Exploring pre-service teachers’ generative AI readiness and behavioral intentions

Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers' viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers' behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers' behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers' perspectives and intentions regarding GenAI technology.

Generative artificial intelligence (GenAI) is advancing rapidly and is increasingly integrated into educational settings. How to effectively leverage GenAI to support instructional design has thus become a critical issue in teacher education. While existing studies have validated the technical potential and functional value of GenAI in instructional design, there remains a notable gap in qualitative investigations into pre-service teachers’ subjective willingness to adopt GenAI and its underlying influencing factors. To address this gap, this present study employed grounded theory to explore the factors that shape pre-service teachers’ intention to use GenAI for instructional design. Semi-structured interviews were conducted with 23 pre-service teachers from Shaanxi Normal University, and the data were analyzed through open coding, axial coding, and selective coding. A theoretical model comprising four major dimensions was developed as follows: (1) technical factors (relative advantage and ease of use), (2) environmental factors (social impact, opinion leader, and facilitating conditions), (3) usage characteristics (purpose of use and method of use), and (4) psychological factors (trust, perceived risk, and a professional self-concept). The findings reveal that pre-service teachers’ intention to use GenAI is not shaped by a single factor but is instead the result of dynamic and interrelated interactions among the four dimensions. This study extends current technology acceptance theories and offers practical insights for the effective integration and promotion of GenAI in instructional design.

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in recent years, fundamentally reshaping traditional pedagogical approaches and educational environments. The objective of this study was to investigate the adoption of AI tools by pre-service mathematics and science teachers in their learning processes, as well as to assess the influence of academic self-efficacy (ASE) on this adoption, framed through the Technology Acceptance Model (TAM). Specifically, this research evaluated the effect of ASE on the acceptance of AI technologies by pre-service mathematics and science teachers and their intentions to utilize these technologies in the future. Data were collected from a sample of 146 pre-service mathematics and 91 pre-service science teachers (N=237) at an educational faculty in a university located in Western Türkiye during the spring semester of 2024. The data collection employed two distinct instruments: the first instrument comprised items from the Academic Self-Efficacy Scale to assess levels of academic self-efficacy, while the second instrument included items adapted from the TAM, TAM2 (Technology Acceptance Model), and UTAUT (Unified Theory of Acceptance and Use of Technology) frameworks. The results of hypothesis testing indicated that pre-service teachers with elevated levels of ASE had a more favorable perception of the usefulness and ease of use of GenAI tools, which in turn positively influenced their intention to adopt AI-based technologies. Furthermore, the study revealed that perceived usefulness and ease of use significantly shaped pre-service teachers' attitudes and behavioral intentions toward AI. When pre-service teachers recognize GenAI as a beneficial learning resource and find it user-friendly, their willingness to engage with it increases. This study posits that ASE is a critical factor in the acceptance of GenAI-based tools among pre-service mathematics and science teachers, thereby affirming the TAM as a relevant and effective model for examining pre-service teachers' potential engagement with AI in educational contexts.

Despite the increasing adoption of Generative Artificial Intelligence (GenAI) in education, there is a lack of comprehensive reviews on how GenAI is being utilized within Pre-Service Teachers (PSTs) in science. This systematic literature review (SLR) aims to address this gap by examining the extent and nature of GenAI integration in future science teachers' preparation programs. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 21 peer-reviewed empirical studies published between 2022 and 2025 were identified and analyzed through qualitative thematic synthesis. The analysis addresses three research questions: 1) the extent to which GenAI is used in the curriculum of PSTs in science education; 2) how PSTs use GenAI tools to develop a deeper understanding of science and develop scientific reasoning; and 3) how PSTs in science education are using GenAI tools to plan and carry out teaching activities. Findings reveal that the integration of GenAI into curricula remains fragmented and often experimental, typically confined to technology-related courses or pilot projects. PSTs primarily utilize GenAI tools for conceptual clarification, hypothesis generation, and self-regulated learning. Furthermore, these tools serve as cognitive partners in designing lesson plans, differentiating instruction, and simulating classroom scenarios. However, the absence of structured pedagogical guidance often leads to superficial use and limited critical evaluation of AI-generated content. This review highlights the transformative potential of GenAI in science education while underscoring the need for institutional frameworks, faculty training, and the development of AI literacy. Future research should focus on how to sustainably integrate GenAI into teacher education to foster scientific reasoning, pedagogical adaptability, and responsible use of technology.

