Generative artificial intelligence in teacher training: a narrative scoping review CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Generative artificial intelligence in teacher training: a narrative scoping review Andrii O. Kolhatin Kryvyi Rih State Pedagogical University, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine Abstract. The emergence of generative artificial intelligence (GenAI) has transformed various sectors, including education. This narrative scoping review examines how GenAI is being integrated into teacher training programs, exploring its applications, benefits, challenges, and implementation frameworks. By synthesizing findings from recent literature (2022-2025), we identify key themes including the development of AI literacy among teachers, the impact on pedagogical content knowledge, and ethical considerations in implementation. Our analysis reveals significant benefits of GenAI in enhancing teaching performance and facilitating personalized learning, while also highlighting challenges such as technical limitations, ethical concerns, and resistance to change. We identify gaps in current research, particularly in non-STEM subjects and ethical framework development, and suggest directions for future research to advance the responsible integration of GenAI in teacher education. Keywords: generative artificial intelligence, teacher training, teacher education, AI literacy, pedagogical content knowledge, instructional design, professional develop- ment, ethical implications, educational frameworks, pre-service teachers, in-service teachers, AI integration, large language models 1. Introduction The educational landscape is rapidly transforming with the integration of artificial intelligence (AI), particularly generative AI (GenAI) technologies. Since the release of ChatGPT in late 2022, the educational community has witnessed unprecedented interest in how these technologies might reshape teaching and learning processes [8]. Teacher education stands at the forefront of this transformation, as preparing educators to effectively utilize and teach with GenAI becomes increasingly crucial for contemporary education systems. Generative AI refers to artificial intelligence systems capable of creating original content – text, images, code, or other media – based on patterns learned from extensive datasets [11]. Tools like ChatGPT, DALL-E, and Midjourney represent the current generation of these technologies, characterized by their ability to generate human- like responses and creative outputs. As these tools become increasingly accessible, their potential applications in education have expanded dramatically, prompting both enthusiasm and concern among educators [3]. Teacher training programs worldwide are grappling with how to incorporate these emerging technologies into their curricula, ensuring that future educators are not only proficient in using GenAI tools but also capable of teaching their students to engage with these technologies critically and responsibly [36]. The rapid pace of technological advancement presents both opportunities and challenges for teacher education institutions, demanding thoughtful consideration of how best to prepare educators for an AI-augmented educational future. � 0000-0002-3125-3137 (A. O. Kolhatin) # kolhatin.a@gmail.com (A. O. Kolhatin) CTE Workshop Proceedings © Copyright for this article by its authors, published by the Academy of Cognitive and Natural Sciences. This is an Open Access article distributed under the terms of the Creative Commons License Attribution 4.0 International (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1 https://doi.org/10.55056/cte.920 https://orcid.org/0000-0002-3125-3137 mailto:kolhatin.a@gmail.com https://acnsci.org/cte https://creativecommons.org/licenses/by/4.0/deed.en https://acnsci.org CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 This narrative scoping review seeks to map the current landscape of GenAI in- tegration in teacher training, exploring key applications, benefits, challenges, and implementation frameworks. By synthesizing findings from recent literature, we aim to provide a comprehensive overview of this evolving field and identify gaps that warrant further investigation. Our analysis is guided by the following research questions: 1. What are the current applications of GenAI tools in pre-service and in-service teacher training programs? 2. What benefits and challenges are associated with incorporating GenAI into teacher training? 3. What frameworks or models have been developed to guide the integration of GenAI in teacher education? 4. What gaps exist in current implementation and research? 2. Methodology This narrative scoping review follows a structured approach to identify, select, and synthesize relevant literature on generative AI in teacher training. Unlike systematic reviews with rigid inclusion criteria, narrative scoping reviews offer flexibility in exploring emerging topics while maintaining methodological rigor [39]. 2.1. Search strategy and selection process We conducted a comprehensive search of the literature published between November 2022 (coinciding with the public release of ChatGPT) and March 2025. Primary databases searched included Scopus and Web of Science. Search terms included combinations of “generative AI”, “ChatGPT”, “large language models”, “teacher training”, “teacher education”, “professional development”, and related terms. Articles were selected based on their relevance to the integration of generative AI in pre-service or in-service teacher education. We included empirical studies, theoretical papers, reviews, and case studies that substantially addressed how GenAI technologies are being or could be integrated into teacher education. While not following the strict PRISMA protocol, we documented our search process to ensure transparency and replicability. 2.2. Data extraction and synthesis From each included publication, we extracted information regarding: • study characteristics (type, methodology, context); • GenAI technologies examined; • teacher education context (pre-service, in-service, subject areas); • implementation approaches and frameworks; • reported benefits and challenges; • ethical considerations. Data were analyzed thematically, identifying key patterns, trends, and gaps in the literature. The narrative synthesis focused on mapping the current landscape of GenAI in teacher education rather than evaluating the quality of individual studies or conducting meta-analyses of outcomes. 3. Current applications of GenAI in teacher training The integration of generative AI technologies in teacher training reflects diverse approaches across educational contexts. Our analysis reveals several key applica- tions that are reshaping how teachers are prepared for increasingly AI-influenced educational environments. 