Teacher Educator Professionalism in the Age of AI: Navigating the New Landscape of Quality Education

Abstract

This conceptual chapter discusses how requirements for teacher educator professionalism may be impacted by the integration of Artificial Intelligence (AI) in teacher education. With the aim to continuously facilitate high-quality teacher education, teacher education institutions must evolve in alignment with the rapidly changing landscape of AI and the respective shifting educational needs. Amidst this evolution, we argue that profound AI Literacy and AI-related ethical knowledge constitute two additional and inextricably intertwined knowledge facets of teacher educator professionalism essential for an ethical and effective integration of AI into teaching practices – and thus crucial for high quality teacher education. The paper explores avenues through which these facets of teacher professional competence and quality education can be fostered on the micro, meso and macro levels of institutional education. By consolidating the specific requirements in a framework for teacher educator professionalism in the age of AI, we highlight the necessity for continuous adaptation of teacher education institutions, ongoing multidisciplinary collaboration, and the provision of periodic professional development of educators. Finally, the chapter presents a concrete practical example and future research directions in AI and education with the aim to contribute to the advancement of quality education in the AI era.

1. Introduction

The rapid proliferation of artificial intelligence (AI¹)-powered technologies [4, 5] carries the potential to reshape the foundations of education [6] and to significantly change the demands of the teaching profession [7]. With education’s chief performance mandate to enable future generations for responsible participation in societal, political, cultural and economic processes [8], educational institutions are urged to adapt to the changing requirements of the educational and vocational domains to safeguard high-quality, relevant and fit-for-purpose teaching and research practices [4, 9]. To ensure education continues to optimally prepare forthcoming generations to succeed in their complex futures, curricula need to be aligned with the relevant skills and knowledge that will be relevant in the future [10]. The plethora of frameworks and guidelines that attempt to predict these skills [11, 12] – while not without their challenges [12, 13] – reinforce the importance of inclusive quality education and lifelong learning opportunities to contribute to a progressive and healthy society [14]. In the age of AI, such a society can harness the affordances of AI productively and mitigate its risks and challenges responsibly [15]. To ensure quality education, enhanced students’ learning experience and improved learning outcomes [4], theories and applications of AI thus need to be constructively integrated as essential elements into education [4, 16]. As teachers and teacher educators bridge schools’ and universities’ AI policies and learners’ needs, they play a critical role in fulfilling this task [16]. Accordingly, teacher educators need to acquire the professional competences needed to convey the relevant knowledge related to understanding, using, applying and teaching AI; validating knowledge and information and knowing about fairness, accountability, transparency and ethics related to AI [17, 18, 19].

Part of the responsibility of high-quality teacher education thus involves institutional adaptations and professional development initiatives to enable teacher educators (1) to use AI effectively and ethically in their teaching and research practice (as well as in their everyday lives) and (2) to teach AI skills and knowledge – including AI-related ethical knowledge – to their pre-service teachers to promote the cascading effects of passing on that knowledge to future generations.

This conceptual chapter addresses the evolving demands on teacher educator professionalism and the role of teacher education institutions in the age of AI. Synthesising the conceptual and empirical literature on Artificial Intelligence in Education (AIEd) and teacher professionalism, we propose that AI knowledge and skills and AI-related ethical knowledge, subsumed in the concept of AI Literacy, constitute additional and inextricably intertwined facets of knowledge of (teacher) educator professional competence that are crucial for enabling an ethical and effective integration of AI into education [20]. Based on these elaborations, we propose a framework of ten areas of development at the micro (learners, pre-service and in-service teachers, teacher educators), meso (schools, teacher education institutions, professional development providers, the teaching profession as a system) and macro level of teacher education (educational policy and politics at state, federal, and global levels) that can be seen as driving forces to facilitate avenues for up-to-date teacher professionalisation and high-quality teacher education amidst the AI transformation. Accordingly, we argue that AI Literacy needs to be incorporated in teacher education institutions, teacher education curricula and the conceptualisation of teacher professionalism to ensure equitable and inclusive AIEd practices [20, 21]. We outline the theoretical background, rationale and method for integrating AI Literacy into the conceptualisation of teacher educator professional competence [22], discuss the deduction of each area of development of the framework and present a case study of implementing AI in teacher education based on the developed framework before concluding with considering future empirical research trajectories.

