AE Digital and AI Proficiency Taxonomy (ADAPT)

A national standard for digital competence specifically designed for adult educators in Singapore, empowering them to thrive in the AI-driven future of work.

Introduction

The Challenge

The rapid advancement of digital technologies and artificial intelligence is transforming how adults learn, work, and live. Adult educators are at the forefront of this transformation, yet Singapore's adult education sector lacks a common definition of digital competence that addresses the unique needs of adult learners and the AI-driven future of work. Without a shared framework, adult educators cannot identify their skill gaps, access competency-aligned professional development, or progress systematically in their digital capabilities.

Purpose of This Framework

The AE Digital and AI Proficiency Taxonomy (ADAPT) establishes a national standard for digital competence specifically designed for adult educators in Singapore. It provides:

Target Audience

This framework is designed for:

Guiding Principles

The framework is grounded in four principles:

1
Human-Centred AI Integration

Technology serves to enhance, not replace, the educator-learner relationship. Adult educators maintain agency and accountability in pedagogical decisions.

2
Andragogical Foundation

Recognising that adult learners are self-directed, bring rich prior experience, and are motivated by relevance to their professional and personal goals.

3
Ethical and Inclusive Practice

Ensuring digital technologies are used safely, ethically, and inclusively, with attention to accessibility, data privacy, and equity.

4
Lifelong Professional Growth

Supporting educators' continuous development as technology evolves, from foundational literacy to innovative leadership.

Scope

The framework addresses digital competence across four interconnected domains, each structured across three progression levels (Basic → Intermediate → Advanced), enabling educators to identify their current position and chart their development pathway.

Theoretical Foundations

This framework synthesises established international standards with competency design models to ensure rigour, relevance, and practical applicability.

International Frameworks

Framework Contribution to This Framework
DigCompEdu
(EU, 2017) 		Provided the foundational domain structure for educator-specific digital competencies and the concept of progression levels
UNESCO ICT-CFT
(UNESCO, 2018) 		Informed the integration of ICT competencies with professional practice and pedagogical transformation
UNESCO AI CFT
(UNESCO, 2024) 		Guided the human-centred approach to AI integration and ethical principles for AI in education

Competency Design Models

Bloom's Revised Taxonomy

(Anderson & Krathwohl, 2001)

Ensures competency statements use cognitively appropriate action verbs:

  • Basic: Remember, Understand, Apply
  • Intermediate: Apply, Analyse, Evaluate
  • Advanced: Evaluate, Create

Dreyfus Model of Skill Acquisition

(Dreyfus & Dreyfus, 1980)

Defines expected autonomy at each level:

  • Basic: Novice → Advanced Beginner
  • Intermediate: Competent → Proficient
  • Advanced: Proficient → Expert

How to read the statements: Each competency statement begins with level-appropriate verbs (shown in bold in the tables below) and ends with qualifiers (shown in italics) that indicate the expected level of autonomy, support, and scope.

References

Anderson, L.W., & Krathwohl, D.R. (Eds.). (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives . Longman.

Dreyfus, S.E., & Dreyfus, H.L. (1980). A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition . Operations Research Center, University of California, Berkeley.

Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu . EUR 28775 EN. Publications Office of the European Union.

UNESCO. (2018). UNESCO ICT Competency Framework for Teachers (Version 3). UNESCO.

UNESCO. (2024). AI Competency Framework for Teachers . UNESCO.

