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Artificial Intelligence Literacy and Competency in Pre-Service Teacher Education: History
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Contributor: Hsiao-Ping Hsu

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values.

  • AI literacy
  • AI competency
  • pre-service teacher education
  • generative AI
  • ethics
  • socio-technical systems
Artificial Intelligence (AI) technologies are increasingly present in education, and teacher education programmes are under growing pressure to prepare future teachers to work with AI in responsible and educationally meaningful ways [1][2][3]. In pre-service teacher education, a common question is what teachers need to know about AI and what they need to do with that knowledge in real teaching, learning, and assessment contexts, including situations involving generative AI [4][5][6][7][8]. In this area, two key terms are widely used: AI literacy and AI competency [9][10]. Although these terms are often used interchangeably in international frameworks and the literature [9][10][11], recent scholarship and policy-oriented work increasingly treat them as related but distinct constructs that differ in scope and specificity [9][12][13].

Distinguishing Literacy from Competency: Implications for Teacher Preparation

In pre-service teacher education, AI literacy is commonly described as knowledge that integrates three areas. First, it includes a basic understanding of how AI systems work [14]. Second, it includes the ability to evaluate AI outputs critically [15]. Third, it includes ethical and socio-technical awareness of AI-related issues such as bias, privacy, transparency, accountability, integrity, equity, and environmental sustainability [9][11]. In short, AI literacy helps pre-service teachers understand what AI is, how it generates outputs, and what risks and impacts may follow when it is used in educational settings [12][16][17]. This understanding of literacy aligns with broader perspectives that view literacy as socially situated and context-dependent [18][19][20].
In comparison, AI competency refers to a teacher’s context-specific capacity to apply AI-related knowledge, skills, and dispositions within their professional role [7][9][21]. This also resonates with established models of teacher knowledge that emphasise the integration of technology, pedagogy, and content [22][23]. In simple terms, AI competency is about using AI in ways that support teaching, learning, and assessment effectively, critically, and ethically under real constraints [6][9][24]. For example, a pre-service teacher may recognise that a generative AI tool can produce fluent but potentially inaccurate explanations of a topic, but AI competency involves deciding whether such output is appropriate for a specific lesson under time and curricular constraints, checking it against reliable sources, and adapting it in ways that support pupils’ learning outcomes while maintaining accuracy, fairness, and transparency. Research on teacher AI competency also highlights motivational and psychological factors, such as confidence, self-efficacy, and a reflective orientation [1][12]. These factors can shape whether pre-service teachers can adopt, critique, and adapt AI tools in beneficial ways in the classroom [6][9][11].
At the same time, major policy-oriented frameworks do not treat the boundary between literacy and competency as fully separate [1][25][26]. For example, the European Commission and the Organisation for Economic Co-operation and Development () [27] define AI literacy as the technical knowledge, durable skills, and future-ready attitudes required to thrive in a world influenced by AI. This definition already includes attitudes, and the framework presents learning expectations that resemble competency statements. For pre-service teacher education, the practical value of the distinction is, therefore, not to enforce rigid terminology. Instead, it helps clarify curriculum functions. AI literacy can be treated as the knowledge base and shared language across the programme, while AI competency can be treated as the application of that knowledge in professional tasks [9][12][13]. This framing also supports later sections in this entry, because ethical and socio-technical concerns require both understanding and enacted professional judgement [13][28].

UNESCO AI Competency for Teachers

UNESCO’s AI Competency Framework for Teachers (AI CFT) [24] describes how teachers can develop AI competency over time. It is organised as a matrix that crosses five aspects with three progression levels (Acquire, Deepen, and Create). The five aspects are human-centred mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional development. The framework notes that competency development is complex and context-dependent, so the progression levels serve as a reference pathway rather than fixed steps. At the Acquire level, teachers develop the essential competencies needed to evaluate, select, and use AI tools appropriately. The Deepen level focuses on designing meaningful pedagogical strategies that integrate AI. The Create level emphasises innovative use, including more creative configuration of AI systems in education. By crossing the three levels with the five aspects, the AI CFT specifies fifteen competency blocks.
For pre-service teacher education, the AI CFT can be used as a planning reference for staged learning objectives across coursework and school placement. Early learning activities can focus on safe and critical use in low-stakes tasks, including checking the trustworthiness of AI outputs. Later activities can focus on designing, justifying, and evaluating AI-informed teaching and assessment practices in authentic classroom contexts, with clear evidence of professional judgement. The framework also cautions that over-reliance on AI may lead to the atrophy of teachers’ essential competencies, and it, therefore, highlights teacher agency and human accountability.