2. Rethinking (teacher) educator professionalism in the age of AI

For future generations to be able to navigate AI-permeated societies responsibly, they will require profound AI-related knowledge [18, 23, 24]. To ensure educational institutions convey this knowledge in their curricula, educators need to become “fluent” in AIEd [21, 25, 26] and develop the respective professional competence to meet the changing demands of their vocation [27]. “Professional competence” is a core constituent of “professionalisation” and “professionalism” [27]. In the context of teacher education, professionalisation is generally understood as the development of professionalism, that is, developing professional competence and “becoming professional” [28]. Professionalism, on the other hand, refers to meeting the requirements of the profession [29]. Professionalisation and professionalism are understood to be both individual (micro level) and collective (micro and meso level) phenomena that are inextricably intertwined [30]. As an individual phenomenon, professionalism describes the extent to which educators’ professional competence is developed to cope with the demands of their profession [31, 32]. As a collective phenomenon, professionalisation refers to the aim of an evidence-based modernisation of the teaching profession through clear task descriptions, the identification of educational goals and standardised professional development obligations [32].

Due to the multiplicity of theoretical models [31] and methodological approaches, the discourse on both concepts is multifaceted [30]. Building on Weinert’s definition of competence [33], the competence-theoretical approach as one of the most prominent approaches in the German-speaking research tradition defines areas of competence and knowledge dimensions that are constitutive for mastering the tasks and challenges of the teaching profession [34]. Subsumed under the term “professional competence,” these competences are conceptualised as profession-related abilities [31] that encompass the latent potential, the process that leads to the decision to act and the performance as the action itself [30]. The present chapter uses Baumert and Kunter’s generic COACTIV model of teachers’ professional competence as its theoretical foundation [22]. According to this model, professional competence is composed of the four aspects: (1) professional knowledge (i.e., knowledge and skills, including its distinct domains of knowledge and respective facets); (2) professional values, beliefs and goals; (3) motivational orientations and (4) professional self-regulation skills. While all aspects of competence are categorically separated, they interact with one another, and it is through their interaction that professionally competent behaviour arises [22]. The following sections of this chapter address professional competence and teacher educator professionalism in the context of AI in teacher education with a particular focus on teacher educators. Based on a review of the affordances of AI that may contribute to, and challenges that may hinder high-quality teacher education during rapid transitions, the subsequent sections investigate whether any of the above aspects of competence require revisiting and adaptation in alignment with the rapidly changing educational needs in an AI-mediated society and whether AI-related knowledge and skills can and should be conceptualised as additional domains or facets of knowledge of any of the model’s aspects of competence [35].

3. AI in education: essentials and perspectives on benefits and challenges

AI is considered a powerful instrument to facilitate new opportunities for instructional design, independent and self-directed learning, educational innovation and educational research [7]. Successfully integrating, applying and teaching AI in education, however, raises profound questions and poses a range of challenges, such as revisiting and realigning research and teaching practices to ensure high-quality education that remains focused on the human learner [36]. This endeavour demands profound evidence-based knowledge about the background, affordances, challenges and prevailing questions related to AIEd. Among others, Bond et al.’s [20] meta-systematic review (i.e., a review of reviews) of AI in higher education provides a solid evidence base. By rigorously analysing a corpus of 66 reviews, they synthesised 12 categories of benefits and 17 categories of challenges of AIEd from a sub-corpus of 31 reviews. Among the 12 identified categories of the former, the top 6 benefits encompass: (1) personalised learning (e.g., customising educational materials to fit individual learners’ needs), (2) greater insight into student understanding (e.g., using machine learning and analytics to classify patterns, model student profiles, identify learning issues or provide customised guidance or adaptive feedback), (3) positive influence on learning outcomes (despite very little empirical evidence of impact), (4) reduced planning and administration time for educators (e.g., streamlining administrative workflows, using AI to facilitate lesson planning or handle student inquiries), (5) greater equity in education (e.g., AI enhancing accessibility to education and expert systems) and (6) precise assessment and feedback (e.g., enhancing the likelihood for timely, objective and error-free grading or monitoring student progress).

