service teachers in teacher education (Zhang et al., 2023), and only a few studies deal with the question of how teacher training can be improved to prepare pre-service teachers for the future teaching implementation of AI (Ayanwale et al., 2022). However, current studies on the evaluation of professional development programs for pre-service teachers focused on designing lessons incorporating ChatGPT have proven to be effective in enhancing pre-service teachers’ self-reported AI literacy skills and attitudes (Younis, 2024). Specifically, Younis (2024) found that after participating in such a program, pre-service teachers reported more positive attitudes towards integrating AI tools into their teaching practice and demonstrated improvements in their self-reported AI literacy, particularly in understanding the pedagogical applications of AI. Park (2023) was able to show that after a 15-week extension program that aimed to improve the professionalism of pre-service teachers in AI education, pre-service teachers perceived increased joy in acquiring knowledge about AI tools, increased usefulness of AI in the classroom, and increase AI literacy. Within these AI-related seminars and training programs, the research suggests that pre-service teachers benefit most from practical, experience-based learning environments in the context of AI (Jang & Lee, 2023; Park, 2023; Younis, 2024). However, these implications relate primarily to the promotion of AI-related competencies. No empirical findings exist concerning the relationships between the participation in AI-related courses and pre-service teachers’ perceived usefulness and perceived ease of use of AI. Based on the results of studies on digital technologies, it can be shown that the usefulness and ease of use of digital technology could be increased as part of a teacher training course (e.g., Schina et al., 2021).

1.1.2. AI-TPACK

Various studies have considered pre-service and in-service teachers’ self-reported technological pedagogical content knowledge (TPACK) (Mishra et al., 2023) as another salient factor in the TAM, which significantly affects teachers’ perceptions of usefulness and ease of use (Joo et al., 2017; Teo et al., 2019; Wangdi et al., 2023), their intention to use digital technologies (Max et al., 2023; Sun et al., 2023; Teo et al., 2019), and their actual use (Hsu et al., 2024). Related to AI, most existing studies primarily address teachers’ AI literacy, which emphasizes a foundational technological understanding of AI techniques and concepts (Ayanwale et al., 2024) and refers to the skills that allow individuals to critically assess AI technologies, communicate with AI, and utilize it in various contexts (Almatrafi et al., 2024). However, in addition to basic technological knowledge, teachers also need AI-related pedagogical knowledge to critically evaluate and integrate AI into educational settings to foster students’ learning (Chiu et al., 2024; Meß & Guggemos, n.d.; Mikeladze et al., 2024; Ng et al., 2023; Ning et al., 2024). As a consequence, the TPACK model has been extended to describe the technological and pedagogical content knowledge required for effectively integrating AI in teaching, resulting in the AI-TPACK model (Celik, 2023; Mishra et al., 2023). The integration of AI within TPACK describes adapting teaching practices to the evolving demands of AI, equipping teachers with the competencies necessary for navigating the unique affordances of AI technologies in teaching (Celik, 2023; Mishra et al., 2023). Just like the original TPACK model, the knowledge dimensions of the extended AI-TPACK model can also be assigned to the digital competence concept as competence-related beliefs (Rubach, 2024; Rubach & Lazarides, 2023). Regarding the TAM, An et al. (2023) showed in their study that in-service teachers’ AI-TPACK predicted the intention to use AI technologies in teaching. However, there are also study from Yao and Wang (2024), who investigated the relationships between pre-service teachers’ AI literacy and AI-related intention, revealing no significant results. Current studies only consider AI literacy, perceived usefulness, and ease of use, showing that all three constructs are associated with pre-service teachers’ intention to use AI technologies (Ma & Lei, 2024). To date, there have been no studies investigating the relationships between pre-service teachers’ AI-TPACK, perceived usefulness, ease of use, behavioral intention, and actual use of AI.

