3.3 The DUAL.AI.TEACHer Competence Framework

DUAL.AI.TEACHer Competencies

The intersection of the horizontal and vertical dimensions of the matrix yields a set of eighteen competencies, each of which represents a specific point of convergence between a pedagogical area and an aspect of AI. These competencies define what teachers need to know, understand, and be able to do in order to effectively and responsibly integrate AI into their teaching practice, combining pedagogical competencies with values, ethical awareness, and technical knowledge required by an AI-powered education environment.

 

HUMAN-CENTERED MINDSET x PROFESSIONAL ENGAGEMENT COMPETENCY

“Teachers can maintain a human-centered perspective when using AI in professional communication, networking, and institutional and professional development.”

This competence encompasses the reflective attitudes and practical abilities needed to maintain a human-centred perspective when AI mediates professional communication, networking, and institutional or professional development. Teachers are able to keep human accountability, agency, and meaningful relationships at the centre of their professional life as AI becomes more present in the tools they use to communicate with colleagues, build learning networks, engage with stakeholders, and contribute to their institutions. They critically assess where AI is enhancing professional life and where it is beginning to displace human judgement, presence, or trust, and they make deliberate choices to keep their own voice, professional identity, and collegial relationships intact rather than outsourced to AI. They use AI to support their professional growth and collective work in ways that strengthen rather than erode their capacity to think, decide, and act as professionals, and they remain attentive to how AI shapes the texture of professional relationships within their teams and institutions. In doing so, educators act as professionally reflective practitioners who use AI purposefully in their professional engagement while ensuring that human judgement, relationships, professional identity, and educational values remain central.

HUMAN-CENTERED MINDSET x DIGITAL RESOURCES COMPETENCY

“Teachers can ensure human oversight and learner agency in AI-supported processes for selecting, creating, and evaluating digital resources.”

This competence encompasses the reflective attitudes and practical abilities needed to keep human oversight and learner agency at the centre when AI is used to select, create, customise, evaluate, manage, and share digital resources. Teachers recognise that AI tools can make resource work faster and broader in scope, and that this convenience can quietly shift the focus of pedagogical decision-making from the teacher to the system. They deliberately keep themselves in the loop on the materials that reach learners, review what AI produces or recommends, decide which resources are appropriate for their learners and context, and remain personally accountable for the choices made even when AI carried out parts of the work. They consider how the resources they create or share affect learners' agency, attending to whether materials open up choice, voice, and interpretive space for learners, or close these down. They involve learners themselves where appropriate in the use, evaluation, and discussion of AI-enhanced resources. In doing so, educators act as pedagogically responsible designers and curators of digital resources, ensuring that AI strengthens rather than replaces the teacher's judgement and the learner's place in the educational relationship. 

HUMAN-CENTERED MINDSET x TEACHING & LEARNING COMPETENCY

“Teachers can keep learner agency, human relationships, and teacher judgment at the centre of AI-supported teaching and learning.”

This competence encompasses the reflective attitudes and practical abilities needed to keep learner agency, human relationships, and teacher judgement at the centre of AI-supported teaching and learning. Teachers recognise that introducing AI into instruction changes the texture of the classroom and the dynamics of teacher–learner and peer relationships, and they remain attentive to what these shifts mean for learners. They make deliberate choices about where AI is welcome in their teaching and where the human teacher–learner relationship, direct dialogue, or learners' own thinking should remain protected. They keep their own pedagogical judgement in the foreground when AI suggests, generates, or recommends, treating these outputs as proposals to be considered rather than instructions to be followed. They preserve and actively cultivate learners' agency in AI-supported lessons by leaving room for choice, voice, struggle, and ownership of learning, rather than letting AI smooth all of these away. They remain personally accountable for the learning process and its outcomes, even when AI has been involved at multiple points. In doing so, educators act as pedagogically intentional practitioners who use AI as a tool in their teaching while keeping the human relationship between teacher and learner, and the learner's own developing mind, at the centre. 