Among the 17 identified categories of challenges, the 5 most imminent concerns of AIEd across all 31 reviews include: (1) a lack of ethical consideration, (2) issues related to curriculum development (e.g., disconnection between AI technology and educational systems), (3) infrastructure (e.g., technical, literacy and financial barriers, or access to stable high-speed internet), (4) a lack of educator knowledge (e.g., widespread misconceptions and unawareness of AI, lack of technological skills and knowledge, lack of pedagogical knowledge and pedagogical content knowledge to apply AI [35], or limited time resources to effectively integrate AI into the curriculum) and (5) shifting authority (e.g., misconceptions of AI’s potentials and challenges that could lead to a transfer of authority from professionals to AI systems). While all the above categories may significantly shape how educational institutions appropriate AI [36], we argue that two categories are particularly central and demand more emphasis:

1. AI Literacy: The AI Literacy construct refers to AI-related knowledge and skills. By outlining their importance, the construct addresses questions such as what educators, learners and the public need to know about AI; how entire sectors of society can be trained in these competences; what pedagogy is most suitable to achieve this goal and who carries the responsibilities to initiate, fund and facilitate these processes.

2. Ethical Considerations: AI-related ethical knowledge and skills include, among others, the ability to address risks such as data privacy and unreflective use of information, understanding and uncovering biases in and ignorance regarding AI algorithms or overcoming the digital divide. Prioritising these concerns is necessary to ensure AI’s integration into education is efficient and equitable and that it safeguards individual rights and societal standards.

If a lack of AI Literacy and an absence of AI-related ethical knowledge compound, problematic use and negative consequences of AIEd could propel and limit or reduce the quality of educational programmes. In alignment with Bond et al. [20], both categories are thus pivotal to ensure constructive collaboration with AI. As ethical knowledge about AI is inextricably intertwined with AI Literacy [23], it can be considered a subdimension of the broader AI Literacy construct.

4. AI literacy in education

AI Literacy is generally understood as “a set of competencies that enables [nonexpert] individuals to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool online, at home, and in the workplace” [23]. While the publications on AI Literacy have increased significantly since the early 2020s [19, 25, 37], empirical research in this area is still in its infancy [25] and construct definitions vary [18, 23, 24]. For the purpose of this paper, we use Long and Magerko’s definition of AI Literacy [38]. Their framework outlines 17 competences that make up AI Literacy, spanning from the ability to distinguish between tools that use and do not use AI to identify problems that AI can or cannot solve well, to recognise how computers reason and make decisions, to critically interpret data, or to understand ethical concerns related to AI. With education’s responsibility to prepare individuals for personal and professional success [36], teacher education institutions are, among others, responsible for mitigating the benefits and concerns of education’s AI-transformation [21]. Meeting this performance mandate crucially depends on teacher educators’ readiness [16] and knowledge of AI, including the implications of applying and integrating AI to pedagogy, teaching and assessment [35]. In other words, in order to be able to convey the necessary AI-knowledge to their learners, educators need to become AI-literate [18, 19]. This prerequisite has come to be so prominent that AI Literacy has even been predicted to become as important as literacy in its original sense [18]—the ability to think (at least) in the first language and mathematical language, to use these abilities effectively to cope with professional and private tasks and the ability to read and write [39].

5. AI-related ethical knowledge and skills in education

Ethical concerns related to AI in education are among the most mentioned across the AIEd literature [35, 40, 41, 42, 43] and are inextricably linked with AI Literacy. Among the 17 competences outlined in Long and Magerko’s AI Literacy framework [38], competency 16 relates to ethics. Accordingly, AI-related ethical knowledge and skills refers to the ability to “identify and describe different perspectives on the key ethical issues surrounding AI (i.e., privacy, employment, misinformation, the singularity, ethical decision making, diversity, bias, transparency, accountability)” (p. 7). Further ethical concerns include apprehensions relating to fairness [41], issues relating to data sources and data sourcing (e.g., corporatisation and commodification of data, unethical treatment of staff who source the data), (data) ownership (e.g., increased potential to leak or misuse user data when AIEd systems are developed by for-profit organisations [44]), authorship and copyright (e.g., can AI tools be considered authors and own copyright [45]?), and authority and responsibility (e.g., can AI tools be held accountable and “responsible” for the content they produce [45]?). While ethical codes and policy on the use of AI-generated data [42, 46] and the implementation of AI-powered tools in professional practice [47] have been discussed in disciplines such as law, engineering and social science, the discussion (and scientific research) thereof is largely absent in education [35, 46]. This absence including a lack of a regulated AI ethics policy in education render ethical considerations in AI-permeated education especially pressing [47]. Aside from the need for a regulated AI ethics policy, enabling responsible and ethical use of AI in education will require a move away from closed, corporate-owned and guarded datasets to the generation of large, open datasets so that models appropriate and suitable for education can be developed, trained and validated [36]. Such a move necessitates the need for creating or improving ethical frameworks [48], alongside a deeper understanding of the social implications of AI more broadly. This development involves educating learners and educators about ethical principles, their own ethical behaviour and the ethical use of AI [41]; equipping them with necessary skills and knowledge required to navigate and shape a desirable future [6] and empowering educators to develop and uphold their professional ethics (i.e., professional ethos).