2 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 3.1. Pre-Service teacher education Pre-service teacher education programs are incorporating GenAI tools to enhance various aspects of teacher preparation. Blonder, Feldman-Maggor and Rap [6] found that GenAI tools are being used to evaluate and enhance pedagogical content knowl- edge (PCK) among pre-service science teachers. Through interactive dialogues with GenAI systems, pre-service teachers engage in lesson planning activities that reveal their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. The CONALI Ontology framework, examined by Lombardi et al. [20], demonstrates how structured ontological approaches combined with ChatGPT can support instruc- tional design processes among pre-service teachers. In their study of 110 students enrolled in a Primary Education Sciences Laboratory, they found that this combina- tion helped future educators articulate SMART objectives, resulting in clearer, more focused instructional design. The integration of ChatGPT significantly improved the efficiency and creativity of the design process, enabling students to quickly generate ideas and refine their projects. Beyond specific applications, Moorhouse et al. [24] explored the development of professional GenAI competence among pre-service language teachers through an 11-week intervention course. Their findings revealed improvements in participants’ pedagogical competence and critical awareness of GenAI tools deployment, though they noted less evidence of development in teachers’ capacity to guide their future students in using these tools effectively and responsibly. 3.2. In-Service teacher professional development For practicing educators, GenAI tools are being integrated into professional de- velopment initiatives that aim to enhance teaching practices and address evolving classroom needs. Mulyani et al. [26] demonstrated that GenAI significantly enhances teaching performance by improving ease of use, usefulness, and learning. Their study of 466 teachers found that teacher perceptions of AI’s usability influence its integration into student-focused learning, learning material development, and teaching practice enhancement. Project-based training models, such as those examined by Xie et al. [41], show promise in helping in-service teachers develop practical AI integration skills. Their design-based research on a project-based training model for primary and secondary school teachers found that GenAI-empowered approaches improved training perfor- mance and teachers’ capabilities to implement AI-enhanced instruction. Laak and Aru [19] conducted a nationwide survey among Estonian K-12 teachers, finding that 49% had already modified their teaching processes in response to GenAI by including tasks that encourage critical thinking, eliminating written homework, and allowing the use of GenAI to generate new ideas. Moreover, 74% reported using GenAI to make their work more efficient (e.g., answering emails from parents), highlighting the practical utility of these tools in teachers’ professional lives. 3.3. AI literacy development A common thread across both pre-service and in-service contexts is the focus on developing AI literacy among educators. Macdowell et al. [22] described a self-study approach to investigate professional practices related to GenAI, analyzing curriculum, instruction, and assessment in an upper-level undergraduate course in multimedia design and production. They developed the Student Artificial Intelligence Literacy (SAIL) framework to support equitable and inclusive access to the educational benefits offered by AI, facilitating student AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. 3 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Similarly, Siiman [34] designed a 45-minute training session for pre-service teach- ers to use Microsoft Copilot and identify ways AI can be used to assist with future teaching practices. Their results showed that teachers perceived AI tools as most ben- eficial for lesson planning, creating assessment questions and tasks, creating images, brainstorming and idea generation, creating educational games, and generating and rewriting text. GenAI applications in teacher training Pre-service education PCK develop- ment Instruc- tional design Lesson planning In-service development Teaching enhance- ment Administ- rative tasks Resource creation AI literacy Cognitive skills Ethical understan- ding Technical competence Figure 1: Major applications of generative AI in teacher training. 4. Benefits of incorporating genai in teacher training The integration of generative AI technologies in teacher training programs has yielded several notable benefits, as evidenced by the emerging literature in this field. These benefits span from enhanced teaching performance to the development of specialized AI competencies. 4.1. Enhanced teaching performance and efficiency Research consistently highlights improved teaching performance as a key benefit of GenAI integration in teacher education. Mulyani et al. [26] found that GenAI significantly enhances teaching effectiveness by improving ease of use, usefulness, and learning capabilities. Their study involving 466 teachers demonstrated that 4 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 perceptions of AI’s usability positively influenced its integration into student-focused learning, learning material development, and teaching practice enhancement. The efficiency gains are particularly notable. Laak and Aru [19] reported that 74% of surveyed teachers use GenAI to make their work more efficient, handling administrative tasks such as responding to parent emails and generating feedback for students. This efficiency allows teachers to dedicate more time to high-value instructional activities and personalized student interactions. 4.2. Personalized learning and content creation GenAI tools support the development of personalized learning experiences, an increasingly important aspect of contemporary education. Fortino, Mangione and Pupo [11] analyzed case studies showcasing GenAI applications in education, highlighting tangible benefits such as increased student engagement, improved test scores, and accelerated skill development. The capacity of GenAI to generate diverse educational content tailored to individual student needs represents a significant advancement in teaching capabilities [14]. Kong and Yang [17] proposed a human-centered learning and teaching framework using GenAI for self-regulated learning development. Their case study involving Chinese language writing ability among primary students demonstrated that teachers equipped with GenAI tools and AI literacy could refine their teaching strategies to better equip students to meet future challenges. The 60-hour development program for teachers increased their perceived ability to design AI-integrated courses that enhanced students’ attention, engagement, confidence, and satisfaction. 4.3. AI literacy and professional competence development The development of AI literacy emerges as both a means and an end in teacher education. Macdowell et al. [22] documented how teachers engaged in experiential activities focused on developing AI literacy alongside collaborative assignments to co-author an open-access textbook on teaching with GenAI. Their Student Artificial In- telligence Literacy (SAIL) framework facilitates student AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. Moorhouse et al. [24] introduced the concept of professional GenAI competence (P-GenAI-C) and evaluated an 11-week intervention course aimed at enhancing this competence among pre-service language teachers. Their findings showed significant improvement in participants’ pedagogical competence and critical awareness of GenAI tools, though development was less pronounced in their capacity to guide students in using these tools effectively and responsibly. 