HUMAN-CENTERED MINDSET x ASSESSMENT COMPETENCY

“Teachers can ensure human accountability and fairness in AI-assisted assessment processes.”

This competence encompasses the reflective attitudes and practical abilities needed to keep human accountability and pedagogical purpose at the centre of AI-assisted assessment. Teachers recognise that assessment is a deeply human act of judgement that shapes learners' trajectories and self-understanding, and that introducing AI into this act does not transfer the responsibility for it. They remain the accountable evaluators of their learners' work, treating AI-generated scores, analytics, and feedback as inputs into their own judgement rather than as decisions in their own right. They protect the teacher–learner relationship in the assessment process, maintaining direct human contact around feedback, growth, and difficulty, and they ensure that learners experience assessment as something a person does with them rather than something a system does to them. They make assessment outputs for individual learners, place them in the context of what they know about each learner, and use them to support growth rather than to label. In doing so, educators act as pedagogically intentional and personally accountable assessment practitioners who use AI to inform their evaluative work without delegating the judgement, the meaning-making, or the human relationship that assessment requires.

HUMAN-CENTERED MINDSET x EMPOWERING LEARNERS COMPETENCY

“Teachers can use AI to address diverse learning needs and styles, placing human dignity, equity, and the right to education at the core of every inclusive practice.”

This competence encompasses the reflective attitudes and practical abilities needed to use AI in ways that affirm human dignity, equity, learner agency, and the right to education for every learner. Teachers approach AI-supported personalisation, accessibility, and differentiation as expressions of a fundamental orientation: that every learner is seen and respected as a person, and that inclusive practice is a matter of recognition and dignity rather than only of efficiency. They keep their attention on each learner as a person rather than as a profile, an output, or a category produced by a system. They remain vigilant about whether AI-supported differentiation is genuinely opening up opportunities for participation, growth, and belonging, or quietly narrowing them, and they intervene when learners are being served less well than others. They keep the teacher–learner relationship intact for every learner, especially for those whose needs are most demanding, ensuring that AI extends their capacity to be present to learners rather than substitutes for that presence. In doing so, educators act as advocates for educational equity who use AI in service of every learner's dignity, agency, and right to participate fully in their education.

HUMAN-CENTERED MINDSET x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE COMPETENCY

“Teachers can model and cultivate human-centered attitudes towards AI as part of students' reflective digital (AI) competence development.”

This competence encompasses the reflective attitudes and practical abilities needed to model and cultivate human-centered attitudes towards AI as part of learners' developing AI competence. Teachers recognise that learners are watching how the adults around them relate to AI, and that this modelling shapes learners' own dispositions more deeply than explicit instruction. They demonstrate in their own visible practice the dispositions they hope to develop in learners: curiosity without naivety, critical engagement without cynicism, accountability for the choices they make with AI, presence to learners rather than absorption in tools, and a quiet confidence that being human in an AI-permeated world is something worth practising. They create classroom conditions in which learners can develop their own human-centred orientation to AI, by giving them space to reflect on their own use, to articulate what feels right or wrong, and to take ownership of their relationship to these technologies. They distinguish between transmitting knowledge about AI and cultivating attitudes towards AI, and they place themselves consciously in the second role. In doing so, educators act as facilitators of critical AI citizenship who let their own way of being with AI become part of the curriculum learners experience.

ETHICS OF AI x PROFESSIONAL ENGAGEMENT COMPETENCY

“Teachers can apply ethical principles and regulatory awareness to their own use of AI in professional practice and institutional contexts.”

