Artificial intelligence (AI) has emerged as a transformative force across various fields, including healthcare, finance, law, and education (Manyika et al., 2017). In the field of education, AI has been increasingly utilized to support personalized learning, automate feedback processes, and enhance administrative efficiency (Chounta et al., 2022). The successful integration of AI in educational settings depends on educators’ readiness to adopt these technologies, as well as their ability to manage the psychological and emotional challenges that come with technological change.

Readiness in digital environments refers to an individual’s ability to effectively engage with technology by enhancing self-efficacy, communication skills, and autonomous learning. Both in- and pre-service teachers’ readiness to incorporate AI technologies into their teaching practices is crucial for future classroom innovation. AI tools, such as intelligent tutoring systems, intelligent chatbots and adaptive learning platforms, have already begun reshaping educational environments (Akpan et al., 2025). Within the European and international educational landscape, initiatives such as the Digital Education Action Plan (European Commission, 2020) and UNESCO’s guidelines on AI in education (such as AI and education: Guidance for policy-makers) (UNESCO, 2021) highlight the need for teachers and students to develop AI competency.  While readiness fosters a positive approach to AI adoption, AI anxiety can serve as a psychological barrier, limiting individuals' willingness to engage with AI technologies. AI anxiety is a growing concern, defined as the nervousness and uncertainty individuals experience due to the rapid advancement of AI technologies (Wang et al., 2024). The fear of job displacement, challenges in learning AI applications, and misconceptions about AI autonomy contribute to increased anxiety levels (Wang & Wang, 2019). While automation and computerization have historically reshaped the job market, the increasing role of AI in education raises concerns about whether teachers will be replaced or whether AI will augment their roles. AI anxiety is affected by multiple dimensions, including fears of skill obsolescence, sociotechnical blindness, and apprehension toward humanoid AI systems (Wang & Wang, 2019).

In this context, emotion regulation emerges as a key psychological mechanism that may moderate the relationship between AI readiness and AI anxiety. Emotion regulation refers to the processes individuals use to monitor, assess, and modify emotional responses in order to achieve their goals (Gross, 1998). Effective emotion regulation enables individuals to cope with challenges, reduce stress, and adapt to technological advancements (Berking & Whitley, 2014). Conversely, difficulties in regulating emotions are linked to anxiety disorders, depression, and negative attitudes toward change (Akkuş & Peker, 2022). Given the rapid evolution of AI in education, the ability to regulate emotions may play a crucial role in mitigating AI anxiety.

Understanding the interplay between pre-service teachers’ AI readiness, AI anxiety and emotion regulation is critical for developing strategies to support future educators in adapting to AI-driven changes in education. By identifying the role of emotion regulation in this process, this research seeks to contribute to a more holistic approach to AI adoption in educational settings to ensure the alignment of technological developments with educators' psychological well-being.

Guided by the preceding notion, this study aims to examine the extent to which pre-service teachers’ readiness to use AI affects their levels of AI anxiety and how difficulties in emotion regulation mediate this relationship. In order to address to this aim, the following research question guides the current study:

1. Do difficulties in emotion regulation variable have a mediator role in the relationship between pre-service teachers' AI readiness and AI anxiety?

This study employs cross-sectional design as it aims to examine the mediator role of difficulties in emotion regulation between AI readiness and AI anxiety. Setting and Participants The sample, reached through convenience sampling, initially consisted of 324 participants; however, 12 outliers were removed, resulting in a final sample of 312 participants, of whom 81.1% were women. Participants’ ages ranged from 17 to 38 years. The majority (86.2%) perceived their socioeconomic status as middle-class. Initial teacher education departments’ representation was as follows: 21.45% from English Language Teacher Education, 19.94% from Guidance and Psychological Counseling, 57.22% from Preschool Teacher Education, 13.90% from Special Education Teacher Education, 14.20% from Primary School Teacher Education, and 7.55% from Primary Mathematics Teacher Education. Measurement Tools Pre-service teachers’ difficulties in emotion regulation levels were measured by the Difficulties in Emotion Regulation Scale-Brief Form (Yiğit & Guzey Yiğit, 2017) with five factors: clarity, goals, impulse, strategies, and non-acceptance. It uses a 5-point Likert scale, where higher scores indicate greater emotion regulation difficulties. The Turkish version demonstrated high reliability (Cronbach's Alpha = 0.92) and strong validity. Cronbach's Alpha was .93 in this study. Pre-service teachers’ AI readiness was measured by the AI Readiness Scale for Preservice Teachers (Özüdoğru & Yıldız Durak, 2024) including 18 items and four subscales: cognition, ability, vision, and ethics in teaching with high internal consistency (Cronbach's Alpha = 0.967), and Cronbach's Alpha was .91 in this study. Pre-service teachers’ AI anxiety was measured by the Artificial Intelligence Anxiety Scale (Terzi, 2020). This scale is evaluated using a 7-point Likert scale and consists of four sub-dimensions and a total of 21 items: learning anxiety, job replacement anxiety, sociotechnical blindness, and ai configuration anxiety. Reliability analyzes indicated that the overall internal consistency of the scale is Cronbach's Alpha = 0.96 and this value is .94 in this study. Data Collection Procedure The data were collected upon receiving university’s ethics committee approval (Report Number: E-45379966-100-143698). Teacher educators in the related departments were informed and asked to distribute the online form to pre-service teachers. They were informed about the research processes, ethical considerations and voluntariness. Data Analysis Descriptive statistics were analyzed through SPSS 26. Structural equation model (SEM) was tested by AMOS 22 as it elaborates the relationships among constructs, and assesses both the structural model and the measurement model (Gefen et al., 2000; Hair et al., 2010).

Conclusions This study examined the mediating role of difficulties in emotion regulation in the relationship between pre-service teachers’ AI readiness and AI anxiety. The results indicated that challenges in emotion regulation were linked to increased levels of AI anxiety, while a higher level of AI readiness was associated with lower AI anxiety. Furthermore, the analysis revealed that AI readiness not only directly affected AI anxiety but also affected it indirectly through its impact on emotion regulation difficulties. These findings highlight the crucial role of individual emotional factors in the adoption and integration of AI technologies in educational settings by pre-service teachers. Although technical competence for AI can reduce anxiety, pre-service teachers who struggle with regulating their emotions are more likely to experience heightened AI anxiety. This suggests that teacher education programs should extend beyond the development of AI competency and also include strategies for enhancing emotional regulation skills. Given the growing influence of AI in education, fostering both cognitive and emotional readiness is essential for cultivating a resilient and adaptive teaching workforce. In addition, aligning teacher preparation with European educational policies can further strengthen digital transformation processes. Emphasizing emotional competence alongside technical skills not only supports pre-service teachers in managing AI anxiety but also contributes to the broader goals of digital education strategies advocated by the European Commission. Future research should continue to explore the complex interplay between technical skills and emotional regulation to provide deeper insights that inform both educational practices and policies.

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