Incorporating technologies with Generative Artificial Intelligence (GenAI) into education requires a shift in teaching methodologies. However, little is known about how pre-service teachers perceive the relevance and challenges of this incorporation, particularly in mathematics education. This study investigates pre-service teachers’ interactions with GenAI, addressing the relevance and challenges of integrating it in mathematics teaching and discussing possible implications for their knowledge and professional development. Specifically, it intends to understand: How does the pre-service teachers’ interaction with GenAI during the design and implementation of teaching activities relate to their professional knowledge? And how does this relation impact the relevance they ascribe to GenAI? In this qualitative and interpretative study, involving seven pre-service mathematics teachers, we analyse the interplay between participants’ knowledge and use of a GenAI (in this case, ChatGPT), guided by KTMT – Knowledge for Teaching Mathematics with Technology model. The main conclusions revealed a landscape characterised by promise and challenge, where GenAI can be a valuable educational tool when used to facilitate discussion and promote critical thinking, highlighting the relevance and development of KTMT. The ability to evaluate and reflect on AI-generated responses can promote professional development, preparing pre-service teachers for an increasing presence of technology in educational environments.

Identifying highly correlated determinants influencing student nurses' behavioral intention of using generative artificial intelligence (Generative AI): A network analysis.

Integrating generative artificial intelligence (GenAI) in pre-service teachers’ education programs offers a transformative opportunity to enhance the pedagogical development of future science educators. This conceptual paper suggests applying the GenAI tool to evaluate pedagogical content knowledge (PCK) among pre-service science teachers. By holding interactive dialogues with GenAI, pre-service teachers engage in lesson planning in a way that reveals their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. Interpretation of these interactions provides insights into teachers-to-be knowledge and skills, enabling personalized learning experiences and targeted program adjustments. The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices. It contributes to the ongoing discourse on technology’s role in teacher preparation programs, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI. The suggested future research directions aim to further investigate the GenAI usage implications in educational contexts.

This study explores the impact of generative artificial intelligence (GenAI) tools and microlearning strategies on the professional development of pre-service language teachers in Hong Kong. Grounded in self-regulated learning (SRL) and self-directed professional development (SP-PD), the research explores how teachers develop technological and pedagogical competencies through flexible, informal learning. Through semi-structured interviews with 14 pre-service teachers, the study identifies four key themes: navigating GenAI with curiosity, microlearning as a practical scaffold, integrating SRL strategies, and reimagining teacher identity and agency. The findings reveal that participants valued the accessibility and responsiveness of GenAI and microlearning but faced cognitive and emotional challenges, particularly without institutional support. Notably, GenAI tools functioned not only as instructional aids but also as co-regulators of learning, facilitating goal-setting, feedback, and reflection. By positioning GenAI as an active agent in teacher learning, this study contributes to the field and advocates for integrating AI literacy, SRL, and ethical frameworks into teacher education. The study proposes reimagining professional development to strike a balance between autonomy and support and between innovation and pedagogical integrity in AI-enhanced educational contexts.

Digital drivers: How the acceptance and use of technology model shapes university students' behavior toward generative AI.

As Artificial Intelligence (AI) systems, including generative AI (GenAI), evolve, they offer new opportunities for information access and feedback across disciplines. However, limited research has examined pre-service teachers’ attitudes towards GenAI, particularly their perceptions, experiences, and usage. This study addresses the gap using a mixed methods approach, combining survey data from 209 Norwegian pre-service teachers with interviews from 11 of the same participants. Most were aware of GenAI and had used AI tools, though none considered themselves experts. Statistical analysis identified key predictors of AI knowledge and beliefs about GenAI’s usefulness in teaching. Participants acknowledged GenAI’s practical benefits for lesson planning and content discovery but raised concerns about its trustworthiness and implications for human-centred education. Interviews offered deeper insight into differences between experienced and novice users. The study highlights implications for teacher education, emphasising the need to develop AI competency to prepare future educators for an increasingly digital landscape.