This competence encompasses the reflective attitudes and practical abilities needed to apply ethical principles and regulatory awareness to teachers' own use of AI in professional practice and institutional contexts. Teachers are able to identify the ethical principles relevant to professional AI use, including transparency, accountability, fairness, privacy, integrity, and human oversight, and to recognise the legal and regulatory frameworks that govern it, such as data protection law, the EU AI Act, institutional AI policies, professional codes of conduct, and rules on intellectual property and confidentiality. They can analyse ethical dilemmas that arise in AI-mediated professional communication, collaboration, and development (e.g. disclosure of AI use, attribution of AI-generated content, handling of confidential information, conflicts between efficiency and accountability) and respond to them in a principled way. At the same time, teachers recognise their role in contributing to the ongoing development of ethical rules, shared norms, and responsible AI practices within educational organisations, helping to translate abstract principles and regulations into workable institutional routines. In doing so, educators act as ethically responsible and regulation-aware practitioners who not only comply with existing standards but also help shape an institutional culture in which professional AI use is transparent, principled, accountable, and trustworthy.

ETHICS OF AI x DIGITAL RESOURCES COMPETENCY

“Teachers can identify & mitigate ethical risks in AI-generated or AI-enhanced learning resources.”

This competence encompasses the reflective attitudes and practical abilities needed to identify and mitigate ethical risks in AI-generated or AI-enhanced learning resources. Teachers are able to recognise the typical risks embedded in such materials, including factual inaccuracies and hallucinations, bias and stereotyping along lines of gender, ethnicity, disability, language, or socio-economic status, lack of transparency about AI authorship, copyright and attribution issues with AI-generated content, the marginalisation of minority perspectives through mainstream-trained models, and privacy concerns arising from prompts or input data. They apply structured review practices to detect these risks in resources they create, adapt, select, or share, and they apply concrete mitigation strategies, such as editing, supplementing with alternative sources, adding context or counter-examples, labelling AI-generated material, refusing unsuitable outputs, and being transparent with learners about AI's role. Teachers also recognise the legal and regulatory frameworks relevant to AI-generated resources, including copyright and licensing rules, data protection law, and transparency obligations under the EU AI Act, and they apply them in their day-to-day resource work. In doing so, educators act as ethically informed reviewers and risk-mitigators of AI-enhanced educational content, ensuring that the materials reaching learners are accurate, fair, inclusive, transparent, and legally sound, and that ethical responsibility for those materials remains with the teacher rather than the AI system.

ETHICS OF AI x TEACHING & LEARNING COMPETENCY

“Teachers can integrate ethical reasoning into decisions on whether and how to use AI for teaching strategies & learning activities.”

This competence encompasses the reflective attitudes and practical abilities needed to integrate ethical reasoning into decisions about whether, when, and how to use AI in teaching strategies and learning activities. Teachers are able to identify the ethical principles relevant to AI-supported instruction, including fairness, learner autonomy, transparency, non-maleficence, intellectual honesty, and beneficence, and to recognise the ethical issues that are specific to AI in classrooms, such as disclosure of AI use to learners, academic integrity when learners use AI, informed engagement and (where appropriate) consent, emotional and developmental effects of AI tutors and companions, the homogenising influence of generative tools on student thinking, and the handling of learner data generated during AI-supported activities. They apply structured ethical reasoning to concrete instructional choices, weighing pedagogical benefit against ethical risk, justifying decisions to learners and colleagues, and revisiting decisions as evidence accumulates. At the same time, they critically engage with emerging AI capabilities, neither rejecting them by default nor adopting them uncritically, ensuring that each instructional decision involving AI is principled, defensible, and aligned with the broader values of education. In doing so, educators act as ethically grounded instructional decision-makers who treat the integration of AI into teaching not as a technical choice but as a recurring ethical practice.

ETHICS OF AI x ASSESSMENT COMPETENCY

“Teachers can ensure that AI-assisted assessment practices meet standards of fairness, transparency, and legal compliance, allowing them to reflect on feedback & improve their assessment practices.”