1.2. The Present Study

Several studies grounded in the TAM have explored pre-service teachers’ acceptance of various digital technologies (e.g., Islamoglu et al., 2021; Teo et al., 2019). However, only a limited number of studies have extended this model to specifically assess pre-service teachers’ acceptance of AI, and even fewer have included factors such as participation in AI-related courses and AI-TPACK. This highlights a significant gap in understanding how these factors jointly contribute to shaping pre-service teachers’ acceptance and usage of AI in their future teaching practice. Figure 1 depicts our hypothesized research model, addressing these gaps. Accordingly, this study investigates the following research questions and hypotheses, as outlined in the TAM framework:
RQ1: To what extent does pre-service teachers’ participation in AI-related courses relate to their self-reported AI-TPACK and their perceptions of AI-related usefulness and ease of use?
H1a. 
Pre-service teachers’ participation in AI-related courses is positively related with their self-reported AI-TPACK.
According to study results on the evaluation of AI-related professional development programs for pre-service teachers (Younis, 2024), we also assume that participation in AI-related courses is positively related to competence-related beliefs in terms of self-reported AI-TPACK.
H1b. 
Pre-service teachers’ participation in AI-related courses is positively related to their perceived AI-related usefulness and ease of use.
Although there are no results on the direct relationships between AI-related courses and perceived AI-related usefulness and ease of use, studies such as those by Park (2023) and Younis (2024) indicate that pre-service teachers’ participation in courses is related to a positive change in attitudes and perceptions of AI. In the context of digital technologies, Schina et al. (2021) were also able to show that the participation in teacher training courses is related to pre-service-teachers’ perception of usefulness and ease of use. Since AI belongs to digital technologies in the broadest sense, it could be assumed that these TAM-related constructs are also related to teacher training courses on AI.
RQ2: To what extent does pre-service teachers’ self-reported AI-TPACK relate to their perceptions of AI-related usefulness and ease of use, as well as to their behavioral intention to use AI in future teaching and to their AI usage in teacher training?
H2a. 
Pre-service teachers’ self-reported AI-TPACK relates positively to their perceived AI-related usefulness and ease of use.
There is a lack of studies on the relationships between pre-service teachers’ AI-TPACK and perceived AI-related usefulness and ease of use; however, there is a large number of results regarding TPACK and perceived usefulness and ease of use illustrating positive correlations (Joo et al., 2017; Teo et al., 2019; Wangdi et al., 2023). Based on these research findings, similar correlations are assumed for the AI context.
H2b. 
Pre-service teachers’ self-reported AI-TPACK relates positively to their behavioral intention to use AI in future teaching.
According to the study results of An et al. (2023), in-service teachers’ self-reported AI-TPACK was statistically significantly related to their intention to use AI in teaching. It can be assumed that these relationships also exist for pre-service teachers.
H2c. 
Pre-service teachers’ self-reported AI-TPACK is positively related to their AI usage in teacher training.
So far, there are no studies investigating the relationships between pre-service teachers’ AI-TPACK and their actual use of AI, but study results on TPACK indicate statistically significant positive relationships to the use of digital technologies (Hsu et al., 2024). These results are therefore assumed in context of AI-TPACK.
RQ3: How do pre-service teachers’ self-reported AI-TPACK and their perceptions of AI-related usefulness and ease of use relate to their behavioral intention to use AI in future teaching and to their AI usage in teacher training?
H3a. 
Perceived AI-related usefulness and ease of use relate positively to behavioral intention to use AI in future teaching.
Based on existing study results for pre-service teachers (Ma & Lei, 2024; Yao & Wang, 2024; Zhang et al., 2023), positive relationships between perceptions of usefulness and ease of use for AI and intention to use AI in teaching are assumed.
H3b. 
Perceived AI-related usefulness und ease of use are positively related to pre-service teachers’ AI usage in teacher training.
In the context of digital technologies, Scherer et al. (2015) demonstrated that perceived usefulness has a positive relationship with the actual use of digital technologies, suggesting that when individuals believe a technology can improve their effectiveness, they are more likely to use it. Although similar studies explicitly examining ease of use in relation to actual usage are scarce, findings of usefulness are also assumed for ease of use in the context of AI.

2. Materials and Methods

2.1. Sample

This study draws on a sample of 143 pre-service teachers enrolled in various German universities, with the majority coming from Brandenburg (42%) and Berlin (13%). Data were collected during the summer semester of 2024, from April to September. The study was approved by the Ethics Committee of the University of Potsdam, Germany. During the acquisition process, the survey link was shared through multiple university newsletters and social media channels focused on teacher training. Outliers were identified by examining the z-scores, with values exceeding ±3 considered as outliers. Consequently, 41 cases with z-scores above ±3.00 were excluded from further analysis. The remaining 143 pre-service teachers included in our sample were enrolled in either a bachelor’s (56%) or master’s (42%) program in teacher training. Pre-service teachers predominantly identified as women (60%), and most (43%) were between 21 and 24 years old. The majority of the pre-service teachers had not yet participated in AI-related courses at the university (56%), with only 33% having had such experience. The sample was almost evenly divided between first-generation academic students (43%) and those from academic households (47%).

