AI literacy for teacher educators: a holistic curriculum for capacity-building in higher education

Abstract

The emerge of Generative Artificial Intelligence (GenAI) sparked diverse reactions within the educational sector, from enthusiasm to prohibition. The core question is no longer whether educators should use GenAI, but rather what skills, knowledge, and mindset they need to use AI effectively and responsibly. Educational AI literacy focuses on the competencies educators must develop to understand, utilize, and critically evaluate AI in teaching, learning, and assessment. However, fostering educational AI literacy requires more than simply identifying and defining necessary competencies; existing literature does not adequately address providing a comprehensive curriculum for developing AI literacy in educational contexts. Drawing on an integration of conceptual theory, empirical research, and the author’s extensive experience in faculty development across diverse international contexts, this conceptual paper introduces a holistic, participatory, and inquiry-driven curriculum framework for developing AI literacy among teacher educators in higher education. The synergy of these three sources of knowledge ensures a theoretically grounded and practically relevant foundation for the framework’s design. Developed through an iterative process of design, implementation, and evaluation, the framework offers structured guidance for creating, embedding, and assessing AI literacy initiatives responsive to the unique needs and institutional contexts of higher education settings. Rather than prescribing a fixed hierarchical sequence, the framework is intentionally designed as a flexible blueprint—an open-ended roadmap, where the curriculum’s depth and breadth can be adapted to an institution’s AI-readiness and its broader mission regarding educators’ expected AI proficiency or mastery. Additionally, the analytical study addresses potential implementation challenges, strategies to mitigate them, and implications for stakeholders.

1 Introduction: AI in education

Digitalization and the advancement of technologies such as Artificial Intelligence (AI) have transformed how we live, learn, and communicate. AI can broadly be defined as a set of computational algorithms capable of performing tasks typically associated with human intelligence, such as pattern recognition, perception, decision-making, learning, and language use (Banh and Strobel, 2023; Winston, 1993). A more functional definition—one that avoids direct comparison with human cognition—frames AI as “automation based on associations,” wherein computers automate reasoning by identifying patterns in data or deriving associations from expert knowledge (U.S. Department of Education, 2023).

Artificial intelligence in Education (AIEd) emerged over 60 years ago, with its primary goals including personalizing learning, enhancing learners’ engagement and retention, automating routine teaching tasks, and streamlining administrative processes. AI-driven technologies—such as intelligent tutoring systems, adaptive learning environments, automated scoring tools, real-time feedback mechanism, learning analytics, and educational data mining—have been widely implemented in educational practices (Caspari-Sadeghi, 2023a, b; Neshaei et al., 2024).

More recently, breakthroughs in Generative Artificial Intelligence (GenAI), particularly in Large Language Models (LLMs), have revolutionized the field of natural language processing (NLP)—a domain of AI focused on understanding and generating human language. AI-powered tools such as ChatGPT, Claude, Dall-E, and Synthesia can mimic human creativity, answer questions, solve mathematical problems, write essays, explain complex concepts, transcribe audio, and produce multimedia content (World Economic Forum, 2024; Yan et al., 2023). The application of LLM in education has produced mixed outcomes, both positive (e.g., enhanced self-regulated learning) and negative (e.g., over-reliance and dependency), depending on the employed learning and pedagogical approach (Bastani et al., 2024; Kasneci et al., 2023; Prihar et al., 2023). Teachers thus play a pivotal mediating role in optimizing the benefits of GenAI while mitigating its potential harms and risks. To harnesses the advantages of GenAI effectively, educators must be equipped with the necessary knowledge and competencies to integrate these technologies in educational settings. Additionally, teachers should be informed of the ethical risks associated with AI-driven educational technologies to ensure their responsible and safe use.