4.4. Impact on pedagogical content knowledge and self-efficacy Research suggests that GenAI positively impacts teachers’ pedagogical content knowledge (PCK) and self-efficacy. Blonder, Feldman-Maggor and Rap [6] proposed using GenAI tools to evaluate PCK among pre-service science teachers, finding that interactive dialogues with GenAI revealed teachers’ understanding of content and pedagogy while facilitating the practical application of theoretical knowledge. Lu et al. [21] conducted a study comparing traditional teaching methods with GenAI- assisted teaching skills training among 215 preservice mathematics, science, and computer teachers. They found that scores of teachers in the experimental group were considerably higher than those in the control group, both in teacher self-efficacy and higher-order thinking. This suggests that GenAI can effectively support teachers’ professional development and enhance their confidence in implementing innovative teaching strategies. 5 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Table 1 Key benefits of GenAI integration in teacher training. Benefit category Description Key studies Enhanced teaching performance Improved teaching effectiveness, efficiency in planning and assessment, streamlined administrative tasks Mulyani et al. [26], Laak and Aru [19] Personalized learning Creation of adaptive content, differentiated instruction materials, student-focused resources Fortino, Mangione and Pupo [11], Kong and Yang [17] AI literacy development Building teacher competence in AI tools, frameworks for developing AI understanding Macdowell et al. [22], Moorhouse et al. [24] Pedagogical content knowledge Integration of subject knowledge with teaching methodology, enhanced lesson design Blonder, Feldman-Maggor and Rap [6], Lombardi et al. [20] Teacher self-efficacy Increased confidence in technology integration, higher professional self-assessment Lu et al. [21], Kong, Yang and Hou [18] 5. Challenges and barriers in GenAI implementation Despite the promising benefits, the integration of generative AI in teacher education presents significant challenges that must be addressed to ensure effective and ethical implementation. These challenges span technical, ethical, pedagogical, and equity dimensions. 5.1. Technical challenges The technical infrastructure required to implement GenAI effectively represents a substantial challenge for many teacher education programs. Roy et al. [33] highlighted the need for significant infrastructure and faculty training to effectively implement GenAI in education. Their analysis of postgraduate education in pathology and microbiology revealed that barriers to implementation include technical limitations and the necessity for substantial faculty training. Many educators lack the necessary AI competencies and familiarity with these technologies, which can hinder their integration. Ng, Chan and Lo [28] identified school readiness and teachers’ AI competencies as major challenges in their study of educators in Canada. These findings align with those of Cheah and Kim [9], who found diverse levels of familiarity with GenAI among STEM teachers, with over half lacking user experience despite acknowledging the importance of equipping students with AI-related knowledge and skills. Concerns about data quality and reliability persist as well. Blonder and Feldman- Maggor [5] and Wang and Li [38] documented educators’ apprehension regarding the accuracy and reliability of AI-generated content, which can affect the quality of educational materials and assessments. The uncertainty about the trustworthiness of GenAI outputs creates hesitation among educators, particularly when considering high-stakes educational contexts. 5.2. Ethical concerns Ethical considerations emerge as a significant challenge in implementing GenAI in teacher education. Yu et al. [42] explored ethical dimensions related to the use of GenAI in higher education, focusing on issues such as data privacy, algorithmic 6 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 bias, and intellectual property concerns. Similarly, Tang and Su [35] identified five main ethical implications of using AI models in the classroom: algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct. The potential for GenAI to perpetuate existing biases is a particularly pressing concern. Gabriel [12] examined the complex relationship between GenAI and educa- tional equity, noting that AI systems can perpetuate existing biases, leading to unfair educational outcomes and further marginalizing already disadvantaged groups. Academic integrity concerns also feature prominently in the literature. Gallent- Torres, Zapata-González and Ortego-Hernando [13] analyzed the impact of GenAI in higher education with a focus on ethics and academic integrity, highlighting concerns about the potential for AI-generated plagiarism and the ethical implications of data accuracy. These issues become increasingly complex when considering how teachers should both use these tools themselves and guide their students in responsible use. 5.3. Pedagogical concerns The pedagogical implications of GenAI integration pose challenges for teacher educa- tion. Nadim and Di Fuccio [27] analyzed the potential negative impacts of GenAI on teaching and research, highlighting concerns about diminished critical thinking and negative effects on educational outcomes. Their analysis suggests that overreliance on AI tools could negatively affect students’ critical thinking skills and their ability to engage deeply with material. The evolving role of teachers in an AI-augmented educational environment creates uncertainty. Zhai [43] explored the transformative impact of GenAI on teachers’ roles and agencies in education, noting that teachers may resist adopting AI due to fears of being replaced or concerns about the technology’s impact on their professional roles. This resistance can be particularly pronounced among educators with established teaching approaches. 5.4. Equity and access concerns Digital divide issues represent a significant barrier to equitable GenAI implementa- tion. Ramírez-Montoya, Oliva-Córdova and Patiño [32] identified challenges related to the digital divide and unequal access to technology in their survey of 115 educators working in higher education institutions in Ecuador, Guatemala, and Mexico. Simi- larly, Henadirage and Gunarathne [16] found that in Global South contexts like Sri Lanka, barriers to GenAI integration include the absence of comprehensive policies and guidelines at the university level and unequal access to technology. Gabriel [12] specifically addressed GenAI and educational inequity, analyzing both opportunities and challenges presented by these emerging technologies in educational contexts. Their paper highlights concerns about digital divides, both in terms of access to technology and digital literacy skills, as well as the potential for AI systems to perpetuate existing biases. 6. Frameworks and models for GenAI integration Several frameworks and models have emerged to guide the integration of generative AI in teacher education, providing structured approaches for developing AI literacy and implementing GenAI tools in educational contexts. 