2.2. Measures

The online questionnaire consisted of two parts: the first part collected socio-demographic information, and the second part measured pre-service teachers’ participation in AI-related courses, their perceived AI-related usefulness and ease of use, their self-reported AI-TPACK, their behavioral intention for AI usage in future teaching, and their AI usage in teacher training. The items were originally presented in German and are translated into English in this paper. The wording of all items is provided in Table A2 in Appendix A.
Socio-Demographics. The pre-service teachers’ gender was assessed using categorial coding (1 = female; 2 = male; 3 = diverse). As there were no responses regarding “diverse”, we recoded the item as binary (1 = female; 2 = male). Age was assessed with a categorial scale (1 = below age 20, 2 = 21 to 24 years, 3 = 25 to 30 years, 4 = 31 to 34 years, and 5 = 35 years or more). The pre-service teachers were also asked whether they were aiming for a bachelor’s or master’s degree at the time of the survey and which academic semester they were currently attending. With regards to educational background, the pre-service teachers were asked if members of their close family obtained an academic professional qualification (university of applied sciences; college; university).
Participation in AI-related Courses. The pre-service teachers were asked how many courses they had attended during their teacher training that dealt with AI in teaching. The response options were categorized as 1 = none, 2 = 1–2 courses, 3 = 3–4 courses, and 4 = more than 5 courses. The answers were recoded into a binary format for the analyses (1 = none, 2 = at least one AI-related course).
Perceived AI-related Usefulness. To assess pre-service teachers’ perceived usefulness of using AI in teaching, they were asked to rate four items from Choi et al. (2023). The items were reformulated for teachers, so that an example item states: “Using AI in the teaching profession would improve my performance.” Each item was rated on a five-point Likert scale ranging from 1 (do not agree at all) to 5 (fully agree). One item was excluded from the analysis due to its insufficient contribution to the internal consistency of the scale. After exclusion, the internal consistency of the scale used in the present study was ω = 0.88.
Perceived AI-related Ease of Use. A scale from An et al. (2023) was used to assess pre-service teachers’ perceived ease of use of AI in teaching. Four items were rated on a five-point Likert scale ranging from 1 (do not agree at all) to 5 (fully agree). An example item is “AI teaching systems are easy to operate for me.” The internal consistency of the scale in the present study was ω = 0.93.
AI-TPACK. Pre-service teachers’ AI-related technological pedagogical content knowledge was assessed through a scale created by An et al. (2023). The items were reformulated for the general use of AI in teaching, so that one example reads: “I know how to use appropriate strategies with AI to help students learn better.” From the original scale with nine items, four items were excluded due to low reliability, to ensure overall consistency. The shortened scale, as applied in the present study, has an internal consistency of ω = 0.94.
Behavioral Intention for AI Usage. To assess pre-service teachers’ behavioral intention to use AI technologies in future teaching, they were asked to rate three items from Galindo-Domínguez et al. (2023) on a five-point Likert scale ranging from 1 (do not agree at all) to 5 (fully agree). An example item is “I would love to be able to use artificial intelligence in my work as a teacher”. The internal consistency of the scale used in the present study was excellent, with ω = 0.88.
AI Usage in Teacher Training. On a scale based on the work of Garrel et al. (2023), pre-service teachers reported how often they use AI in teacher training for (a) the preparation of teaching materials, (b) recommendations for relevant teaching materials, (c) planning teaching units, and (d) the generation of test questions. The items were answered on a six-point Likert scale ranging from 1 (never) to 6 (very often). The internal consistency, calculated for the scale used in the present study, was ω = 0.87.