The rapid advancement of Generative Artificial Intelligence (GAI) technologies has significantly impacted various sectors, including higher education. This study investigates the behavioral intention of students in higher education institutions to adopt GAI and its effect on their academic performance mediating the intention to use GAI. This study uses an analytical cross-sectional design to assess the current relationships among behavioral intention factors, intention to use GAI, and academic performance. Data were collected using a quantitative approach from 279 online and 105 student self-administered responses through purposive sampling. Purposive sampling was employed to target students with GAI experience, ensuring relevant insights aligned with the study’s objective of examining adoption patterns among the active users in higher education settings. The students represent seven higher education institutions that are accredited by the University Grants Commission, Nepal. A seven-point Likert scale measured variables like performance expectancy, effort expectancy, social influence, facilitating conditions, intention to use GAI and academic performance. The final sample size was 384, and pilot testing ensured instrument validity. Data analysis was conducted using SMART Partial Least Square (PLS). SmartPLS was chosen for its capacity to handle complex models, making it suitable for analyzing predictive relationships without requiring the normal data distribution. The results show that all the factors of behaviorual intentions significantly influence the intention to use GAI; however, the effort expectancy and social influence did not influence academic performance. The mediating role of intention to use GAI was also ensured. The findings highlight the need for educational institutions to provide targeted training and clear ethical guidelines for responsible GAI use, emphasizing its integration into curricula to enhance academic performance.

Generative Artificial Intelligence (GenAI) tools are transforming the field of language education. Recent studies indicated that English language teachers generally lacked the readiness and skills necessary to effectively and critically integrate these tools into their professional practices. This study employed a mixed-method interventional design to investigate the impact of integrating GenAI tools into a 12-week training course on the development of pre-service English language teachers’ GenAI literacy. 90 pre-service language teachers enrolled in a B.A. in English program at a university in southeastern China were recruited. The participants were divided into an experimental group (n = 45) and a control group (n = 45). Data was collected through a mixed-methods questionnaire and analyzed using Analysis of Covariance (ANCOVA) and thematic analysis. The results demonstrated a significant improvement in the experimental group’s overall GenAI literacy and its five sub-dimensions following the 12-week intervention. The experimental group significantly outperformed the control group in the post-test, as evidenced by their GenAI literacy across all five sub-dimensions (overall GenAI literacy, p < 0.001; technical proficiency, p < 0.001; critical evaluation, p < 0.001; communication proficiency, p < 0.05; creative application, p < 0.001; ethical competence, p < 0.001). This study provides valuable insights for school leaders and program directors to integrate explicit GenAI literacy training into language teacher education programs. It emphasizes the necessity for language educators to engage in ongoing professional development focused on GenAI, ensuring that they stay updated with the latest GenAI advancements and their applications in language education.