This competence encompasses the reflective attitudes and practical abilities needed to ensure that AI-assisted assessment practices meet standards of fairness, transparency, and legal compliance, and to use feedback from these practices to continuously improve assessment. Teachers are able to recognise the specific fairness risks that arise when AI is used in evaluation, including algorithmic bias, unequal accuracy across learner groups (e.g. by language, disability, socio-economic background), validity and construct concerns, and the risk of opaque automated decisions affecting learners' futures. They understand the transparency obligations of AI-assisted assessment, such as making clear to learners and other stakeholders when and how AI is involved, explaining results in accessible terms, and ensuring that learners can question, contest, and seek human review of AI-influenced decisions. They recognise the legal and regulatory frameworks that apply to AI in assessment, including data protection law, national regulations on examinations and grading, and the EU AI Act's classification of educational assessment systems as high-risk, with the corresponding obligations for documentation, human oversight, and accountability. Teachers systematically reflect on the feedback generated by AI-assisted assessment, including outcomes, disparities between learner groups, learner and parent feedback, and complaints and use these insights to refine their assessment practices over time. In doing so, educators act as fairness-aware, transparency-oriented, and regulation-conscious assessment practitioners who treat AI-assisted assessment as a domain requiring continuous ethical scrutiny and evidence-based improvement.

ETHICS OF AI x EMPOWERING LEARNERS COMPETENCY

“Teachers can address the ethical dimensions of AI personalisation, including risks of inequity, data misuse, and exclusion.”

This competence encompasses the reflective attitudes and practical abilities needed to address the ethical dimensions of AI-based personalisation in education, particularly the risks of inequity, data misuse, algorithmic discrimination, automated decision-making, and exclusion. Teachers are able to recognise the specific ethical risks that arise when AI personalises learning paths, supports special educational needs, or adapts to individual learners: the collection and reuse of sensitive learner data (including behavioural, biometric, emotional, diagnostic, and socio-economic information), opaque or biased system decisions that may channel learners onto narrowed pathways, differential accuracy and treatment across learner groups, and the reduction of human discretion when automated systems make consequential recommendations. They recognise the legal and rights-based frameworks that apply with particular force in this area, including GDPR rules on consent, data minimisation, purpose limitation, special categories of data, and automated individual decision-making, as well as children's data rights, accessibility and disability-rights standards, and the EU AI Act's high-risk classification of educational AI. They apply concrete safeguards in their own practice, such as limiting which data are fed into personalisation systems, ensuring meaningful human review of consequential recommendations, communicating with learners and families about data use, and refusing personalisation features that introduce disproportionate risk. In doing so, educators act as risk-aware and rights-conscious practitioners of AI-supported personalisation, ensuring that personalisation expands rather than restricts learners' opportunities and that the ethical and legal responsibility for personalised decisions remains visibly human.

ETHICS OF AI x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE COMPETENCY

“Teachers can educate students in responsible, critical, and ethically informed use of AI technologies.”

This competence encompasses the reflective attitudes and practical abilities needed to educate students in the responsible, critical, and ethically informed use of AI technologies. Teachers are able to introduce learners to the ethical questions raised by AI, including fairness and bias, privacy and data protection, transparency, intellectual honesty, attribution, manipulation and persuasion (e.g. deepfakes, generative misinformation), automated decisions affecting people's lives, surveillance, the role of AI in democratic processes, and the environmental footprint of AI, and to make these questions accessible at a level appropriate to learners' age and development. They use pedagogical methods that develop learners' own ethical reasoning rather than transmitting fixed answers: dilemma discussions, case studies, perspective-taking across affected stakeholders, structured ethical argumentation, and reflection on learners' own AI practices. They equip learners to recognise ethical issues in everyday encounters with AI, to weigh competing values, to act responsibly in their own use of AI (e.g. attribution, integrity, respectful interaction), and to question AI's societal impact as informed citizens. Teachers also know how to respond pedagogically when learners use AI in ethically problematic ways, including academic dishonesty, sharing of harmful generated content, or reliance on AI companions in unsafe ways, turning such moments into ethical learning rather than only sanction. In doing so, educators act as facilitators of learners' ethical AI agency, equipping students with the dispositions, vocabulary, and reasoning practices needed for critical AI citizenship.