6.1. AI literacy frameworks AI literacy frameworks focus on developing educators’ understanding and compe- tence in using AI technologies. Gómez-Rodríguez et al. [15] proposed a comprehensive AI literacy training program designed to develop necessary skills for university teachers 7 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Technical challenges • Infrastructure requirements • Faculty technological competence • Data quality and reliability • System integration issues Ethical concerns • Data privacy and security • Algorithmic bias • Academic integrity • Intellectual property Pedagogical concerns • Impact on critical thinking • Changing teacher roles • Resistance to new approaches • Assessment authenticity Equity and access • Digital divide • Socioeconomic barriers • Language and cultural biases • Global implementation gaps Figure 2: Major challenges in GenAI implementation for teacher training. to integrate AI into their curricula and research. Their program covers the founda- tions of AI to its specific application in teaching, highlighting ethical aspects, critical thinking, and pedagogical integration. Based on frameworks such as DigiComEdu, the program aims to empower teachers, ensuring their preparation to lead the responsible implementation of AI in the classroom and research. The Student Artificial Intelligence Literacy (SAIL) framework, developed by Mac- dowell et al. [22], facilitates AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. This framework emerged from analyzing the curriculum, instruction, and assessment in an upper-level undergraduate course in multimedia design and production, offering a structured approach to developing AI literacy among future educators. Black et al. [4] articulated a framework of seven critical strategies for addressing the urgent need for Educator Preparation Programs (EPPs) to prepare preservice teachers to effectively integrate AI-powered instructional tools and teach this new area of content knowledge in PreK-12 classrooms. Their framework emphasizes the importance of preservice teachers’ critical examination and application of AI, including a focus on equity, ethics, and culturally responsive teaching. 6.2. Pedagogical integration models Several models focus specifically on integrating GenAI into pedagogical practice. Kong and Yang [17] proposed a human-centered learning and teaching framework that uses GenAI tools for self-regulated learning development through domain knowledge learning. Their framework illustrates how GenAI tools can revolutionize educational practices and transform teaching and learning processes to become human-centered. It emphasizes the evolving roles of teachers as skillful facilitators and humanistic storytellers who craft differentiated instructions and develop students’ individualized learning. The LAIK framework, developed by Al-Ali, Tlili and Al-Ali [1], offers a practical ap- proach to integrating GenAI in higher education classrooms. The framework identifies four practical stages: (1) laying the foundation, (2) assembling GenAI-friendly classes, (3) investigating and monitoring, and (4) keeping the teacher informed. It offers a variety of options for practical ways to integrate GenAI technology to support learning in the classroom. McDermott and Stager [23] examined how GenAI could be integrated into the ELEVATE framework, which was originally developed for designing eXtended Reality (XR) training experiences. This adapted framework incorporates learning theories from 8 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 behaviorism, cognitivism, and constructivism into a cohesive approach based on the Dreyfus and Dreyfus skill acquisition model and Bloom’s Revised Taxonomy, offering guidance for developing appropriate expectations and forms of instruction for students at different proficiency levels. 6.3. Ethical and policy frameworks Recognizing the ethical implications of GenAI, several frameworks focus on guiding responsible implementation. Paschal and Melly [30] conducted a critical analysis and synthesis of relevant literature on ethical guidelines for using AI in education, discussing approaches to ensure effective and efficient use of AI in education. Their work calls for institutions to establish clear policies and frameworks that align with ethical guidelines and incorporate them into decision-making processes. The AI Ecological Education Policy Framework, described by Cacho [8], provides guidelines for incorporating GenAI into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy. This framework offers a suggestive reference for faculty and students to integrate GenAI into their coursework, with a focus on ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Mouta, Pinto-Llorente and Torrecilla-Sánchez [25] explored the ethical dimensions surrounding the utilization of AI technologies in education, conducting a systematic literature review to analyze various applications and objectives, with a particular focus on pinpointing inherent shortcomings within the existing literature. Their work discusses how cultural differences, inclusion, and emotions have been addressed in AI education contexts and explores capacity-building efforts and guidelines for the ethical use of these systems. 6.4. Teacher role and agency frameworks Some frameworks focus specifically on how GenAI transforms teachers’ roles and agencies. Zhai [43] proposed a comprehensive framework that addresses teachers’ perceptions, knowledge, acceptance, and practices of GenAI. Their framework catego- rizes teachers into four roles – Observer, Adopter, Collaborator, and Innovator – each representing different levels of GenAI engagement and outlining teachers’ agencies in GenAI classrooms. This approach highlights the need for quality teacher education programs, continuous professional development, and institutional support to help teachers evolve from basic GenAI users to co-creators of knowledge alongside GenAI systems. Xie et al. [41] constructed a project-based training model for primary and secondary school teachers empowered by GenAI. Their model, developed through design-based research, aims to improve training performance and capability through a structured approach to GenAI integration. The model emphasizes practical application of GenAI tools in educational contexts, highlighting the importance of hands-on experience in developing teachers’ AI competencies. 7. Gaps in current implementation and research Despite growing interest in generative AI applications for teacher education, our analysis reveals several notable gaps in current implementation and research that warrant further attention. 7.1. Underrepresented subject areas and teaching competencies Research on GenAI in education has predominantly focused on STEM fields, with less attention to humanities, social sciences, and other non-STEM disciplines. Wu and Zhang [40] noted this disparity, highlighting the need for more research on GenAI 9 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Table 2 Frameworks for GenAI integration in teacher training. Framework type Key components Representative models AI Literacy frameworks Developing understanding of AI technologies, ethical awareness, critical evaluation of AI outputs SAIL Framework [22], AI Literacy Training Program [15] Pedagogical integration models Instructional design with AI, classroom implementation strategies, assessment approaches Human-Centered Learning Framework [17], LAIK Framework [1], ELEVATE Framework [23] Ethical and policy frameworks Guidelines for responsible use, data privacy considerations, equity and access principles AI Ecological Education Policy Framework [8], Ethical Guidelines [30] Teacher Role and agency frameworks Evolving teacher identities, professional development pathways, implementation stages Teacher Roles Framework [43], Project-Based Training Model [41] applications in non-STEM higher education contexts. This gap is significant because GenAI tools may offer unique possibilities and challenges when applied to subjects that emphasize interpretation, argumentation, and creative expression. Ethical and moral implications of GenAI use in education represent another under- explored area. While several studies acknowledge ethical concerns [3, 5], fewer offer substantive frameworks or guidelines for addressing these issues in teacher education contexts. This gap is particularly concerning given the rapid adoption of GenAI tools and their potential to influence educational values and practices. Teacher training specifically focused on effective GenAI integration also remains underdeveloped. Lombardi et al. [20] noted “a paucity of training courses for educators in the use of AI systems” despite the increasingly crucial role of AI in education. This gap between technological advancement and teacher preparation creates challenges for effective implementation. 