6. Connecting the dots: AI literacy and AI-related ethical knowledge as knowledge facets of professional competence

Despite AI’s pervasive impact, a general lack of understanding about AI, its decision-making algorithms and the role humans play in AI interactions prevails [16, 23, 40, 49]. This lack is coupled with widespread confusion and misunderstanding surrounding AI [50], indicating a gap in teacher educators’ AI-related professional competence that is paramount to be addressed. Indeed, carefully embedding AI in education and fostering teacher educators’ AI Literacy [21] are crucial to empower not only learners but also themselves as professional educators and individuals to leverage AI for creating opportunities rather than incurring opportunity costs [18, 19]. To embed AI Literacy development - with a particular focus on the development of its sub-dimension “AI-related ethical knowledge” - effectively into education, an appropriate competence model is necessary. As mentioned above, the present chapter uses Baumert and Kunter’s COACTIV model of teacher professional competence [22] as its theoretical foundation. Following this model, we propose reconsidering teacher professional competence by locating the AI Literacy construct with its subdimension “AI-related ethical knowledge” within the knowledge domain of “professional knowledge”. We thereby conceptualise AI Literacy with its 17 competences as outlined by Long and Magerko [38] as facets of content knowledge (CK [51]) and, combined with its effective application to pedagogy, as facets of pedagogical content knowledge (PCK [51]). As professional values and beliefs are central functions of professional ethics, that is, teacher professional ethos [52], we situate AI Literacy’s subdimension “AI-related ethical knowledge and skills” additionally as a domain in the professional competence aspect “professional values, beliefs and goals” [22].

In line with the competence-theoretical approach to teacher professionalism [34], these proposed conceptualisations of AI Literacy and AI-related ethical knowledge as knowledge facets include both teacher educators’ latent potential, the process that leads to their decision to act and their performance visible in their actions itself (e.g., selecting suitable AI-tools based on ethical principles, using AI responsibly when planning and conducting lessons, incorporating AI equitably and effectively in pedagogy, using AI for enhanced student engagement and outcome, using AI for assessment and evaluation etc.). This proposal rests on mere theoretical reasoning. To determine whether AI Literacy and AI-related ethical knowledge indeed constitute facets of professional knowledge, and to verify whether they can indeed be situated in Baumert and Kunter’s COACTIV model [22], empirical research and validation is indispensable. For now, we use the above theoretical elaborations as a foundation for reflecting on how these competence facets can be incorporated in institutional teacher education to contribute to a successful integration of AI—and consequently to contribute to safeguarding high-quality teacher education.

7. Establishing a framework for teacher educator professionalisation in the AI era

With teacher professionalism being both an individual (micro level) and collective (micro and meso level) phenomenon, questions regarding the development of teacher professional competence need to also be approached on the level of institutionalised teacher education including its structures, routines and cultures (meso and macro level) [30]. The elaborations on AI Literacy and AI-related ethical knowledge indicate that part of teacher education institutions’ responsibility is to proactively and continuously address both facets by means of a coherent, strategic, holistic and adaptable approach—an approach that constructively intertwines the interrelated individual, micro level (professional development), the collective, organisational meso level (organisational development) and the overarching macro level (politics and policy). In the following sections, we propose a framework that outlines areas of development deducted from the literature to contribute to the development of AI Literacy and AI-related ethical knowledge. The aim of the framework is to thus suggest avenues for enabling a more aligned professionalisation process of teacher educators and teacher education from the perspective of AIEd (see Figure 1).

Figure 1.

Framework for teacher educator professionalisation in the AI era.

Underlying the framework is the need for a continuously growing degree of agility and flexibility in teacher education institutions—organisations known for their systemic resistance to adapt to change [36]—to sustain the fitness for purpose and continued high quality of their study programmes.