AI FOUNDATIONS AND APPLICATIONS x PROFESSIONAL ENGAGEMENT COMPETENCY

“Teachers can apply foundational AI knowledge to inform professional decisions, engage critically with AI tools, lead AI-related professional learning in institutional contexts.”

This competence encompasses the reflective attitudes and practical abilities needed to apply foundational AI knowledge to professional decision-making, critical tool use, and AI-related professional learning in institutional contexts. Teachers understand at a working conceptual level how contemporary AI systems function, including the role of training data, the probabilistic nature of generative models, the origins of hallucinations and confident-sounding errors, the differences between model types (e.g. plain chat models, retrieval- and search-augmented systems, agentic tools), and the implications of context windows, persistence, and tool integration. They draw on this understanding to evaluate AI tools for specific professional tasks, applying criteria such as fit-for-purpose, accuracy, validation, data residency and provider trustworthiness, model generation, institutional approval, and known limitations. They use AI tools deliberately and critically in their own professional work, recognising what each tool can and cannot reliably do, adjusting prompts and workflows accordingly, and knowing when to defer to other sources or to human judgement. They keep their AI literacy up to date as the technology evolves, and they share this understanding with colleagues, supporting peers, helping evaluate new tools, contributing to AI-related staff development, and informing institutional decisions about which AI tools to adopt or avoid. In doing so, educators act as AI-literate professionals who can translate technical understanding into informed practical choices and into institutional capacity-building.

AI FOUNDATIONS AND APPLICATIONS x DIGITAL RESOURCES COMPETENCY

“Teachers can critically select & use AI tools for creating, adapting & enhancing digital educational resources based on technical understanding of AI systems.”

This competence encompasses the reflective attitudes and practical abilities needed to critically select and use AI tools for creating, adapting, and enhancing digital educational resources, based on a sound working understanding of how these tools actually function. Teachers understand at a conceptual level how the AI systems behind common resource tools work, including generative text models (and the origins of variability, hallucinations, and stylistic limits), image-generation systems (and their typical failure modes), translation and adaptation models, text-to-speech and voice systems, and retrieval-augmented systems that combine generation with sourced content. They draw on this understanding to choose the right type of tool for a given resource task (e.g. generating a worked example vs. translating a text vs. producing illustrative images vs. summarising sources with citations), to set realistic expectations about what each tool can deliver, and to apply effective prompting and iteration strategies. They evaluate AI tools for resource creation against criteria such as output quality in the relevant language and subject, controllability and reproducibility, export and integration options, and licensing terms for generated content. They use this knowledge throughout a deliberate workflow, drafting, reviewing, refining, and finalising AI-generated material and recognise when a tool is structurally unable to deliver what is needed and a different tool, a different approach, or a non-AI resource is required. In doing so, educators act as technically informed resource designers who let their understanding of the technology, rather than novelty or hype, guide which AI tools they use, how they use them, and where they stop.

AI FOUNDATIONS AND APPLICATIONS x TEACHING & LEARNING COMPETENCY

“Teachers can apply understanding of how AI systems work to make informed, critical decisions about AI integration in teaching and learning, improving their own teaching practices and instructional design approaches.”