7.2. Methodological limitations in current research Current research on GenAI in teacher education exhibits several methodological lim- itations. Case studies represent the most common approach [10], providing valuable insights into specific implementations but limiting generalizability across contexts. While valuable for exploring emerging practices, case studies alone cannot provide comprehensive evidence of effectiveness or transferability. Mixed-methods research combining qualitative and quantitative approaches offers promising insights [2, 7], but these studies often involve small samples or specific institutional contexts, limiting broader applicability. Moreover, few studies employ experimental or quasi-experimental designs that could establish causal relationships between GenAI integration and educational outcomes. Pishtari et al. [31] conducted one of the few experimental studies, implementing an ABAB reversal design to investigate the impact of an AI-driven feedback system on teachers’ learning designs. Such methodologically rigorous approaches remain rare in the literature, highlighting the need for more robust research designs. 7.3. International and contextual variations Research on GenAI in education exhibits significant geographical disparities, with studies predominantly originating from developed countries such as the United States, the United Kingdom, and Australia [37]. This imbalance creates gaps in understanding 10 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 how GenAI implementation might vary across different educational systems and cultural contexts. Henadirage and Gunarathne [16] highlighted the scarcity of research on GenAI imple- mentation in Global South contexts, particularly South Asian countries. Their study of barriers to GenAI adoption in Sri Lankan higher education revealed unique challenges, including the absence of comprehensive policies and guidelines, uncertainty about information reliability, and resistance to technological advancement. Cultural and institutional readiness for GenAI varies significantly across contexts. Alammari [2] found that approximately half of surveyed educators in Saudi Arabia were at stages characterized by understanding and familiarity with GenAI integration, indi- cating tangible readiness for adoption. However, such readiness cannot be assumed across all educational contexts, particularly in regions with limited technological infrastructure or different educational priorities. 7.4. Emerging research directions Several emerging areas warrant further investigation as the field of GenAI in teacher education evolves. The long-term impact of GenAI integration on teacher identity, autonomy, and professional development trajectories represents an important research direction. Nadim and Di Fuccio [27] raised concerns about GenAI’s potential to diminish critical thinking and creativity, highlighting the need for longitudinal studies examining how teacher-AI relationships evolve over time. The development of context-specific frameworks for GenAI integration in teacher edu- cation also represents a promising research direction. While existing frameworks offer valuable guidance [1, 17], they may not adequately address the unique characteristics and challenges of diverse educational systems and cultural contexts. Frameworks tailored to specific educational environments could enhance implementation effective- ness. The intersection of GenAI literacy and specific subject pedagogies represents another underdeveloped area. While general AI literacy frameworks exist [22], research on how these frameworks interact with subject-specific pedagogical knowledge remains limited. Understanding how AI literacy development relates to subject teaching could enhance the relevance and effectiveness of teacher preparation. 8. Discussion and implications The integration of generative AI in teacher training represents a transformative shift in how educators are prepared for increasingly AI-influenced educational environments. Our review reveals several key insights and implications for teacher education policy, practice, and research. 8.1. Balancing technological innovation and pedagogical foundations The effective integration of GenAI in teacher education requires a careful balance between technological innovation and sound pedagogical foundations. While GenAI tools offer significant potential to enhance teaching efficiency, content creation, and personalized learning [17, 26], they must complement rather than replace core peda- gogical principles. Teacher education programs must avoid what Nadim and Di Fuccio [27] term “unquestioning adoption”, which risks diminishing critical thinking and creativity. The frameworks reviewed in this paper, particularly the human-centered learning framework [17] and the LAIK framework [1], demonstrate promising approaches to achieving this balance. These frameworks prioritize human agency and pedagogical purpose while leveraging GenAI capabilities to enhance teaching and learning. Future teacher education initiatives should similarly emphasize how GenAI tools can serve 11 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 Research gaps Subject areas Humani- ties Arts education Language learning Methodo- logical Experi- mental design Longitu- dinal studies Large- scale implemen- tation Contextual Global South Rural settings Low- resource environ- ments Emerging needs Teacher identity Subject- specific pedagogies Long- term impacts Figure 3: Key gaps in current GenAI research and implementation. pedagogical goals rather than allowing technological capabilities to dictate educational practices. 8.2. Developing comprehensive AI literacy AI literacy emerges as a crucial component of teacher preparation in the GenAI era. Beyond technical competence, comprehensive AI literacy encompasses critical evalua- tion, ethical understanding, and pedagogical application of AI technologies. The SAIL framework [22] and the AI literacy training program described by Gómez-Rodríguez et al. [15] offer valuable approaches to developing these multifaceted competencies. Teacher education programs should consider AI literacy not as a standalone skill but as an integrated aspect of pedagogical content knowledge. Ning et al. [29] ex- plored the relationship between various knowledge elements in teachers’ AI-TPACK (Artificial Intelligence – Technological Pedagogical Content Knowledge), finding that AI-Technological Knowledge interacts with other knowledge domains to influence teachers’ overall competence. This suggests that AI literacy development should be integrated across the teacher education curriculum rather than isolated in specialized courses. 