7.1 Micro level: teacher educators, in- and pre-service teachers and learners

In the process of teacher professionalisation, individuals develop the knowledge and skills necessary to cope with the demands of their profession [32]. In the age of AI, the demands on teacher professional competence continue to change rapidly. Thus, it is not just individuals but also the teaching profession as a system that need to adapt continuously [32]. This section proposes two areas of development that may contribute to an up-to-date professionalisation process both at the individual as well as the systemic level. We thereby conceptualise the individual level to correspond with the micro level of institutional teacher education and the systemic level to bridge the micro and meso level of institutional education.

7.1.1 Periodic training and professional development

A prerequisite for effective professionalisation for AI is for educators to learn to understand, use, monitor and critically reflect AI [19, 23] and building thereupon to facilitate their teaching and develop their students’ AI Literacy [26]. Both require knowing and using suitable AI technologies for learning (e.g., adaptive learning systems, intelligent learning environments, data analytics or automated scoring and feedback systems) to understand students’ learning progress and needs in an AI-mediated education, to promote equitable personalised learning and the development of evaluative judgement and to foster students’ ethically responsible, critical and constructive engagement with AI [19]. Teacher education institutions thus need to provide regular comprehensive training and professional development opportunities for developing educators’ AI Literacy [21].

Both the development and support of teacher educators in their professionalisation as well as the integration of new technologies into their teaching are an ongoing and long-term tasks. Employing suitable and empirically sound educational models and didactic concepts (e.g., digitality-related pedagogical and content knowledge DPaCK [21, 53] or the Frankfurt triangle model for education in the digitally connected world² [17]) is crucial for achieving these tasks.

7.1.2 Strengthening the professional teacher-community

As a multifaceted and complex process, the professionalisation of educators transcends the individual and constitutes a collective endeavour. As such, strengthening the teaching community as teacher educators further develop their individual and collective professional competence is highly important. This becomes particularly salient in the context of AIEd. In this rapidly evolving domain, the facilitation of effective practices and discourse concerning diverse AI solutions is pivotal for successfully integrating AI into education. Consequently, the establishment of a supportive and collaborative environment is paramount. One way of supporting the collective teacher community is through initiatives such as platforms (online or analogue) to facilitate the knowledge transfer and encouraging sharing experiences as educators navigate the complexities of integrating AI into their teaching practices. This approach not only fosters an environment of continuous learning but also ensures that the educational community remains at the forefront of technological and methodological advancements.

7.2 Meso level: educational institutions

The meso level encompasses schools, teacher education institutions, professional development providers, decision-makers in educational institutions, (educational) companies and the teaching profession as a system. This section presents avenues for promoting the teacher educator professionalisation process both at the level of organisational development as well as the level of teacher education as a system.

7.2.1 Agility as an organisational mindset

For individuals, organisations and policy makers to be able to establish favourable conditions for the development of AI-related professional competence and a successful appropriation of AI in education, stakeholders at all levels need to become increasingly more agile and flexible. This is relevant because the AI-environment is fast-paced, dynamic and unpredictable. As teacher education institutions are particularly known for systemic resistance to change [36], this area of development may present a considerable challenge for institutions to overcome. However, overcoming this resistance to change is considered pivotal for successful integration AIEd [55].

7.2.2 Institutional AI strategy and ethical principles

A unified institutional AI strategy including ethical principles on AI use is essential. The development of such strategy should involve a multidisciplinary team of all stakeholders and provide a clear framework for an ethically responsible use of AI in education [56]. At the same time, the strategy needs to be flexible enough to allow for adapting to rapid changes in the AI and educational landscape. Higher education institutions across a variety of countries have been developing such principles over the past years, and such developments have proliferated since the launch of Open-AI’s Chatbot ChatGPT-3 in November 2022 (e.g., [57]). As institutional strategies may significantly influence the professionalisation of teacher educators [32], it is important that they align with initiatives to aid teacher educator professionalisation and teacher education institution development.

7.2.3 State-of-the-art ICT infrastructure

Maintaining up-to-date ICT infrastructure is vital for the effective use of AI in educational settings [58]. Without the respective infrastructure, AI cannot effectively be appropriated to education and its affordances cannot be efficiently harnessed. This maintenance is highly dependent on and intertwined with stakeholders of the macro level of institutional education, such as educational policy, state funding or political decisions.

7.2.4 Access to high-quality AI tools

Like the need for state-of-the-art ICT infrastructure, it is crucial to ensure equitable access to AI tools across all stakeholders in institutionalised teacher education. This aims to prevent a disparity in learning opportunities and seeks to avoid reproducing inequalities among students from different economic backgrounds.