2.3. Statistics

To test our hypotheses, we conducted a path model using Mplus 8.10. For the path model structure, we followed the relationship assumptions of the underlying TAM, modified by adding additional factors. Specifically, pre-service teachers’ participation in AI-related courses is modeled as a factor related to AI-TPACK, perceived AI-related usefulness, and perceived ease of use. AI-TPACK, in turn, is also modeled as a factor for perceived usefulness, perceived ease of use, behavioral intention to use AI, and actual use of AI in teacher training. Following TAM, usefulness and ease of use are analyzed in relation to both behavioral intention for AI usage in future teaching and AI usage in teacher training. In contrast to the TAM assumptions, AI usage in teacher training is not modeled in relation to behavioral intention, given the context of pre-service teachers. Most pre-service teachers are not yet actively teaching during their studies. Thus, their actual AI usage reflects professional-related tasks within their teacher training, rather than teaching applications. As a result, we did not assume direct relationships between the behavioral intention for AI usage and AI usage in teacher training. Within the path model, the constructs of AI-TPACK, perceived usefulness, perceived ease of use, behavioral intention to use AI, and actual use of AI in teacher training are modeled as latent, whereas only participation in AI-related course is modeled as a manifest binary variable.
The evaluation of model fit was carried out using several key indicators, as recommended by Kline (2023), including χ2, CFI (comparative fit index), TLI (Tucker–Lewis Index), RMSEA (root mean square error of approximation), and SRMR (standardized root mean residual). Following established guidelines, values above 0.90 for CFI and TLI, and below 0.08 for both RMSEA and SRMR, were considered indicative of an acceptable fit (Hu & Bentler, 1999). All path analyses utilized the maximum likelihood estimation with robust standard errors (MLR). The variance explained for the dependent variables within the path models was estimated using the R2 measure.
In the initial path model analyses, we included covariates shown to be statistically significant in previous AI-related studies, specifically pre-service teachers’ gender (Zhang et al., 2023) and academic semester (Zinn et al., 2022). However, these covariates did not show statistically significant relationships in our path models and negatively impacted the overall model fit. Consequently, the final analyses presented in the results section were conducted without these covariates. For comprehensive documentation, we have included the path model with covariates in the Appendix A (see Table A1).

3. Results

3.1. Descriptive Statistics

Table 1 demonstrates the means, standard deviations, and bivariate Pearson’s correlations of the key constructs. The correlation analyses show that pre-service teacher participation in AI-related courses was positively associated with all constructs that were included in the analyses, with AI-TPACK showing the highest correlation coefficient. Among the AI acceptance constructs, perceived AI-related usefulness and ease of use had statistically significant positive correlations with AI-TPACK, AI usage in teacher training, and with each other. However, only perceived usefulness correlated positively with behavioral intention for AI usage. Furthermore, the results indicate that AI-TPACK and behavioral intention for AI usage were both statistically significantly positively correlated with AI usage in teacher training.

3.2. Path Model Analyses

The path model is depicted in Figure 2, and the standardized coefficients are reported in Table 2. The results of the path model indicate that participation in AI-related courses was positively related to AI-TPACK and perceived AI-related usefulness. No statistically significant relationships occurred between participation in AI-related courses and pre-service teachers’ perceived ease of use. AI-TPACK was positively linked to both the perceived AI-related usefulness and ease of use. However, there were no statistically significant relationships between AI-TPACK and pre-service teachers’ behavioral intention to use AI in future teaching or their actual use of AI in teacher training. Perceived usefulness is statistically significantly related to both behavioral intention for AI usage and actual AI usage in teacher training. Pre-service teachers’ perceived AI-related ease of use, by contrast, only showed positive relationships with the AI usage in teacher training. The path model achieved an acceptable fit to the data, as follows: χ2 = 227.22, df = 150, CFI = 0.95, TLI = 0.93, RMSEA = 0.06, and SRMR = 0.05.

4. Discussion

The acceptance of digital technologies by pre-service teachers has already been shown to be important for their behavioral intention to use these technologies in future teaching and their actual use of technologies (e.g., Islamoglu et al., 2021; Teo et al., 2019). However, the application of the TAM in the context of AI, as well as an examination of factors related to pre-service teachers’ AI acceptance, has so far been limited. Addressing this research gap, the present study investigated the roles of AI-related courses and AI-TPACK as they relate to the TAM constructs—perceived usefulness; perceived ease of use—and to both the behavioral intention and actual usage of AI in teacher training. In the following section, we discuss our study findings in detail, along with their theoretical and practical implications.