This competence encompasses the reflective attitudes and practical abilities needed to apply a working understanding of how AI systems function to the design, implementation, and continuous improvement of teaching and learning. Teachers understand at a conceptual level how the AI systems most relevant to instruction work, including generative chat and tutoring tools, adaptive and recommendation systems, content-generation tools used in lesson preparation, and feedback-generating systems and what each type can and cannot reliably do in a classroom context (e.g. handling of subject content, factual reliability, responsiveness to learners' prompts, variability of outputs, limits of "understanding"). They use this technical insight to make deliberate instructional decisions: choosing the right type of AI tool for a given teaching or learning purpose, anticipating where outputs will need verification or reframing, designing prompts and tasks that exploit what the tool can actually do, and identifying situations in which AI integration adds no instructional value or risks distorting learning. They translate this understanding into instructional design, embedding AI use into lesson sequences with clear pedagogical roles for the tool, the teacher, and the learner, and they review the technical performance of AI in their lessons (e.g. accuracy of outputs, learner interactions, failure modes) to refine their practice over time. In doing so, educators act as technically informed instructional designers who let their understanding of how AI systems actually work shape whether, when, and how AI is integrated into teaching and learning, progressing from competent users of validated tools to designers of AI-supported learning environments grounded in realistic expectations of the technology.

AI FOUNDATIONS AND APPLICATIONS x ASSESSMENT COMPETENCY

“Teachers can apply AI knowledge to design, implement and evaluate efficient and equitable AI-enhanced assessment practices, and systematically improve their assessment practices.”

This competence encompasses the reflective attitudes and practical abilities needed to apply a working understanding of how assessment-related AI systems function to the design, implementation, evaluation, and systematic improvement of AI-enhanced assessment practices. Teachers understand at a conceptual level how the main types of AI used in assessment work, including automated scoring systems (rule-based and machine-learning-based), learning analytics and progress-prediction systems, AI-generated feedback (LLM-based), AI-supported item and quiz generation, AI-detection tools (e.g. for plagiarism or generative-AI use), and adaptive testing systems, and what each type actually measures, what it cannot measure, and where it typically fails (e.g. unusual answer styles, language variation, creative solutions, edge cases). They draw on this understanding to evaluate the appropriateness of an AI tool for a specific assessment task, asking what the tool measures, how it produces its outputs, how stable and reproducible those outputs are, and whether they hold up across different learner groups. They use this knowledge in practice, selecting tools deliberately, designing assessment workflows that combine AI outputs with structured human review, recognising when AI-detection or scoring outputs are unreliable enough that they should not drive decisions, and improving the use of tools over time on the basis of observed technical performance (e.g. comparing tool outputs against own judgement, tracking failure modes, switching tools when limitations are structural). In doing so, educators act as technically grounded assessment practitioners who let their understanding of how the underlying systems work shape which AI tools they use for assessment, how they use them, where they verify their outputs, and where they stop.

AI FOUNDATIONS AND APPLICATIONS x EMPOWERING LEARNERS COMPETENCY

“Teachers can select pedagogical strategies and AI tools that support personalisation and inclusion, based on informed understanding of how they function.”

This competence encompasses the reflective attitudes and practical abilities needed to select pedagogical strategies and AI tools that support personalisation, accessibility, and inclusion, based on a working understanding of how these tools actually function. Teachers understand at a conceptual level how the main types of AI used to personalise and support diverse learning functions, including adaptive learning systems, recommender systems, speech recognition and text-to-speech tools, image-description and OCR tools, real-time translation and text-simplification systems, AI-supported augmentative and alternative communication (AAC), AI-tutoring systems, and emotion- or engagement-recognition tools, and what each is technically able to do, what it cannot do, and where its performance breaks down (e.g. for accented speech, less-resourced languages, atypical answer patterns, learners with few prior data points, learners whose demographics are under-represented in the training data). They use this understanding to evaluate whether a tool is genuinely fit for a particular learner or group, to anticipate where the tool is likely to under-serve precisely the learners who need it most, and to make informed decisions about configuration and customisation (e.g. accessibility settings, AAC vocabularies, recommender constraints, fallback workflows). They distinguish what a tool claims to measure from what it actually measures (e.g. engagement-recognition systems often track facial proxies rather than engagement) and choose tools accordingly. They refine their use over time by observing how the tool performs across different learners, switching tools when limitations are structural, and combining AI support with non-AI alternatives where appropriate. In doing so, educators act as technically informed practitioners of inclusive AI use, who let their understanding of how the underlying systems work shape which tools they deploy for which learners, how they are configured, and where they should not be relied upon.