1. Teaching, Learning & Empowering Learners

Technology-enhanced andragogy for inclusive and learner-centred facilitation

1.1. Design & Planning
Designing and planning technology-enhanced learning experiences.
Basic
Applies established templates to plan technology-enhanced learning activities, using digital tools with guidance .
Examples:
  • Identifies digital tools that could be used for a specific part of a lesson, with guidance.
  • Applies a mentor-provided lesson plan template to incorporate one digital or AI activity.
  • Uses an accessibility checklist to ensure basic inclusivity when planning activities with AI tools.
Intermediate
Designs blended learning experiences independently , integrating diverse digital and AI strategies to adapt to learner needs .
Examples:
  • Sequences learning activities across multiple sessions, aligning AI tools with specific adult learning objectives independently.
  • Selects and adapts digital resources, including AI-generated content, evaluating their pedagogical affordances and potential biases.
  • Applies AI tools strategically to generate lesson ideas, create assessments, and personalise content for diverse adult learner needs.
Advanced
Architects innovative curriculum frameworks with authentic learning experiences that leverage AI, guiding peers through the design process and advancing professional practice .
Examples:
  • Appraises contemporary digital andragogies and advances new approaches within the professional community.
  • Architects innovative, authentic learning activities enhanced by AI, guiding peers through the design process.
  • Co-designs learning interventions with learners and colleagues, synthesising AI-gathered feedback to refine approaches.
1.2. Implementation & Facilitation
Implementing and facilitating technology-enhanced learning.
Basic
Demonstrates structured lesson delivery using digital tools, applying established protocols in supervised settings .
Examples:
  • Uses AI-powered presentation tools to present information to learners in supervised settings.
  • Applies established communication protocols using digital channels, including AI chatbots, with guidance.
  • Monitors learner participation indicators (e.g., attendance, submission status) in an LMS with supervisor support.
Intermediate
Orchestrates diverse digital learning scenarios, adapting facilitation strategies based on real-time learner engagement patterns .
Examples:
  • Adapts facilitation approach mid-session based on real-time learner engagement data from AI analytics.
  • Orchestrates interactive and collaborative learning activities across synchronous and asynchronous digital settings, integrating AI-powered tools.
  • Facilitates asynchronous discussions and self-paced learning modules, using AI to personalise learner pathways.
Advanced
Pioneers innovative andragogical practices using digital and AI tools, coaching fellow educators in advanced facilitation and leading institutional implementation .
Examples:
  • Experiments with emerging AI-facilitation models (e.g., AI teaching assistants, adaptive pathways) and documents outcomes for institutional adoption.
  • Guides fellow educators on digital and AI teaching strategies, modelling advanced facilitation techniques.
  • Leads implementation of innovative digital and AI-enhanced teaching approaches across programmes.
1.3. Learner Engagement
Engaging and motivating learners by fostering creativity and collaboration in digital and AI-enhanced environments.
Basic
Uses digital tools to initiate learner interaction, applying structured collaborative learning protocols with guidance .
Examples:
  • Uses AI-powered tools for question-and-answer sessions (e.g., AI chatbots, polls) following established protocols.
  • Applies pre-designed collaborative activities with educator guidance and some AI support.
  • Sets up simple collaborative tasks (e.g., contributing to a shared document with AI-generated prompts) using provided templates.
Intermediate
Facilitates collaborative learning systematically, selecting appropriate digital tools to foster meaningful peer interaction .
Examples:
  • Designs and facilitates structured collaborative projects, selecting appropriate digital tools.
  • Designs gamified learning challenges using digital platforms, incorporating adaptive elements where available.
  • Facilitates peer feedback processes using AI-powered tools to structure and enhance feedback quality.
Advanced
Creates innovative collaborative learning ecosystems that empower learners as co-creators with AI, supporting fellow educators and enriching the field with evaluation findings .
Examples:
  • Develops and experiments with new formats for online collaboration, including AI-facilitated discussions, supporting fellow educators in adoption.
  • Enables learners to self-organise and manage their collaborative projects using AI tools for project management.
  • Rigorously evaluates the effectiveness of collaborative strategies and AI tools, sharing findings with the professional community.
1.4. Support & Wellbeing
Supporting learners' wellbeing by promoting safe, legal, and ethical use of digital technologies, including AI.
Basic
Recognises digital safety risks including AI-related threats, applying institutional protocols and referring learners to support services .
Examples:
  • Recognises signs of digital fatigue or AI anxiety in learners and refers them to appropriate support services.
  • Directs learners to available support resources when they express concerns about digital overload or technology-related stress.
  • Applies institutional policies for learner support and online safety.
Intermediate
Implements safe, supportive learning environments, integrating digital and AI wellbeing strategies adapted to diverse learner needs .
Examples:
  • Selects digital tools and adapts teaching strategies to support learner wellbeing.
  • Designs activities that develop learners' critical awareness of digital wellbeing and AI safety.
  • Implements differentiated support strategies for learners with diverse needs in the digital environment.
Advanced
Advocates for institutional digital and AI wellbeing policies, coaching colleagues in creating supportive environments and enabling learners to self-manage digital safety .
Examples:
  • Develops and evaluates institutional strategies for digital and AI wellbeing and inclusion.
  • Coaches colleagues on creating safe and supportive online learning environments in the age of AI.
  • Facilitates learner-led workshops where adult learners share digital wellbeing strategies with peers.