Despite the growing awareness about the importance of AI in education, particularly for teachers and teacher educators, current research and policy initiatives remain underdeveloped in several key areas. First there is a lack of comprehensive, empirically grounded frameworks tailored specifically to the professional development needs of teacher educators in higher education. Second, the current literature pays limited attention to the factors that influence the success and sustainability of such educational initiatives—such as institutional AI readiness, technological infrastructure, human resource capacity, and organizational culture. Third, most existing professional development efforts tend to focus narrowly on the technical mastery of AI tools—often through sandbox environments—without adequately addressing their pedagogical integration or alignment with core educational principles.

This analytical paper aims to address these gaps by proposing a structured yet flexible framework for embedding AI literacy for teacher educators within higher education. The objective is to provide a practical and adaptable model that supports the systematic and sustainable development of AI literacy in educators. Drawing on relevant literature (Ng et al.’s AI literacy framework, 2021), empirical work (e.g., inquiry-based approach to professional development), and the author’s extensive experience—including initiatives focused on teacher assessment literacy, data literacy, and digital competence curricula in international contexts—we propose a framework which highlights sustainability, inclusivity, and co-ownership.

The conceptual study is organized as follows: first, we examine the need for AI literacy in education, reviewing existing theoretical frameworks, conceptualizations, programs and initiatives designed to foster AI literacy. Next, we introduce a holistic, participatory, and inquiry-based approach to designing, implementing, and evaluating a comprehensive AI literacy curriculum tailored to teacher educators. Each phase of the curriculum design is discussed in detail. Finally, we identify key challenges associated with implementing the proposed curriculum and suggest strategies to address them. We conclude with recommendations for future research and practical implications for stakeholders.

2 The need for AI literacy in higher education

“Without universal AI literacy, AI will fail us.”

 —World Economic Forum, 2022.

Traditionally, literacy has been defined as the ability to access, share, and communicate ideas through written language. However, advancements in science, technology, and society expanded the scope of essential literacies required to survive and thrive in rapidly evolving complex world. These now include digital literacy, computational literacy, data literacy, media literacy, information literacy, and digital citizenship (Jacob and Warschauer, 2018; Leu et al., 2013; Njenga, 2018).

Among these, digital and data literacy are fundamental to AI literacy. Digital literacy—the ability to use ICT and digital technologies such as the internet, social media, and mobile for communication, learning, working, or completing a task—is a prerequisite for developing AI literacy (Chiu et al, 2024; Falloon, 2020). Moreover, data literacy can be a significant facilitator of educational AI literacy due to the centrality of data in machine learning (a subfield of AI). It requires the educators to understand, use, and reflect on data-informed decision-making process, e.g., data collection, analysis, visualization, and interpretation, to improve educational outcomes (Mandinach and Gummer, 2016).

Although AI literacy builds on all previous literacies, it goes beyond them due to the unique capacity of AI to mimic human intelligence. Furthermore, the current AI technologies have distinctive features that differentiate them from previous generations of human-made technologies, including (a) autonomous decision-making, (b) self-learning capability, and (c) inscrutability—the inherent lack of explainability and interpretability often associated with neural network architectures and deep learning models— (Berente et al., 2021; Pinski and Benlian, 2024).

AI literacy is a critical competency that must be urgently developed across all sectors of society, including among public and professions. Technological literacy (e.g., AI and big data) is among employers’ top priorities for 2027. It is predicted that by 2030, over 40% of the core skills required for all jobs will change due to technological advancements (World Economic Forum, 2024). The urgency of this transformation is reflected in the UNESCO Sustainable Development Goals (SDGs) which emphasizes education’s role in eradicating poverty and fostering sustainable growth through inclusive, equitable, and lifelong learning opportunities for all. However, the main barrier is the global shortage of qualified teachers capable of preparing students to live, work, and learn in an AI-rich world. This underscores the pressing need for higher education institutions to develop and implement comprehensive curricula that foster AI literacy among both educators and students.