AI FOUNDATIONS AND APPLICATIONS x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE COMPETENCY

“Teachers can build students' understanding of AI technologies and their societal implications as part of AI competence development.”

This competence encompasses the reflective attitudes and practical abilities needed to build learners' understanding of how AI technologies actually work and their societal implications, as the technical foundation of AI literacy. Teachers are able to make core AI concepts accessible to learners at a level appropriate to their age and prior knowledge, including the difference between AI and conventional programming, the role of training data and pattern-finding, the probabilistic nature of generative models, the main categories of AI systems (e.g. generative AI, classification and prediction, computer vision, speech and language, recommender systems), and the limits of what current AI can do (e.g. hallucinations, lack of real-time knowledge, variability of outputs, absence of genuine understanding). They guide learners in developing the practical skills to work productively with AI, including formulating effective prompts, verifying outputs against other sources, combining AI assistance with their own thinking, and recognising AI-driven systems already embedded in the everyday tools they use. They explain the societal implications of AI from a technical perspective (e.g. why filter bubbles arise in recommender systems, why hallucinations are not "bugs" to be patched out, why some applications consume large amounts of energy, why certain tasks are easier or harder for current systems), so that learners can ground their judgements in how the technology actually functions rather than in hype or fear. Teachers select age-appropriate pedagogical methods for AI literacy, such as analogies, unplugged activities, hands-on experimentation with real tools, demonstrations of typical failure modes, and small data experiments and adapt them as the technology evolves. In doing so, educators act as technically grounded facilitators of AI literacy who equip learners with conceptual understanding, practical skills, and the technical insight needed to navigate AI-permeated environments with realistic expectations and informed agency.

 

DUAL.AI.TEACHer Learning Objectives

For each of the eighteen competencies identified, a set of specific learning objectives has been defined in order to translate the competency into concrete and practical outcomes. These learning objectives specify the knowledge, skills, and attitudes that teachers are expected to develop to achieve the corresponding competency, providing a more detailed and practical description of what mastering that competency entails in practice. 

By breaking down each competency into clearly defined learning objectives at different levels, the DUAL.AI.TEACHer Framework facilitates its practical application in professional development, training design, and self-assessment, offering educators a concrete blueprint for progressively building and demonstrating their AI-related competencies.

HUMAN-CENTERED MINDSET x PROFESSIONAL ENGAGEMENT

HUMAN-CENTERED MINDSET x DIGITAL RESOURCES

HUMAN-CENTERED MINDSET x TEACHING & LEARNING

 

HUMAN-CENTERED MINDSET x ASSESSMENT

HUMAN-CENTERED MINDSET x EMPOWERING LEARNERS

HUMAN-CENTERED MINDSET x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE

ETHICS OF AI x PROFESSIONAL ENGAGEMENT

ETHICS OF AI x DIGITAL RESOURCES

ETHICS OF AI x TEACHING & LEARNING

ETHICS OF AI x ASSESSMENT

ETHICS OF AI x EMPOWERING LEARNERS

ETHICS OF AI x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE

AI FOUNDATIONS AND APPLICATIONS x PROFESSIONAL ENGAGEMENT

AI FOUNDATIONS AND APPLICATIONS x DIGITAL RESOURCES

AI FOUNDATIONS AND APPLICATIONS x TEACHING & LEARNING

AI FOUNDATIONS AND APPLICATIONS x ASSESSMENT

AI FOUNDATIONS AND APPLICATIONS x EMPOWERING LEARNERS

AI FOUNDATIONS AND APPLICATIONS x FACILITATING LEARNERS’ AI DIGITAL COMPETENCE