2. Assessment

Leveraging digital tools and AI for formative, summative, and personalised feedback

2.1. Design & Delivery
Designing and delivering digital and AI-enhanced assessments.
Basic
Uses digital tools to create basic assessments, applying pre-defined templates and institutional guidelines .
Examples:
  • Uses AI quiz generators to create simple assessments, applying provided templates and institutional guidelines.
  • Applies a simple AI tool to create a quiz with standard question types (e.g., multiple-choice).
  • Administers a given digital test following institutional guidelines.
Intermediate
Designs diverse digital and AI-powered assessment formats, selecting strategies that enhance feedback quality based on learning objectives .
Examples:
  • Combines multiple assessment methods (e.g., AI-generated quizzes, portfolios, peer review), selecting based on learning objectives.
  • Configures AI assessment tools to provide immediate, scaffolded feedback adapted to learner needs.
  • Selects and adapts digital assessment tools, including AI, to fit specific learning objectives and adult learner contexts.
Advanced
Develops innovative digital and AI-powered assessment strategies, appraising them for authenticity and fairness and advising colleagues on best practices .
Examples:
  • Appraises the suitability and fairness of digital assessment tools, including potential biases.
  • Develops new and innovative assessment formats that leverage AI capabilities.
  • Serves as a source of inspiration and advice for colleagues on digital and AI-powered assessment.
2.2. Analytics & Feedback
Using data and analytics from digital and AI tools to provide feedback and improve learning.
Basic
Interprets basic data from digital tools (e.g., quiz scores, completion rates), applying this information to provide feedback following established procedures .
Examples:
  • Accesses and uses the markbook feature of an LMS, following established procedures.
  • Identifies which learners have completed assignments by examining LMS gradebook data.
  • Provides generic, whole-class feedback based on areas for improvement identified by an AI tool.
Intermediate
Analyses digital and AI-powered analytics to identify learning patterns, providing timely, targeted feedback adapted to individual learner needs .
Examples:
  • Analyses digital data from AI-powered dashboards to identify learner needs and adapt teaching interventions.
  • Uses AI tools to provide specific and actionable feedback on learner work.
  • Monitors learner progress over time using data from digital platforms, adapting support accordingly.
Advanced
Develops ethical, AI-driven feedback systems, scrutinising data for bias and guiding colleagues in analytics-informed practice .
Examples:
  • Scrutinises data generated by learning platforms and AI systems for validity and potential bias.
  • Configures and customises learner-facing dashboards to translate AI analytics into actionable insights.
  • Guides other educators on using AI-powered learning analytics to improve their practice.
2.3. Learner Participation
Involving learners in the AI-enhanced assessment process.
Basic
Uses digital platforms for assessment submission and return, applying structured processes according to institutional procedures .
Examples:
  • Uses a digital platform for learners to submit assignments, applying institutional procedures.
  • Responds to learner queries about assessment requirements using digital communication tools.
  • Organises assessment materials and deadlines within the LMS for learner access.
Intermediate
Facilitates learner participation through AI-assisted peer review and self-assessment, integrating choice in assessment topics or formats.
Examples:
  • Implements structured peer-assessment activities using AI-powered digital tools.
  • Facilitates learner self-assessment using AI-generated feedback as a starting point for reflection.
  • Gathers learner input on assessment preferences and incorporates feedback into assessment design.
Advanced
Empowers learners as assessment co-designers through collaborative rubric development and shared ownership of assessment processes .
Examples:
  • Facilitates learner-led rubric design sessions where adult learners define assessment criteria based on professional needs.
  • Enables learners to choose their own assessment formats, including AI-generated artefacts.
  • Establishes a culture where assessment is seen as a tool for learning, with AI as a supportive partner.
2.4. Ethical Data Use
Using learner data ethically and responsibly in AI-supported systems.
Basic
Complies with institutional data privacy and security policies, identifying personal and sensitive learner data in AI systems .
Examples:
  • Verifies AI tool privacy settings before use to identify what learner data is collected.
  • Applies established rules for handling learner data (e.g., not sharing passwords) when using AI platforms.
  • Uses institutional platforms for storing and communicating about learner data.
Intermediate
Applies data protection principles proactively, ensuring transparency in AI use and anonymising learner data for analysis .
Examples:
  • Implements strategies to protect learner data when using third-party digital tools.
  • Communicates clearly and accurately to learners about how their data is used by AI systems.
  • Anonymises or de-identifies learner data before using it for analysis.
Advanced
Advocates for institutional AI data policies, appraising AI systems for fairness and involving stakeholders in ethical decision-making .
Examples:
  • Collaborates with technical specialists to audit AI assessment systems for bias, interpreting findings for educational implications.
  • Formulates and advocates for institutional policies ensuring fairness, transparency, and accountability in AI use.
  • Involves learners, colleagues, and stakeholders in continuous dialogue about data ethics and AI.