8.3. Addressing ethical implications proactively The ethical implications of GenAI in education require proactive attention in teacher preparation programs. Tang and Su [35] identified five main ethical implications of 12 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 AI in education: algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct. Similarly, Blonder and Feldman-Maggor [5] emphasized the need for comprehensive teacher training to effectively and ethically employ GenAI in educational practices. Teacher education programs should incorporate explicit ethical frameworks and guidelines for responsible GenAI use, similar to those proposed by Paschal and Melly [30]. These frameworks should address not only technical ethical issues like data privacy and algorithmic bias but also broader educational ethics concerns such as academic integrity, student autonomy, and equitable access. Moreover, teacher candidates should be prepared to guide their future students in navigating these ethical considerations when using GenAI tools. 8.4. Ensuring equitable implementation Addressing equity concerns in GenAI implementation represents a significant chal- lenge for teacher education. The digital divide, both in terms of access to technology and digital literacy, poses barriers to equitable GenAI integration [12, 32]. Teacher education programs must prepare future educators to recognize and address these dis- parities, ensuring that GenAI implementation does not exacerbate existing educational inequities. Ng, Chan and Lo [28] suggested several strategies to motivate GenAI integration in education, including professional development, clear guidelines, and access to AI software and technical support. These strategies should be adapted to address equity concerns specifically, with teacher education programs emphasizing approaches to GenAI implementation that can bridge rather than widen opportunity gaps. This might include preparing teachers to implement GenAI in low-resource environments, addressing language and cultural biases in AI systems, and developing alternative approaches for contexts with limited technological infrastructure. 8.5. Implications for research Our review highlights several implications for future research on GenAI in teacher education. First, more research is needed on GenAI applications in non-STEM subjects, exploring how these tools can enhance teaching and learning in humanities, social sciences, arts, and language education. Second, methodologically rigorous studies, including experimental and longitudinal designs, are necessary to establish causal relationships between GenAI integration and educational outcomes. Third, research should explore GenAI implementation across diverse educational contexts, particularly in Global South regions and low-resource environments. Additionally, research should investigate the long-term impacts of GenAI on teacher identity, autonomy, and professional development. As Zhai [43] suggested, teachers may evolve through various roles in relation to GenAI – from Observer to Innovator – with implications for how teacher education programs prepare educators for these evolving relationships with AI technologies. 9. Conclusion This narrative scoping review has examined the landscape of generative AI integra- tion in teacher training, exploring applications, benefits, challenges, and implementa- tion frameworks. Our analysis reveals a field in rapid development, with promising approaches emerging alongside significant gaps and challenges. Generative AI offers substantial benefits for teacher education, including enhanced teaching performance, personalized learning capabilities, AI literacy development, and positive impacts on pedagogical content knowledge and self-efficacy. However, these benefits are accompanied by technical, ethical, pedagogical, and equity challenges 13 https://doi.org/10.55056/cte.920 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 that must be thoughtfully addressed. Various frameworks have been developed to guide GenAI integration, focusing on AI literacy, pedagogical implementation, ethical considerations, and teacher roles. Despite growing interest in this area, significant gaps remain in current research and implementation. These include underrepresented subject areas, methodological limitations, contextual variations, and emerging research needs related to teacher identity and long-term impacts. Addressing these gaps will require collaborative efforts among researchers, teacher educators, policymakers, and technology developers. As generative AI technologies continue to evolve, teacher education must adapt to prepare educators who can leverage these tools effectively, critically, and ethically. This preparation should balance technological innovation with sound pedagogical foundations, develop comprehensive AI literacy, address ethical implications proac- tively, and ensure equitable implementation. By addressing these considerations, teacher education can help shape an educational future where generative AI enhances rather than diminishes human teaching and learning. Declaration on generative AI: During the preparation of this work, the authors used Claude 3.7 Sonnet to improve writing style. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the publication’s content. References [1] Al-Ali, S., Tlili, A. and Al-Ali, A.R., 2024. LAIK your classroom: A practical framework to integrate generative AI in higher education classrooms. Journal of Applied Learning and Teaching, 7(2), pp.61–76. Available from: https://doi.org/ 10.37074/jalt.2024.7.2.30. [2] Alammari, A., 2024. Evaluating generative AI integration in Saudi Arabian educa- tion: a mixed-methods study. PeerJ Computer Science, 10, p.e1879. Available from: https://doi.org/10.7717/peerj-cs.1879. [3] Alasadi, E.A. and Baiz, C.R., 2023. Generative AI in Education and Research: Opportunities, Concerns, and Solutions. Journal of Chemical Education, 100(8), pp.2965–2971. Available from: https://doi.org/10.1021/acs.jchemed.3c00323. [4] Black, N.B., George, S., Eguchi, A., Dempsey, J.C., Langran, E., Fraga, L., Brunvand, S. and Howard, N., 2024. A Framework for Approaching AI Education in Educator Preparation Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), pp.23069–23077. Available from: https://doi.org/10.1609/ aaai.v38i21.30351. [5] Blonder, R. and Feldman-Maggor, Y., 2024. AI for chemistry teaching: Responsible AI and ethical considerations. Chemistry Teacher International, 6(4), pp.385–395. Available from: https://doi.org/10.1515/cti-2024-0014. [6] Blonder, R., Feldman-Maggor, Y. and Rap, S., 2024. Are They Ready to Teach? Generative AI as a Means to Uncover Pre-Service Science Teachers’ PCK and Enhance Their Preparation Program. Journal of Science Education and Technology. Available from: https://doi.org/10.1007/s10956-024-10180-2. [7] Burbano G., D.C. and Ibarra C., J.F., 2024. The Role of Generative Artificial Intelligence in Educational Innovation. In: M.F. Mata-Rivera, R. Zagal-Flores and C. Barria-Huidobro, eds. Telematics and Computing. Cham: Springer Nature Switzerland, Communications in Computer and Information Science, vol. 2250, pp.283–297. Available from: https://doi.org/10.1007/978-3-031-77293-1_20. [8] Cacho, R.M., 2024. Integrating Generative AI in University Teaching and Learning: A Model for Balanced Guidelines. Online Learning Journal, 28(3), pp.55–81. Available from: https://doi.org/10.24059/olj.v28i3.4508. [9] Cheah, Y.H. and Kim, J., 2025. STEM teachers’ perceptions, familiarity, and 14 https://doi.org/10.55056/cte.920 https://doi.org/10.37074/jalt.2024.7.2.30 https://doi.org/10.37074/jalt.2024.7.2.30 https://doi.org/10.7717/peerj-cs.1879 https://doi.org/10.1021/acs.jchemed.3c00323 https://doi.org/10.1609/aaai.v38i21.30351 https://doi.org/10.1609/aaai.v38i21.30351 https://doi.org/10.1515/cti-2024-0014 https://doi.org/10.1007/s10956-024-10180-2 https://doi.org/10.1007/978-3-031-77293-1_20 https://doi.org/10.24059/olj.v28i3.4508 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 support needs for integrating generative artificial intelligence in