3 AI literacy: theoretical frameworks and programs

Conceptualizing AI literacy is a critical step in guiding curriculum development, instructional design, and assessment. Theoretical definitions and specification of proficiency dimensions directly shape how educators are trained to engage with AI technologies. The first discussion on AI Literacy emerged a decade ago, suggesting that users should have basic knowledge and ability to interact with AI-based platforms, services, and products (Kandlhofer et al., 2016; Konishi, 2015). Since then, interest in defining the components of AI literacy has grown significantly. This section reviews key definitions and theoretical frameworks that outline the core dimensions of AI literacy.

Mills et al. (2024) similarly defined AI literacy as the knowledge and skill required to (a) understand, (b) use, and (c) evaluate AI safely, ethically, and effectively in both personal and professional contexts. Their framework highlights the centrality of human judgement and justice as core values across all components of AI literacy. Furthermore, they distinguish between different modes of engagement with AI: interacting with AI in daily life, using AI to perform job-related tasks, or developing AI systems.

The UNESCO AI Competency Framework for Teachers further advances this conceptual landscape by defining five core dimensions of AI literacy: (a) AI foundations and applications, (b) AI pedagogy, (c) AI ethics, (d) AI for professional development, and (e) human-centred mindset (Miao and Cukurova, 2024). Each dimension has three mastery levels—acquire, deepen, and create— to ensure all teachers, regardless of their disciplinary background or prior knowledge, can progressively develop a foundational level of AI competency for educational practices. Table 1 presents a concise summary of key AI literacy frameworks, highlighting their core components and focus areas.

Taken together, these frameworks consistently position AI literacy as a multidimensional construct, that extend beyond technical knowledge of AI tools to include critical assessment and ethical considerations. While we may use AI literacy and AI competency interchangeably, we acknowledge Chiu’s (2025) distinction that AI literacy refers to understanding what AI does and its limitations, whereas AI competency involves the ability to apply AI effectively. Literacy provides the essential foundation, but competency builds on this knowledge to enable skillful, ethical, and productive use of AI.

Beyond theoretical conceptualizations, numerous institutional, national and international programs have been developed to promote AI literacy in education. Several countries, including China, USA, Australia, South Korea, Japan, and Singapore, have pioneered comprehensive policies and programs to develop AI literacy in education, particularly at K-12 levels.

For example, the AI for Teachers (AI4T) program, an EU initiative delivered through Massive Open Online Courses (MOOCs), consists of four modules: (1) introduction to AI in Education, (2) foundational AI knowledge, (3) applications and technical aspects, and (4) AI ethics. It allows teachers to learn about AI, access and explore AI tools and platforms, experiment with AI applications such as prompt engineering in ChatGPT, and integrate AI in their daily teaching practices. Other notable programs include AI4K12, AI for kids (AI4 K), and AI for Students (Heintz, 2021; Laupichler et al., 2022). At the higher education level, institutions like the University of Florida have introduced initiatives such as AI Across the Curriculum, which aim to equip undergraduate students with AI competencies across all disciplines (Southworth et al., 2023).

All these initiatives and programs highlight the world-wide recognition of AI literacy as an essential competency for educational stakeholders such as teachers and students. Although AI literacy has begun to appear in several national curricula, Sperling et al. (2024) suggest that efforts to systematically integrate it into teacher education programmes are still limited.

While the above frameworks provide foundations for conceptualizing AI literacy, several gaps remain. First, many existing frameworks, e.g., AI4K12, are developed for K–12 education, focusing on schoolteachers and students. Consequently, there is limited conceptual guidance specifically addressing higher education, where AI literacy must also support disciplinary learning and research practices. Second, existing frameworks vary in their focus: some focus mainly on technical understanding of AI systems (e.g., Long and Magerko, 2020), while others highlight ethical, pedagogical, or human-centered dimensions (e.g., Miao and Cukurova, 2024; Mills et al., 2024). This variation reflects an ongoing debate about whether AI literacy should prioritize technical competence, critical understanding, or responsible use in real-world contexts.