4.1. Participation in AI-Related Courses, AI-TPACK, and Perceptions of AI-Related Usefulness and Ease of Use

Regarding the first research question (RQ1), the results indicate that pre-service teachers who participated in AI-related courses in teacher training reported high AI-TPACK (H1a) and a high perception of usefulness of AI in teaching (H1b). As expected (H1a), the positive relationships with AI-TPACK aligned with existing study results showing that AI-related professional development programs for pre-service teachers were positively related to self-reported competence beliefs in terms of AI-TPACK (Younis, 2024). These results may indicate that the participation in AI-related courses is relevant to how pre-service teachers perceive their own AI-TPACK. According to our assumption (H1b), the participation in AI-related courses was positively related to pre-service teachers’ perception of usefulness of AI. Contrary to our assumption (H1b), pre-service teachers who participated in AI-related courses did not report statistically significantly high levels of perceived AI-related ease of use. Previous findings regarding digital technologies showed that participating in digital-related teacher training courses related positively to pre-service teachers’ perceived usefulness and ease of use (Schina et al., 2021). Our results support these relationships for usefulness in the context of AI, but not for ease of use. Thus, Hypothesis 1b can thus only be partially confirmed. The absence of relationships between course participation and perceived ease of use in our results could possibly be linked to the content of the AI-related courses. It is possible that the AI-related courses prioritized pedagogical applications of AI over technical understanding, consistent with teacher training programs focused on instructional use. In the studies of Park (2023) and Younis (2024) on AI-focused courses, while an understanding of AI was addressed, the main emphasis was on subject-specific instruction regarding designing with AI and promoting student engagement by using AI. This could explain why pre-service teachers who have participated in AI-related courses report perceiving AI as useful because the courses are more likely to teach how AI can be used for teaching to increase productivity and effectiveness. These assumptions should be tested in future studies, taking into account course content.

4.2. AI-TPACK and Perceived AI-Related Usefulness, Ease of Use, Behavioral Intention to Use AI, and Actual Usage of AI in Teacher Training

Based on the TAM, the second research question (RQ2) examined the extent to which pre-service teachers’ AI-TPACK is related to the constructs of their acceptance—perceived usefulness and ease of use (H2a)—as well as their AI-related behavioral intention (H2b) and actual use in teacher training (H2c). Previous studies on digital technologies have demonstrated positive relationships between TPACK and both perceived usefulness and ease of use for pre-service teachers (Joo et al., 2017). Our study provides the first results on these relationships in the AI context, since the results indicate that pre-service teachers’ self-reported AI-TPACK was positively related to their perceived AI-related usefulness and ease of use. The results suggest that pre-service teachers with a high AI-TPACK are more likely to perceive AI as both useful and easy to use. Hypothesis 2a can thus be confirmed. In contrast, Hypotheses 2b and 2c, which posited that AI-TPACK would be related to behavioral intention to use AI and actual AI usage, were not supported. This diverges from findings for in-service teachers showing positive relationships between AI-TPACK and intention to use AI (An et al., 2023). A possible explanation for these differences could be that our study focused on pre-service teachers, whereas An et al. (2023) examined in-service teachers. Supporting our findings, Yao and Wang (2024), who also studied pre-service teachers, found no statistically significant relationships between self-reported AI literacy and the behavioral intention to use AI. With regard to actual AI usage, studies specifically focusing on pre-service teachers and AI are lacking. However, previous research on digital TPACK found positive relationships between TPACK and in-service teachers’ use of digital technologies (Hsu et al., 2024). These discrepancies may reflect the difference in teaching experience between pre- and in-service teachers. In-service teachers’ practical experience may enhance the application of AI-TPACK, strengthening the intention and actual use of AI. These findings underscore the need for further research that distinguishes between pre- and in-service teachers, focusing on how the context of professional experience may affect the relationships between AI-TPACK on AI-related behaviors in terms of behavioral intention and actual usage. Furthermore, based on the TAM, it could be argued that AI-TPACK may not have a direct relationship with behavioral intention or actual use of AI. Mediation analyses were not conducted in the present study, which is why this assumption cannot be made on the basis of the results. But Yao and Wang (2024) found an indirect relationship between pre-service teachers’ AI literacy and their intention to use AI, mediated by perceived usefulness and ease of use. Consequently, future research should consider examining AI-TPACK’s potential indirect relationship within TAM to fully capture its role in shaping behavioral intention to use AI and the actual usage of AI among pre-service teachers.