EU and OECD AI Literacy Framework

The draft AI literacy framework, developed through collaboration between the EU and OECD, [27], was established on the basis of the European Commission’s Digital Competence Framework for Citizens [29], UNESCO’s AI competencies for students [30] and AI competencies for teachers [24], The Digital Promise AI Literacy Framework [31], and The AI4K12 5 Big Ideas in AI [32] to define AI literacy through “technical knowledge, durable skills, and future-ready attitudes”. It structures AI literacy into four interaction domains: engaging, creating, managing, and designing. Each domain has a number of competencies. Each competence is a learning expectation that reflects technical knowledge, durable skills, and future-ready attitudes.
Within Engaging with AI, the framework expects learners to recognise where AI is used, evaluate whether AI outputs should be accepted, revised, or rejected, and consider how recommendations, bias, and environmental costs may shape decisions. Creating with AI focuses on co-producing ideas and artefacts with generative systems, using AI for ideation, prototyping, and feedback while attending to authenticity, attribution, and clear language that avoids anthropomorphism. Managing AI emphasises human agency in delegation: learners decide whether to use AI based on the task, and decompose a problem based on the capabilities and limitations of both AI systems and humans, then monitor and adjust AI use to stay aligned with goals and values. Designing AI develops a deeper understanding of how AI works and why it matters, including comparing rule-based and data-driven systems, considering data quality and representation, and evaluating AI systems against criteria such as purpose, users, limitations, and potential impacts. For pre-service teacher education, these four domains provide a practical organiser for mapping learning outcomes to routine tasks in planning, teaching, assessment, and professional judgement.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia6040076