7.2.5 Provision of resources for learning new paradigms

Institutions should provide resources that enable educators and students to explore and learn knowledge paradigms that may emerge with the AI-transformation of education and society as a whole [58]. In addition to making relevant resources available (e.g., time, targeted further training opportunities, highly qualified support staff and curricular freedom), it is important that an atmosphere of open discourse, innovative spirit and mutual support prevails across the institution.

7.3 Macro level: politics and policy

The macro level of institutional education spans dimensions such as educational policy and politics at state, federal and global levels. This section suggests foundational aspects that are relevant on this level to contribute to the development of professional competence and its cascading effects.

7.3.1 Interdisciplinary collaborative policy development and vision

Institutions must collaborate to update existing policies and develop a clear vision for the (ethical) use of AI in education [20]. Amidst the rapid technological advancements, teacher education institutions need to establish consensus among trans- and interdisciplinary teams (including students) that enables adopting and responding to technological AI-development to provide guidance to all its stakeholders. Furthermore, it is crucial to fund and conduct extensive research into the affordances, limitations, perils and impact on student and teacher outcomes and learning in (teacher) education [56]. This includes the establishment of full-time research positions at higher education institutions to foster and strengthen international collaboration on AI research [56]. Co-developing and sharing “good practices” and experiences across institutions will be crucial for establishing guiding principles for AI Literacy and AI-related ethical knowledge development across all levels of the educational sector.

7.3.2 Policies, ethical guidelines and data sovereignty as foundational documents

Establishing comprehensive ethical guidelines is paramount to a successful appropriation of AI in education. These guidelines should serve as the foundation for all AI-related activities in institutionalised education, ensuring that the selection and application of AI technologies aligns with ethical standards and values.

7.3.3 Focus on equity and inclusion

To support the creation of an ethical foundation for achieving a “Good AI Society” [15, 16], a clear ethical policy and integrity guidelines at the macro level are pivotal [59]. A prerequisite for meeting this requirement is to ensure that the adoption of new technologies does not widen existing disparities in education. Cascading from the macro level to the meso level, equitable access to AI tools and services needs to be provided to all students, pre-service and in-service teachers as well as teacher educators alike.

8. Implementing AI at the St. Gallen University of Teacher Education

In connection to the areas of development outlined in the present framework, this section discusses the St. Gallen University of Teacher Education’s (PHSG) approach to appropriating AI in teacher education as a case study. With the goal to gradually incorporate AI into all its domains, the PHSG has taken a holistic approach by recently developing an institutional AI-Policy (meso level). The policy outlines five key initiatives that encompass the institution’s commitment to (1) continuous assessment of stakeholders’ needs and experiences with AI; (2) the provision of professional AI Literacy-development opportunities; (3) fair and reliable educational assessment including support on aspects such as academic integrity, ethics and legal implications of AI use; (4) the collaborative development of strategies for AI use in academic research and student written assignments and (5) a collaborative approach to appropriating AI in education that complies with guidelines, ensures knowledge transfer and facilitates faculty training.

Targeting the micro level, the PHSG is developing various learning modules on the continuing education platform www.aprendo.ch designed to enhance educators’ digital and AI-related competences [60]. The platform aims to offer around 100 learning modules in the future that facilitate synchronous and asynchronous learning opportunities and target the development of six competence dimensions in relation to digitality and AI [60].

Another development project of the PHSG constitutes www.zitbox.ch [61], an online platform designed to promote discussions among educators, grow the (teacher) educator network and strengthen the teaching community. It facilitates discussions related to the digitalisation and AI-transformation of education beyond individual school units. First, user feedback indicates that the platform is effective in facilitating active communication and networking among educators. With regard to AI, this interaction focuses on discussing and sharing insights about the possibilities and limitations of AI in schools.