4.3. Perceived AI-Related Usefulness, Ease of Use, Behavioral Intention to Use AI, and Actual Usage of AI in Teacher Training

The third research question (RQ3) addressed the relationships between pre-service teachers’ acceptance of AI in teaching, their behavioral intention to use AI in future teaching, and their actual use of AI in teacher training for profession-related tasks. In alignment with the TAM, our findings demonstrated that perceived AI-related usefulness positively related to the behavioral intention to use AI in teaching (H3a). Specifically, if pre-service teachers perceive AI as useful, they are more likely to intend to use it in their future teaching practice. Similar results were shown in previous studies (Ma & Lei, 2024; Yao & Wang, 2024; Zhang et al., 2023). However, contrary to the assumptions in the TAM and previous study results, no statistically significant relationships were found between pre-service teachers’ perceived ease of use and intention to use AI. Hypothesis 3a is thus partially confirmed for AI-related usefulness. Previous studies (Yao & Wang, 2024; Zhang et al., 2023) have already observed that perceived AI-related usefulness is often a stronger predictor than ease of use for behavioral intention, with higher correlation coefficients typically observed for usefulness. Our results are consistent with these findings, as only perceived AI-related usefulness was statistically significantly positively related to pre-service teachers’ intention to use AI. Zhang et al. (2023) explain that pre-service teachers are primarily interested in using AI for enhancing instructional effectiveness and for fostering learning outcomes; therefore, they perceive a high intention to use AI if they also rate the usefulness as high. This focus on productivity and impact may lead them to perceive high usefulness, which in turn strengthens their intention to use AI, while ease of use may be less critical in this context. Additionally, this could indicate that pre-service teachers may place greater importance on the pedagogical benefits of AI tools rather than on their technical usability. This focus aligns with the objectives of teacher training programs, which emphasize the development of didactic and pedagogical strategies. As a result, pre-service teachers may prioritize how AI can be used to improve teaching and learning processes over how easy it is to operate these tools. Since teacher education aims to equip future teachers with the skills to integrate technologies meaningfully into their classrooms, the perceived usefulness of AI becomes a more decisive factor in shaping their intention to use AI (An et al., 2023; Ma & Lei, 2024; Scherer et al., 2015; Yao & Wang, 2024; Zhang et al., 2023).
Moreover, we examined the relationships between perceived AI-related usefulness, ease of use, and the actual usage of AI for profession-related tasks (H3b). Our results showed that both the perceived usefulness and the perceived ease of use positively related to pre-service teachers’ use of AI in teacher training. Specifically, pre-service teachers who reported that they perceived AI for teaching as useful and easy to use also reported that they use AI in their teacher training to plan lessons, design teaching materials, and develop test questions. This finding is consistent with the work of Scherer et al. (2015), which showed that in-service teachers’ perceived usefulness of digital technologies was linked to its use in teaching. Our study extends this research to pre-service teachers, highlighting that for AI, both perceived usefulness and ease of use are positively related to its actual usage during teacher training.

4.4. Limitations and Future Steps

The following limitations should be considered when interpreting the findings of the present study. First, the sample is limited to student teachers from Germany, more than half of whom came from two out of sixteen federal states. Moreover, the sample size is 143 pre-service teachers and is therefore quite small. These aspects limit the generalizability of the findings. Future studies should include a larger and more heterogeneous group of pre-service teachers.
Second, given that this study utilizes cross-sectional data, our model examines relationships rather than predictive or causal effects. This design does not allow for tracking whether, e.g., pre-service teachers’ self-reported AI-TPACK and perceived usefulness or ease of use develops as they progress through their training or specifically after their participation in an AI-related course. With regard to our results, it could also be possible that pre-service teachers are more likely to attend AI-related courses because they already consider AI to be useful in teaching or because they already rate their AI-TPACK as high. Therefore, these relationships should be examined longitudinally. In this context, longitudinal data could also be used to identify possible mediation effects of perceived AI-related usefulness and ease of use for the relationships between AI-TPACK, behavioral intention to use AI, and actual usage of AI.
Lastly, we do not have any information on the AI courses attended, so we cannot consider, e.g., the courses’ duration, quality, or content. Taking these aspects into account would support the interpretation of statistically identified relationships. In the context of AI-related courses, it is already known that, if they have practical components, these are particularly related to pre-service teachers’ AI competencies (Jang & Lee, 2023; Park, 2023; Younis, 2024). Additional information on the courses should thus be assessed in future studies. Additionally, the study did not consider information on the specific AI tools used by pre-service teachers during their teacher training. Different AI tools may have varying impacts on pre-service teachers’ acceptance toward AI integration. Future research should explore how specific AI tools relate to these factors to better inform the design of effective teacher training programs.