K-12 education. School Science and Mathematics. Available from: https://doi.org/10.1111/ssm. 18334. [10] Chung, H.H., Chung, F.L., Lin, S.M. and Lan, Y.J., 2023. Tools and Approaches of Generative Artificial Intelligence Used in Education. In: J.L. Shih, A. Kashihara, W. Chen, W. Chen, H. Ogata, R. Baker, B. Chang, S. Dianati, J. Madathil, A.M.F. Yousef, Y. Yang and H. Zarzour, eds. 31st International Conference on Computers in Education, ICCE 2023 - Proceedings. Asia-Pacific Society for Computers in Education, vol. 2, pp.17–26. Available from: https://scholar.lib.ntnu.edu.tw/en/ publications/tools-and-approaches-of-generative-artificial-intelligence-used-i. [11] Fortino, G., Mangione, F. and Pupo, F., 2024. Intersection between generative artificial intelligence and education: A hyphothesis. Journal of Educational, Cultural and Psychological Studies, 2024(30), pp.25–52. Available from: https: //doi.org/10.7358/ecps-2024-030-fort. [12] Gabriel, S., 2024. Generative AI and Educational (In)Equity. Proceedings of the 4th International Conference on AI Research, ICAIR 2024, 4, pp.133–142. Available from: https://doi.org/10.34190/icair.4.1.3153. [13] Gallent-Torres, C., Zapata-González, A. and Ortego-Hernando, J.L., 2023. The im- pact of Generative Artificial Intelligence in higher education: a focus on ethics and academic integrity. RELIEVE - Revista Electronica de Investigacion y Evaluacion Ed- ucativa, 29(2). Available from: https://doi.org/10.30827/RELIEVE.V29I2.29134. [14] Granda, B.S., Inzhivotkina, Y., Apolo, M.F.I. and Fajardo, J.G.U., 2024. Ed- ucational innovation: Exploring the potential of Generative Artificial Intelli- gence in cognitive schema building. Edutec, (89), pp.44–63. Available from: https://doi.org/10.21556/edutec.2024.89.3251. [15] Gómez-Rodríguez, V.G., Avello-Martínez, R., Gajderowicz, T., Álvarez, N.B.D., Jara, J.I.E., Hernández, N.B., Hevia, S.G. and Iturburu Salvador, D.D., 2024. Assessment of three strategies for teaching an AI literacy program, based on a neutrosophic 2-tuple linguistic model hybridized with the ARAS method. Neutro- sophic Sets and Systems, 70, pp.378–388. Available from: https://doi.org/10. 5281/zenodo.13182404. [16] Henadirage, A. and Gunarathne, N., 2025. Barriers to and Opportunities for the Adoption of Generative Artificial Intelligence in Higher Education in the Global South: Insights from Sri Lanka. International Journal of Artificial Intelli- gence in Education, 35(1), pp.245–281. Available from: https://doi.org/10.1007/ s40593-024-00439-5. [17] Kong, S.C. and Yang, Y., 2024. A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K-12 Settings. IEEE Transactions on Learning Technologies, 17, pp.1588–1599. Available from: https: //doi.org/10.1109/TLT.2024.3392830. [18] Kong, S.C., Yang, Y. and Hou, C., 2024. Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, p.100328. Available from: https://doi.org/10.1016/j.caeai.2024. 100328. [19] Laak, K.J. and Aru, J., 2024. Generative AI in K-12: Opportunities for Learning and Utility for Teachers. In: A.M. Olney, I.A. Chounta, Z. Liu, O.C. Santos and I.I. Bittencourt, eds. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. Cham: Springer Nature Switzerland, Commu- nications in Computer and Information Science, vol. 2150, pp.502–509. Available 15 https://doi.org/10.55056/cte.920 https://doi.org/10.1111/ssm.18334 https://doi.org/10.1111/ssm.18334 https://scholar.lib.ntnu.edu.tw/en/publications/tools-and-approaches-of-generative-artificial-intelligence-used-i https://scholar.lib.ntnu.edu.tw/en/publications/tools-and-approaches-of-generative-artificial-intelligence-used-i https://doi.org/10.7358/ecps-2024-030-fort https://doi.org/10.7358/ecps-2024-030-fort https://doi.org/10.34190/icair.4.1.3153 https://doi.org/10.30827/RELIEVE.V29I2.29134 https://doi.org/10.21556/edutec.2024.89.3251 https://doi.org/10.5281/zenodo.13182404 https://doi.org/10.5281/zenodo.13182404 https://doi.org/10.1007/s40593-024-00439-5 https://doi.org/10.1007/s40593-024-00439-5 https://doi.org/10.1109/TLT.2024.3392830 https://doi.org/10.1109/TLT.2024.3392830 https://doi.org/10.1016/j.caeai.2024.100328 https://doi.org/10.1016/j.caeai.2024.100328 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 from: https://doi.org/10.1007/978-3-031-64315-6_49. [20] Lombardi, D., Traetta, L., Maffei, A. and Podrzaj, P., 2024. Enhancing Instruc- tional Design: The Impact of CONALI Ontology and ChatGPT in Primary Education Training. In: D. Taibi, D. Schicchi, M. Temperini, C. Limongelli and G. Casalino, eds. Proceedings of the Second International Workshop on Artificial INtelligent Systems in Education co-located with 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024), Bolzano, Italy, November 26, 2024. CEUR-WS.org, CEUR Workshop Proceedings, vol. 3879. Available from: https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_5.pdf. [21] Lu, J., Zheng, R., Gong, Z. and Xu, H., 2024. Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy. IEEE Transactions on Learning Technologies, 17, pp.1279–1289. Available from: https://doi.org/10.1109/TLT.2024.3369690. [22] Macdowell, P., Moskalyk, K., Korchinski, K. and Morrison, D., 2024. Preparing Educators to Teach and Create With Generative Artificial Intelligence. Canadian Journal of Learning and Technology, 50(4 Special Issue). Available from: https: //doi.org/10.21432/cjlt28606. [23] McDermott, A.F. and Stager, S.J., 2024. Integrating ChatGPT into the ELEVATE- XR Adaptive Instructional Framework. In: R.A. Sottilare and J. Schwarz, eds. Adaptive Instructional Systems. Cham: Springer Nature Switzerland, Lecture Notes in Computer Science, vol. 14727, pp.250–260. Available from: https: //doi.org/10.1007/978-3-031-60609-0_18. [24] Moorhouse, B.L., Wan, Y., Wu, C., Kohnke, L., Ho, T.Y. and Kwong, T., 2024. Developing language teachers’ professional generative AI competence: An inter- vention study in an initial language teacher education course. System, 125, p.103399. Available from: https://doi.org/10.1016/j.system.2024.103399. [25] Mouta, A., Pinto-Llorente, A.M. and Torrecilla-Sánchez, E.M., 2024. Uncovering Blind Spots in Education Ethics: Insights from a Systematic Literature Review on Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 34(3), pp.1166–1205. Available from: https://doi.org/10.1007/ s40593-023-00384-9. [26] Mulyani, H., Istiaq, M.A., Shauki, E.R., Kurniati, F. and Arlinda, H., 2025. Transforming education: exploring the influence of generative AI on teaching performance. Cogent Education, 12(1), p.2448066. Available from: https://doi. org/10.1080/2331186X.2024.2448066. [27] Nadim, M.A. and Di Fuccio, R., 2025. Unveiling the Potential: Artificial Intelli- gence’s Negative Impact on Teaching and Research Considering Ethics in Higher Education. European Journal of Education, 60(1), p.e12929. Available from: https://doi.org/10.1111/ejed.12929. [28] Ng, D.T.K., Chan, E.K.C. and Lo, C.K., 2025. Opportunities, challenges and school strategies for integrating generative AI in education. Computers and Education: Artificial Intelligence, 8, p.100373. Available from: https://doi.org/10.1016/j. caeai.2025.100373. [29] Ning, Y., Zhang, C., Xu, B., Zhou, Y. and Wijaya, T.T., 2024. Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements. Sustainability, 16(3), p.978. Available from: https://doi.org/10.3390/su16030978. [30] Paschal, M.J. and Melly, I.K., 2023. Ethical Guidelines on the Use of AI in Education. In: J. Keengwe, ed. Creative AI Tools and Ethical Implications in Teaching and Learning. Hershey, PA: IGI Global, chap. 13, pp.230–245. Available from: https://doi.org/10.4018/979-8-3693-0205-7.ch013. [31] Pishtari, G., Sarmiento-Márquez, E., Rodríguez-Triana, M.J., Wagner, M. and Ley, T., 2024. Mirror mirror on the wall, what is missing in my pedagogical goals? 