Aim/Purpose: The principal aim of this study was to reveal digital citizenship levels of pre-service teachers enrolled in 1st and 2nd year in the education faculty at the Muğla Sıtkı Koçman University in Muğla, Turkey. Pre-service teachers’ perceptions of digital citizenship and their patterns of knowledge of digital citizenship were explored. Background: This study examines the concepts of digital citizenship, including digital communication, digital rights / responsibilities, critical thinking, digital participation, digital security, digital skills, digital ethics, and digital commerce, of pre-service teachers and their interaction with instructional technology. This research study will inform policies and strategies to enhance teacher trainings and education in Turkey. Methodology: A mixed methodology of data sources including a survey and open-ended questions were collected to explore pre-service teachers’ perceptions of digital citizenship. The Digital Citizenship Scale was used as the quantitative data collection instrument. Various statistical techniques and tests such as ANOVA, t-Test, and Tukey HSD were used in the analysis of the data. Contribution: This study contributes to existing literature knowledge by demonstrating the patterns of digital citizenship that influence Turkish pre-service teachers’ professional development and deepening the discussion of change in policies and strategies in education programs in Turkey. Findings: Results indicated that there was a statistically significant difference of digital citizenship scores of male and female pre-service teachers with male participants scoring higher than female participants. However, participants’ mean scores did not significantly differentiate by their departments. Similarly, it was observed that participants’ mean scores did not significantly differentiate by the high school types. As far as parent educational background was concerned, the mean scores of the participants did not indicate a significant difference by the education level of the mother, but the scores differed significantly by the father’s education level. Similar responses emerged in the open-ended questions. Participants expressed that they felt competent in digital communication and digital participation and their parents are partly influential in the improvement of these skills. In addition, the majority of the participants stated that the major they enrolled did not have any contribution to their digital skills so far. Recommendations for Practitioners: Recommendations for practitioners to include in their teacher education programs training pre-service teachers to become digital citizens. Recommendation for Researchers: Recommendations for researchers to include identifying practical activities that enhance pre-service teachers’ digital citizenship skills. Impact on Society: The findings and results of this study will help display a universal digital citizenship model for pre-service and veteran teachers in Turkey as well as to strengthen their interaction with instructional and information technologies through policy and strategy changes in educational settings. Future Research: Further studies should be undertaken, especially in developing countries. Future research can further explore pre-service teachers’ perceptions of digital citizenship such as digital rights, responsibilities, and ethics as well as evaluating the opinions of school administrators, students, and parents regarding their perceptions of digital citizenship in educational settings.

The increasing integration of generative Artificial Intelligence (AI) tools, such as ChatGPT, in education has prompted growing interest in their pedagogical potential and the emergent competencies required for their effective use in language instruction. While generative AI is beginning to influence language teaching and learning practices, emerging research suggests a growing need to address AI-related literacies and ethical considerations within language teacher education programs. Despite the growing number of studies examining generative AI’s use in language learning contexts, there remains a notable gap in systematically reviewing how generative AI is being addressed in teacher preparation and professional development. To address this gap, this study presents a bibliometric-based systematic literature review of research on generative AI in language teacher education, employing text-mining algorithms, data-mining heuristics, and social network analysis. The findings identify five major thematic clusters in the literature: (1) Professional Development and AI Literacy in Teacher Education, (2) Chatbots and Conversational AI in Language Learning, (3) Generative AI for Instructional Design, Assessment, and Lesson Planning, (4) Generative AI as a Tool for Enhancing EFL Writing Skills, and (5) Exploring Pre-Service Teachers’ Perceptions and Readiness. This review contributes to the growing discourse on AI in education by mapping the current research landscape and identifying critical directions for advancing generative AI integration in language teacher education.

This rapid study explores teacher educators’ perceptions of generative artificial intelligence (GenAI) in teacher education, conducted through a descriptive survey involving 55 teacher educators from two colleges of education in Ghana. A convenience sampling technique was adopted for data collection, and a data analysis using <i>exploratory factor analysis</i> was used to identify primary factors shaping perceptions and preparedness of GenAI integration. Key findings reveal a generally positive perception among the teacher educators, who recognize GenAI’s potential to support academic achievement, increase student engagement, and improve communication within teacher education settings. The findings further indicate that the teacher educators’ background factors, such as age, years of teaching experience, department, and college, do not significantly predict their perceptions of GenAI. Since none of these measured background factors were significant predictors, this suggests that training and resources for using GenAI should be broadly prioritized, accessible, and not heavily tailored to specific demographic groups. However, the study identified significant concerns within the <i>barriers and challenges</i> factors, including ethical issues, fairness in student assessment, and possible adverse effects on the teacher educator-student relationship. The <i>communication and independence</i> factors highlight a need for professional development, with teacher educators emphasizing the importance of training in GenAI usage to optimize its educational potential. The study concludes that while teacher educators generally support GenAI’s potential benefits, there are essential ethical and practical challenges to address. Recommendations include establishing clear policies and guidelines to guide GenAI implementation and ensure ethical usage. We further recommend the expansion of this research with a larger sample to gather comprehensive insights from the teacher educators and their acceptance levels of GenAI.