4.5. Theoretical and Practical Implications

The theoretical implications lie in the extension of the TAM to include AI-related courses, as well as AI-TPACK, as additional factors that are important for pre-service teachers’ perceived AI-related usefulness, ease of use, behavioral intention to use AI in future teaching, and the actual usage of AI in teacher training. Furthermore, measuring pre-service teachers’ actual AI use in profession-related tasks, rather than focusing solely on their behavioral intention, provides a concrete perspective on AI acceptance in teacher training. This expands the TAM by illustrating how pre-service teachers apply AI to teaching-relevant activities, despite limited teaching experience. Capturing this usage could offer a preliminary indication of pre-service teachers’ AI integration into teaching practices. The expansion of the TAM contributes to a more comprehensive understanding of the relationships that could be relevant for pre-service teachers’ AI acceptance.
Given the growing adoption of AI in teaching and learning processes (Luckin et al., 2022), the participation in AI-related courses may be relevant for pre-service teachers’ AI-TPACK and their perceived AI-related usefulness, which in turn are related to the intention and actual usage of AI. Practical implications for teacher educators suggest that AI-related courses in teacher training should emphasize how AI can be integrated into teaching to enhance instructional processes and improve learning outcomes. Policymakers and curriculum developers may also benefit from these findings by integrating AI-related course content aimed at supporting pre-service teachers’ AI-TPACK into standardized teacher training guidelines, ensuring that future educators are adequately prepared for AI-enhanced teaching. Through these efforts, pre-service teachers may be more likely to perceive AI as useful, which could positively relate to their intention to use AI in teaching and increase their usage of AI for profession-related tasks during their teacher training.

Author Contributions

Conceptualization, I.R. and R.L.; methodology, I.R.; formal analysis, I.R.; investigation, F.H.; data curation, I.R. and F.H.; writing—original draft preparation, I.R.; writing—review and editing, I.R. and R.L.; supervision, R.L.; project administration, R.L.; All authors have read and agreed to the published version of the manuscript.

Funding

Funded by the European Union—NextGenerationEU and supported by the German Federal Ministry of Education and Research. The views and opinions expressed are solely those of the author and do not necessarily reflect the views of the European Union, European Commission or the Federal Ministry of Education and Research. Neither the European Union, the European Commission nor the Federal Ministry of Education and Research can be held responsible for them.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of University of Potsdam, Germany (13/2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Standardized coefficients of the path model with covariates of pre-service teachers’ gender and academic semester (N = 143).
Table A1. Standardized coefficients of the path model with covariates of pre-service teachers’ gender and academic semester (N = 143).
AI-TPACKPerceived AI-Related UsefulnessPerceived AI-Related Ease of UseBehavioral Intention for AI UsageAI Usage in Teacher Training
ßSEpßSEpßSEpßSEpßSEp
Participation in AI-related courses0.4450.0810.0000.2830.6120.643−0.0150.1040.884------
AI-TPACK---0.2720.1170.0200.4370.0970.000−0.0390.1200.7460.0970.1710.572
Perceived AI-related usefulness---------0.6590.1440.0000.5630.4120.172
Perceived AI-related ease of use---------0.0140.0900.8730.3480.1180.003
Gender−0.0180.0910.841−0.5881.2600.6410.1360.0850.111−0.1190.0830.153−0.48110.1120.665
Academic semester0.0400.0850.6370.4333.5370.903−0.1700.1040.1030.0780.0830.1530.1070.0810.189
Notes: Model fit: χ2 = 281.32, df = 183, CFI = 0.93, TLI = 0.92, RMSEA = 0.06, and SRMR = 0.05.
Table A2. Item wording.
Table A2. Item wording.
Participation in AI-related Courses
How many courses did you attend during your teacher training program that dealt with AI in teaching?
Perceived AI-related Usefulness
-
Using AI in the teaching profession would improve my performance.
-
Using AI in the teaching profession would enhance my effectiveness.
-
I find AI to be useful in teaching profession.
Perceived AI-related Ease of Use
-
AI teaching systems are easy to operate for me.
-
I think AI teaching systems are very simple.
-
I can easily master the skills of using AI teaching systems.
-
The operation of AI teaching systems is clear.
AI-TPACK
-
I know how to use appropriate strategies with AI to help students learn.
-
I know how to use appropriate strategies with AI to help students better practice their skills.
-
I know how to use appropriate strategies with AI to help students learn better.
-
I know how to use the strategy of personalized guidance to improve students’ skills with the help of AI.
-
I know how to use appropriate strategies to provide students with opportunities to use their skills with the help of AI.
Behavioral Intention for AI Usage
-
I am willing to use AI in my teaching practice.
-
I am willing to explore new opportunities for integrating AI into teaching and learning processes.
-
I would love to be able to use AI in my work as a teacher.
AI Usage in Teacher Training
As part of my teacher training program, I use AI …
-
for the provision of teaching materials.
-
for recommendations for relevant teaching materials.
-
for planning teaching units.
-
for the creation of exam questions.