These limitations highlight the need for broader conceptual perspectives that extend beyond K–12-oriented frameworks and support the systematic and holistic development of AI literacy in higher education. In response to this gap, the present conceptual article proposes an integrative framework designed to guide the coordinated development of AI literacy across institutional, organizational, and individual levels within universities.

4 AI literacy curriculum for teacher educators

We propose a framework for integrating AI literacy into higher education which is grounded in a triangulated foundation to ensure both theoretical rigor and practical relevance. First, it draws theoretical framework developed by Ng et al. (2021), which was discussed in previous section. Second, it is informed by empirical research on instructional design for faculty development, capturing key challenges and success factors in implementing pedagogical innovation. Third, it is deeply rooted in the author’s decade-long experience leading faculty development programs across a range of international contexts—teacher assessment literacy in Norway, data literacy and digital literacy initiatives in Germany, and evidence-based teaching reform in Iran. This experiential dimension allows the framework to be sensitive to institutional diversity, cultural variation, and systemic constraints.

We propose a participatory, inquiry-based, and system-wide curriculum for integrating AI literacy programs into higher education. This framework serves as an open-ended roadmap or flexible blueprint rather than a prescriptive, hierarchical method. It fosters local agency, cross-disciplinary collaboration, and iterative refinement, making it particularly suited for the dynamic context of higher education.

By participatory, we emphasize the active involvement of key stakeholders—including teacher educators, institutional leaders, instructional designers, and module organizers—throughout the co-design process. Engaging these actors in all phases fosters a deep understanding of the process, increases ownership, and enhances commitment to utilization of resultant findings (Dilek et al., 2025; Patton and Campbell-Patton, 2022). By inquiry-based, we ensure that judgement and decisions regarding program strengths, weaknesses, revisions, improvements, or restructuring are grounded in relevant evidence and systematically collected data. Rooted in constructivist learning theory, inquiry-based learning encourages educators to engage in questioning, exploration, experimentation, and reflection. It supports deeper learning by positioning teacher educators as active co-creators of knowledge (Dobber et al., 2017).

The proposed AI literacy curriculum for teacher educators consists of three interconnected phases—design, implementation, and evaluation—each guided by participatory and inquiry-based principles. These phases reflect the core components of a Design-Based Program, which enables iterative refinement through continuous evidence gathering and critical reflection. Grounded in Design-Based Research (DBR) methodology, this approach integrates theory and practice in real-world educational contexts, allowing for the systematic testing and improvement of pedagogical models. By engaging participants as co-designers and reflective practitioners, the process fosters both contextual relevance and theoretical advancement, ensuring that the curriculum evolves responsively in line with empirical insights and user feedback.

It is important to underscore that the primary goal of our proposed AI literacy program is not to train educators to become AI experts or talents who can code, program, develop, or optimize AI algorithms, models, and solutions. Rather, the training program aims at equipping educators with knowledge and skills necessary to confidently, effectively, and responsibly integrate AI into their teaching and professional practice.

4.1 A. Design phase

The design phase of the AI literacy curriculum involves two key steps: (a) assessing AI readiness and (b) defining core AI competencies for teacher educators.

4.2 B. Specifying AI literacy competencies

After assessing the AI readiness, the next step entails specifying the core AI competencies that teacher educators should develop to meaningfully leverage AI-technologies in educational contexts. These competencies serve as foundational framework, shaping both the content (what should be taught) and the expected learning outcomes (mastery levels).

Building on existing frameworks (e.g., Ng et al., 2021; UNESCO, 2024), we propose that an AI literacy program for teacher educators should encompass three core competencies: (I) foundational knowledge of AI, (II) application or AI-enhanced teaching, and (III) evaluation and AI ethics. Below, we outline the key concepts which should be taught and learned for each of these competency areas.