Further, to enhance educators’ AI Literacy and empower them to effectively integrate AI into their teaching, the PHSG has developed a multitiered curriculum for its continuing education programmes (meso and micro level). Designed based on the Frankfurt triangle model [17], the curriculum constitutes a theory-based blended-learning approach to AI integration in higher education. It is designed to accommodate a diverse audience, including participants with no or little prior AI experience. The curriculum covers a wide range of topics, including machine processing of natural language, the impact of AI tools on didactic processes, experimentation with AI tools across various disciplines, the influence of AI on assessment and the application of AI in academic writing. The programme starts with a kickoff event to introduce participants to independent learning modules that focus on foundational AI concepts. Subsequent face-to-face sessions prepare participants for incorporating AI tools into their teaching. Participants’ experiences thereof are then discussed and reflected in a follow-up session to reinforce learned concepts. An array of supplementary resources, including webinars, lectures and regular AI roundtable discussions, is designed to enhance participants’ practical skills and knowledge, particularly in the performance dimension of AI-related professional competence. The programme aims to ensure participants not only understand AI’s fundamental concepts and educational applications but also develop expertise in areas like prompt engineering and the critical assessment of AI’s benefits and challenges in university teaching.

9. Future directions: research needs and development trajectories

Even though the use of AI and AI research in education has increased significantly since 2020, empirical literature on the application of AI to and impact of AI on teaching and learning [35, 62, 63], relationships between AI technologies and learning outcomes for students and teachers [35]—also beyond immediate concrete applications [41]—and theoretical concepts of AI in education [5, 25] are still scarce. Thus, the needs for further research in this area are manifold [20]. Most crucially, future research is required to further ethical considerations and attention within AIEd, both as a topic of research as well as a pivotal issue in conducting empirical research [20]. Research into ethical issues connected to bias, storage and use of teaching and learning data, fairness and accountability of AI use in education and AI Literacy development among learners and educators is urgently needed [20]. Closely intertwined with such endeavours are research required efforts into examining challenges associated with data sources, ownership and authorship as well as authority [36]. Furthermore, considerable efforts are required in the development of policy and institutional guidelines to increase the responsiveness of education systems to rapid changes driven by AI [36].

A specific call for more research relates to the current lack of educational frameworks underpinning the development and incorporation of AI tools in educational settings [20]. More research efforts are required in investigating the appropriateness, suitability and effectiveness of AI tools for pedagogical purposes—as well as their impact on learning, cognition, affect and overall competence development [20].

To ensure that the needs of minorities and marginalised people are reflected in AI development [35] and to prevent the potential of proliferating inequity and inequality caused by algorithmic bias, future research needs to involve a broader range of stakeholders across a wider range of disciplines [20].

Finally, to determine the appropriateness and validity of situating AI Literacy and AI-related ethical knowledge as facets of knowledge and aspects of professional competence in Baumert and Kunter’s COACTIV model [22], extensive empirical research needs to be conducted. Similarly, to determine the effectiveness of initiatives to develop AI Literacy and to appropriate AI in education such as the PHSG-approach presented above, empirical investigations are necessary. Both strands would require intervention studies of pre-post designs with experimental and control groups.

10. Conclusion

In this chapter, we have argued that teacher professionalism in the age of AI requires the development of AI Literacy and AI-related ethical knowledge as new knowledge facets of professional competence. Fostering their development in teacher education not only aims to enable educators to become responsible navigators, ethical stewards and competent teachers of AI technology but also seeks to ensure high-quality teacher education that is up-to-date and fit-for-purpose as the AI-mediated landscape evolves. Continuously meeting the performance mandate of education requires a multifaceted and holistic approach that interlaces the micro, meso and macro levels of institutional education. As agents of change, teacher education institutions will need to harmonise the technological with the pedagogical and the ethical with the practical. Based on the above elaborations and identified needs, we proposed a framework for teacher educator professionalisation in the AI era that attempts to support such endeavours. Based on the therein contained areas of development, we advocate for embedding the development of AI Literacy and AI-related ethical knowledge into teacher education’s core curriculum and ongoing professional development programmes. We further suggest establishing structures that promote strengthening the teaching community as teacher educators further develop their individual and collective professional competence. Furthermore, we maintain that promoting the development of ethical frameworks on institutional and educational policy level to sustain an equitable and inclusive use of AI in educational settings, investing in state-of-the-art ICT infrastructure with access to cutting-edge AI tools and encouraging interdisciplinary collaboration constitute central components of teacher education professionalism in the age of AI. Finally, with AI Literacy and AIEd research still being in their infancies, extensive empirical research will be required to investigate the suitability and effects of the above recommendations on quality teacher education. As we move forward, embracing the transformative potential of AI in education with foresight and appropriate knowledge will be crucial for ensuring that the educators of today and tomorrow are empowered to harness the potential of AI.

Conflict of interest

The authors declare no conflict of interest.