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Education 15 00167 g001
Figure 1. The hypothesized research model.
Figure 1. The hypothesized research model.
Education 15 00167 g001
Education 15 00167 g002
Figure 2. Path model analyses comparing pre-service teachers’ participation in AI-related courses, self-reported AI-TPACK, perceived AI-related usefulness and ease of use, behavioral intention to use AI in future teaching, and AI usage in teacher training. Notes: standardized effects are reported. Straight lines display statistically significant paths. Dashed straight lines display tested but statistically non-significant paths. Dashed curved lines show statistically non-significant correlations. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 2. Path model analyses comparing pre-service teachers’ participation in AI-related courses, self-reported AI-TPACK, perceived AI-related usefulness and ease of use, behavioral intention to use AI in future teaching, and AI usage in teacher training. Notes: standardized effects are reported. Straight lines display statistically significant paths. Dashed straight lines display tested but statistically non-significant paths. Dashed curved lines show statistically non-significant correlations. * p < 0.05; ** p < 0.01; *** p < 0.001.
Education 15 00167 g002
Table 1. Bivariate Pearson’s correlations among analyzed constructs (N = 143).
Table 1. Bivariate Pearson’s correlations among analyzed constructs (N = 143).
MSDPerceived AI-Related UsefulnessPerceived AI-Related Ease of UseAI-TPACKBehavioral Intention for AI UsageAI Usage in Teacher Training
Participation in AI-related courses1.380.490.34 ***0.21 *0.45 ***0.26 *0.27 **
Perceived AI-related usefulness3.760.90-0.32 **0.36 ***0.68 ***0.44 **
Perceived AI-related ease of use2.961.04 -0.43 ***0.150.52 ***
AI-TPACK2.360.90 -0.190.41 ***
Behavioral intention for AI usage4.080.73 -0.34 ***
AI usage in teacher training2.361.24 -
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 2. Standardized coefficients of the path model (N = 143).
Table 2. Standardized coefficients of the path model (N = 143).
AI-TPACKPerceived AI-Related UsefulnessPerceived AI-Related Ease of UseBehavioral Intention for AI UsageAI Usage in Teacher Training
ßSEpßSEpßSEpßSEpßSEp
Participation in AI-related courses0.4490.0780.0000.2350.1120.0000.0250.1020.808------
AI-TPACK---0.2600.1210.0320.4170.1020.000−0.0450.1140.6900.1650.1200.169
Perceived AI-related usefulness---------0.7220.1360.0000.2660.0920.004
Perceived AI-related ease of use---------−0.0610.0940.5180.3620.0930.000
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Runge, I.; Hebibi, F.; Lazarides, R. Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Educ. Sci. 2025, 15, 167. https://doi.org/10.3390/educsci15020167

AMA Style

Runge I, Hebibi F, Lazarides R. Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences. 2025; 15(2):167. https://doi.org/10.3390/educsci15020167

Chicago/Turabian Style

Runge, Isabell, Florian Hebibi, and Rebecca Lazarides. 2025. "Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model" Education Sciences 15, no. 2: 167. https://doi.org/10.3390/educsci15020167

APA Style

Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences, 15(2), 167. https://doi.org/10.3390/educsci15020167

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