References

  1. Chee, H.; Ahn, S.; Lee, J. A Competency Framework for AI Literacy: Variations by Different Learner Groups and an Implied Learning Pathway. Br. J. Educ. Technol. 2025, 56, 2146–2182.
  2. Le, T.H.; Huynh, L.; Dang, B.; Pham, H.-H.; Nguyen, N.T.V.; Nguyen, A. Development and evaluation of artificial intelligence literacy training for teacher education students. Br. J. Educ. Technol. 2026, in press.
  3. Kohnke, L.; Zou, D.; Ou, A.W.; Gu, M.M. Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Comput. Educ. Artif. Intell. 2025, 8, 100398.
  4. Tran, P.T.H.; Huynh, L.; Bien, T.-A.; Dang, B.; Nguyen, A. Preparing preservice teachers for generative AI in lesson planning: A process mining study of AI mindset and tool-only training. J. Digit. Learn. Teach. Educ. 2025, 42, 16–32.
  5. Mikeladze, T.; Meijer, P.C.; Verhoeff, R.P. A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. Eur. J. Educ. 2024, 59, e12663.
  6. Heine, S.; König, J. Applying artificial intelligence in teacher education: Preservice teachers’ attitudes and reflections in using ChatGPT for teaching and learning. Eur. J. Teach. Educ. 2025, 48, 934–963.
  7. Zhou, X.; Lavicza, Z.; Chiu, T.K.F. Developing teacher AI competence: A systematic review of beliefs, professional learning, and cultural factors. Teach. Teach. Educ. 2026, 172, 105384.
  8. Hsu, H.-P.; Mak, J.; Werner, J.; White-Taylor, J.; Geiselhofer, M.; Gorman, A.; Torrejon Capurro, C. Preliminary Study on Pre-Service Teachers’ Applications and Perceptions of Generative Artificial Intelligence for Lesson Planning. J. Technol. Teach. Educ. 2024, 32, 409–437.
  9. Chiu, T.K.F. AI literacy and competency: Definitions, frameworks, development and future research directions. Interact. Learn. Environ. 2025, 33, 3225–3229.
  10. Karaduman, C. Pre-Service EFL Teachers’ Perceived AI Literacy and Competency: The Integration of ChatGPT into English Language Teacher Education. Sage Open 2025, 15, 21582440251379712.
  11. Chiu, T.K.F.; Sanusi, I.T. Define, foster, and assess student and teacher AI literacy and competency for all: Current status and future research direction. Comput. Educ. Open 2024, 7, 100182.
  12. Zhou, X.; Li, Y.; Chai, C.S.; Chiu, T.K.F. Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 education from a systematic review. Interact. Learn. Environ. 2025, 33, 5766–5788.
  13. Han, X. Improving pre-service teachers’ AI competencies: A quasi experimental study. Front. Psychol. 2025, 16, 1642465.
  14. Ng, D.T.K.; Leung, J.K.L.; Chu, S.K.W.; Qiao, M.S. Conceptualizing AI literacy: An exploratory review. Comput. Educ. Artif. Intell. 2021, 2, 100041.
  15. Laru, J.; Celik, I.; Jokela, I.; Mäkitalo, K. The antecedents of pre-service teachers’ AI literacy: Perceptions about own AI driven applications, attitude towards AI and knowledge in machine learning. Eur. J. Teach. Educ. 2025, 48, 964–986.
  16. Dilek, M.; Baran, E.; Aleman, E. AI Literacy in Teacher Education: Empowering Educators Through Critical Co-Discovery. J. Teach. Educ. 2025, 76, 294–311.
  17. Pei, B.; Lu, J.; Jing, X. Empowering preservice teachers’ AI literacy: Current understanding, influential factors, and strategies for improvement. Comput. Educ. Artif. Intell. 2025, 8, 100406.
  18. Street, B.V. Literacy in Theory and Practice; Cambridge University Press: Cambridge, UK, 1984.
  19. Buckingham, D. Media Education: Literacy, Learning and Contemporary Culture; Polity Press: Cambridge, UK, 2003.
  20. Bacalja, A.; Nichols, T.P.; Robinson, B.; Bhatt, I.; Kucharczyk, S.; Zomer, C.; Nash, B.; Dupont, B.; De Cock, R.; Zaman, B.; et al. Postdigital Videogames Literacies: Thinking with, Through, and Beyond James Gee’s Learning Principles. Postdigit. Sci. Educ. 2024, 6, 1103–1142.
  21. Hsu, H.-P. Promote Environmental Awareness and Care by Creating a Virtual Reality Tour for the Local Community. Geogr. Teach. 2024, 21, 119–125.
  22. Mishra, P.; Koehler, M.J. Technological pedagogical content knowledge: A framework for teacher knowledge. Teach. Coll. Rec. 2006, 108, 1017–1054.
  23. Celik, I. Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Comput. Hum. Behav. 2023, 138, 107468.
  24. United Nations Educational, Scientific and Cultural Organisation. AI Competency Framework for Teachers; UNESCO: Paris, France, 2024.
  25. Hur, J.W. Fostering AI literacy: Overcoming concerns and nurturing confidence among preservice teachers. Inf. Learn. Sci. 2024, 126, 56–74.
  26. Sperling, K.; Stenberg, C.-J.; McGrath, C.; Åkerfeldt, A.; Heintz, F.; Stenliden, L. In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Comput. Educ. Open 2024, 6, 100169.
  27. European Commission and Organisation for Economic Co-operation and Development. Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education (Review Draft); OECD: Paris, France, 2025.
  28. Chiu, T.K.F. Intelligent-TPACK (I-TPACK) framework developed from TPACK through integration of artificial intelligence literacy and competency. Interact. Learn. Environ. 2026, 1–16.
  29. Vuorikari, R.; Kluzer, S.; Punie, Y. DigComp 2.2, the Digital Competence Framework for Citizens—With New Examples of Knowledge, Skills and Attitudes; Publications Office of the European Union: Luxembourg, 2022.
  30. United Nations Educational, Scientific and Cultural Organisation. AI Competency Framework for Students; UNESCO: Paris, France, 2024.
  31. Mills, K.; Ruiz, P.; Lee, K.-w.; Coenraad, M.; Fusco, J.; Roschelle, J.; Weisgrau, J. AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology; Digital Promise: Washington, DC, USA, 2024.
  32. The AI4K12 Initiative, Grade Band Progression Charts. 2022. Available online: https://ai4k12.org/ (accessed on 16 February 2026).
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