16 https://doi.org/10.55056/cte.920 https://doi.org/10.1007/978-3-031-64315-6_49 https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_5.pdf https://doi.org/10.1109/TLT.2024.3369690 https://doi.org/10.21432/cjlt28606 https://doi.org/10.21432/cjlt28606 https://doi.org/10.1007/978-3-031-60609-0_18 https://doi.org/10.1007/978-3-031-60609-0_18 https://doi.org/10.1016/j.system.2024.103399 https://doi.org/10.1007/s40593-023-00384-9 https://doi.org/10.1007/s40593-023-00384-9 https://doi.org/10.1080/2331186X.2024.2448066 https://doi.org/10.1080/2331186X.2024.2448066 https://doi.org/10.1111/ejed.12929 https://doi.org/10.1016/j.caeai.2025.100373 https://doi.org/10.1016/j.caeai.2025.100373 https://doi.org/10.3390/su16030978 https://doi.org/10.4018/979-8-3693-0205-7.ch013 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 The Impact of an AI-Driven Feedback System on the Quality of Teacher-Created Learning Designs. Proceedings of the 14th Learning Analytics and Knowledge Conference. New York, NY, USA: Association for Computing Machinery, LAK ’24, p.145–156. Available from: https://doi.org/10.1145/3636555.3636862. [32] Ramírez-Montoya, M.S., Oliva-Córdova, L.M. and Patiño, A., 2024. Training Teaching Personnel in Incorporating Generative Artificial Intelligence in Higher Education: A Complex Thinking Approach. In: J.A.d.C. Gonçalves, J.L.S.d.M. Lima, J.P. Coelho, F.J. García-Peñalvo and A. García-Holgado, eds. Proceedings of TEEM 2023. Singapore: Springer Nature Singapore, Lecture Notes in Educational Technology, vol. Part F3283, pp.163–175. Available from: https://doi.org/10. 1007/978-981-97-1814-6_16. [33] Roy, A.D., Dasgupta, S., Roy, R.D., Das, D. and Narayan, K.A., 2024. The use of generative artificial intelligence (AI) in teaching and assessment of postgraduate students in pathology and microbiology. Indian Journal of Microbiology Research, 11(3), pp.140–146. Available from: https://doi.org/10.18231/j.ijmr.2024.027. [34] Siiman, L.A., 2024. AI in Teacher Education: An Introductory Training Session for Pre-service Teachers Involving Microsoft Copilot. In: Y.P. Cheng, M. Pedaste, E. Bardone and Y.M. Huang, eds. Innovative Technologies and Learning. Cham: Springer Nature Switzerland, Lecture Notes in Computer Science, vol. 14786, pp.231–236. Available from: https://doi.org/10.1007/978-3-031-65884-6_24. [35] Tang, L. and Su, Y.S., 2024. Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom: A Systematic Literature Review. Interna- tional Journal of Interactive Multimedia and Artificial Intelligence, 8(5), pp.25–36. Available from: https://doi.org/10.9781/ijimai.2024.02.010. [36] van den Berg, G. and Plessis, E. du, 2023. ChatGPT and Generative AI: Pos- sibilities for Its Contribution to Lesson Planning, Critical Thinking and Open- ness in Teacher Education. Education Sciences, 13(10), p.998. Available from: https://doi.org/10.3390/educsci13100998. [37] Vhatkar, A., Pawar, V. and Chavan, P., 2024. Generative AI in Education: A Bibliometric and Thematic Analysis. 2024 8th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024. Available from: https://doi.org/10.1109/ICCUBEA61740.2024.10774819. [38] Wang, N. and Li, M., 2024. Teachers’ perceptions of the risks and benefits of AI in higher education: A case study of ERNIE Bot. Innovations in Education and Teaching International. Available from: https://doi.org/10.1080/14703297.2024. 2432429. [39] Wang, P., Jing, Y. and Shen, S., 2025. A systematic literature review on the application of generative artificial intelligence (GAI) in teaching within higher education: Instructional contexts, process, and strategies. Internet and Higher Education, 65, p.100996. Available from: https://doi.org/10.1016/j.iheduc.2025. 100996. [40] Wu, T. and Zhang, S.h., 2024. Applications and Implication of Generative AI in Non-STEM Disciplines in Higher Education. In: F. Zhao and D. Miao, eds. AI-generated Content. Singapore: Springer Nature Singapore, Communications in Computer and Information Science, vol. 1946, pp.341–349. Available from: https://doi.org/10.1007/978-981-99-7587-7_29. [41] Xie, Y., Xia, W., Li, C., Qiu, Y. and Chen, W., 2023. The Construction of Project- Based Training Model for Primary and Secondary School Teachers Empowered by Generative AI. Proceedings - 2023 12th International Conference of Educational Innovation through Technology, EITT 2023. pp.113–118. Available from: https: //doi.org/10.1109/EITT61659.2023.00029. [42] Yu, P., Lu, S., Long, Z., Chen, Y., Qian, J. and Shah, Z.A., 2023. Exploring 17 https://doi.org/10.55056/cte.920 https://doi.org/10.1145/3636555.3636862 https://doi.org/10.1007/978-981-97-1814-6_16 https://doi.org/10.1007/978-981-97-1814-6_16 https://doi.org/10.18231/j.ijmr.2024.027 https://doi.org/10.1007/978-3-031-65884-6_24 https://doi.org/10.9781/ijimai.2024.02.010 https://doi.org/10.3390/educsci13100998 https://doi.org/10.1109/ICCUBEA61740.2024.10774819 https://doi.org/10.1080/14703297.2024.2432429 https://doi.org/10.1080/14703297.2024.2432429 https://doi.org/10.1016/j.iheduc.2025.100996 https://doi.org/10.1016/j.iheduc.2025.100996 https://doi.org/10.1007/978-981-99-7587-7_29 https://doi.org/10.1109/EITT61659.2023.00029 https://doi.org/10.1109/EITT61659.2023.00029 CTE Workshop Proceedings, 2025, Vol. 12, pp. 1–18 https://doi.org/10.55056/cte.920 ethical considerations in utilizing generative AI for global knowledge sharing in higher education. In: P. Yu, J. Mulli, Z. Syed and L. Umme, eds. Facilitating Global Collaboration and Knowledge Sharing in Higher Education With Generative AI. Hershey, PA: IGI Global, chap. 1, pp.1–27. Available from: https://doi.org/ 10.4018/979-8-3693-0487-7.ch001. [43] Zhai, X., 2024. Transforming Teachers’ Roles and Agencies in the Era of Generative AI: Perceptions, Acceptance, Knowledge, and Practices. Journal of Science Education and Technology. Available from: https://doi.org/10.1007/ s10956-024-10174-0. 18 https://doi.org/10.55056/cte.920 https://doi.org/10.4018/979-8-3693-0487-7.ch001 https://doi.org/10.4018/979-8-3693-0487-7.ch001 https://doi.org/10.1007/s10956-024-10174-0 https://doi.org/10.1007/s10956-024-10174-0 1 Introduction 2 Methodology 2.1 Search strategy and selection process 2.2 Data extraction and synthesis 3 Current applications of GenAI in teacher training 3.1 Pre-Service teacher education 3.2 In-Service teacher professional development 3.3 AI literacy development 4 Benefits of incorporating genai in teacher training 4.1 Enhanced teaching performance and efficiency 4.2 Personalized learning and content creation 4.3 AI literacy and professional competence development 4.4 Impact on pedagogical content knowledge and self-efficacy 5 Challenges and barriers in GenAI implementation 5.1 Technical challenges 5.2 Ethical concerns 5.3 Pedagogical concerns 5.4 Equity and access concerns 6 Frameworks and models for GenAI integration 6.1 AI literacy frameworks 6.2 Pedagogical integration models 6.3 Ethical and policy frameworks 6.4 Teacher role and agency frameworks 7 Gaps in current implementation and research 7.1 Underrepresented subject areas and teaching competencies 7.2 Methodological limitations in current research 7.3 International and contextual variations 7.4 Emerging research directions 8 Discussion and implications 8.1 Balancing technological innovation and pedagogical foundations 8.2 Developing comprehensive AI literacy 8.3 Addressing ethical implications proactively 8.4 Ensuring equitable implementation 8.5 Implications for research 9 Conclusion