4.2.1 AI evaluation

Educators should learn to critically assess AI-generated content instead of taking AI outputs at the face value. AI evaluation literacy enables educators to critically reflect on AI outputs by understanding AI limitations such as hallucination (i.e., when AI confidently produces false or non-existing answers which seem to be correct or logical at the first sight), context insensitivity (i.e., inability of AI systems to be conscious or understand context), and the role of training dataset in algorithmic accuracy. Educators should use this critical literacy to validate or verify AI-generated content through fact-checking or cross-referencing with authoritative sources (Peng et al., 2023; Stahl and Eke, 2024).

4.2.2 AI ethics

This competency addresses ethical and societal implications of AI technologies. Core topics include: fairness and algorithmic bias, transparency and explainability (XAI), privacy, data protection, AI’s impact on agency, autonomy, and well-being, environmental and societal effects, and malicious uses of AI (UNESCO, 2023; White and Scott, 2024; Williams et al., 2023).

4.3 Implementation phase

A central consideration in this phase is the instructor or teacher who will implement this curriculum. According to UNESCO (2024), only few countries introduced AI literacy into their education systems. This implies a shortage of qualified educators in this field. To address this gap, we propose an efficient, agile, and scalable solution: “train-the-trainer” model. In this model, a small group of highly motivated educators, who already had experience or knowledge in AI, receive intensive training and professional development. These trained educators then take on leadership roles within their faculties, supporting the capacity-building of their colleagues or in-service teachers in schools.

The effectiveness of curriculum implementation can be optimized through constructive alignment, a principle that ensures coherence among learning objectives (core competencies), pedagogy, and assessment. We suggest the following alignments during implementation: (a) aligning delivery modes with participants’ needs and preferences, (b) aligning pedagogical approaches and instructional strategies with target proficiency areas, and (c) aligning instructional materials with specific settings or learning environments.

4.4 A. Implementation: delivery modes

4.5 B. Implementation: pedagogical approaches

4.6 C. Implementation: aligning materials to setting

4.7 Program evaluation

To establishing the effectiveness, relevance, and utility of AI literacy program in practice, it’s essential to conduct a rigorous program evaluation. Rather than relying merely on a summative evaluation, we prioritize participatory, system-wide, and inquiry-based evaluation models. Patton (2003, 2008) suggests a utilization-focused evaluation, where the primary goal of evaluation is to enhance the practical use and uptake of findings by intended-users, such as educators, institutional leaders, and instructional designers.

5 Challenges and strategies

6 Conclusion

As AI continues to impact educational practices, AI literacy is emerging as a foundational competency for educators. This conceptual article has argued that teacher educators—those who prepare the next generation of teachers—require more than surface-level familiarity with AI tools. The conceptual study has addressed critical gaps in the development of comprehensive curricula for teacher educators’ professional learning in AI literacy. Building on established literature, empirical research, and the author’s decade of experience in designing faculty development programs across international contexts, this study proposed a structured and contextually adaptable curriculum for developing AI literacy among teacher educators in higher education based on the elements of BDR. Rather than prescribing a fixed sequence or hierarchical steps, the framework is intentionally designed as a flexible blueprint—an open-ended roadmap that can be adapted to diverse institutional landscapes.

By incorporating a holistic (e.g., formal, informal, and just-in-time learning mechanisms) and evidence-based approach (rigorous, utilization-focused assessment), the framework ensures the sustainability and long-term impact of AI literacy program. Furthermore, through a participatory co-design process, the curriculum aligns with institutional needs while equipping educators with the required skills to critically engage with AI in educational contexts. We also problematized its implementation by identifying potential challenges and suggesting strategies to address them. Future research should focus on empirical validating the framework by evaluating its effectiveness across diverse educational contexts, examining its impact on educators’ cognitive, behavioural and motivational factors, and refining strategies for AI integration into different learning environments. Further relevant research directions include development of rigorous benchmarks and standardized metrics for assessing AI competency development in teachers. Additionally, longitudinal studies are needed to explore the long-term effects of using AI on teacher professional development, particularly in terms of their creativity and ability to engage in life-long learning independently.

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