3. Digital Literacy & Resources

Creating, curating, and managing digital/AI resources, and fostering AI literacy

3.1. Digital & AI Literacy Development
Teaching learners foundational digital and AI literacy skills and developing their understanding of AI and emerging technologies.
Basic
Explains digital tools and foundational AI concepts to learners, demonstrating functional skills using provided resources and examples .
Examples:
  • Demonstrates basic digital tools to learners (e.g., how to navigate an LMS, use video conferencing features).
  • Explains what AI is through simple, everyday examples using provided resources.
  • Describes the main benefits and risks of common digital technologies.
Intermediate
Facilitates learners' critical use of digital and AI tools for problem-solving, guiding them to evaluate social and ethical implications systematically .
Examples:
  • Designs activities for learners to practise and reflect on their digital skills.
  • Guides learners on using AI tools for specific purposes (e.g., brainstorming, studying, content creation).
  • Facilitates learners' critical thinking about social and ethical implications of AI through structured analysis.
Advanced
Develops curriculum frameworks that systematically build learners' capacity as responsible digital innovators, advancing new strategies within the professional field .
Examples:
  • Architects learning programmes that progressively develop learners' digital innovation capabilities.
  • Creates learning activities that help learners critically examine AI outputs and understand model limitations.
  • Enables learners to become co-designers of digital solutions and responsible innovators with AI.
3.2. Critical Evaluation & Information Quality
Evaluating the quality of digital resources and teaching learners to critically evaluate digital and AI-generated information and media.
Basic
Applies established criteria (e.g., currency, authority, accuracy) to evaluate digital and AI-generated information, using evaluation frameworks with guidance .
Examples:
  • Applies basic checks to identify potential misinformation from any source, including AI, using provided frameworks.
  • Demonstrates source evaluation process with learners using think-aloud protocols with guidance.
  • Explains the importance of questioning the source of information before using it.
Intermediate
Evaluates digital and AI-generated content systematically for accuracy and bias, instructing learners in developing independent evaluation strategies.
Examples:
  • Uses a set of criteria to evaluate the quality and reliability of digital resources systematically.
  • Implements activities specifically designed to develop learners' critical evaluation skills for all types of media.
  • Instructs learners on identifying different forms of bias and propaganda in online and AI-generated content.
Advanced
Enables learners to autonomously evaluate complex digital and AI-generated information sources, enriching professional discourse on information and AI literacy.
Examples:
  • Reflects critically on and develops new strategies for teaching information and AI literacy.
  • Facilitates learner-led creation of evaluation toolkits (e.g., checklists, decision trees, verification guides) shared with peers.
  • Advances professional discourse on challenges of misinformation and AI through publications or presentations.
3.3. Resource Selection & Curation
Selecting and curating digital and AI-enhanced resources for teaching and learning.
Basic
Locates and selects ready-made digital resources for immediate teaching needs, organising them for future use following established methods .
Examples:
  • Uses search engines to locate digital resources for a lesson.
  • Selects resources based on their suitability for a specific activity, applying selection criteria.
  • Bookmarks or saves resources, including AI tools, in a personal list for later use.
Intermediate
Curates organised collections of digital resources, evaluating each for pedagogical value and sharing with colleagues .
Examples:
  • Uses advanced search operators and evaluates AI-curated recommendations critically before adding to collections.
  • Evaluates resources, including AI-generated content, against pedagogical and technical criteria.
  • Organises and shares curated collections of resources with colleagues using appropriate tools.
Advanced
Develops collaborative curation systems and strategies, enriching the professional community with high-quality resources and guiding ethical AI use .
Examples:
  • Develops strategies for collaboratively curating, managing, and sustaining resource collections.
  • Enriches the professional community by reviewing and sharing digital resources, including AI tools.
  • Appraises and advises on the use of AI-powered curation tools and platforms.
3.4. Resource Creation
Creating and adapting digital and AI-enhanced resources.
Basic
Uses familiar tools to create digital resources by modifying existing content, applying basic AI features with appropriate attribution .
Examples:
  • Makes simple modifications to existing resources (e.g., editing a worksheet) using familiar tools.
  • Uses basic features of common tools (e.g., presentation software with AI design suggestions).
  • Attributes sources when using third-party content, including AI-generated elements, applying citation guidelines.
Intermediate
Designs accessible, interactive digital resources, integrating multiple formats and AI tools while applying accessibility and inclusivity principles .
Examples:
  • Designs interactive resources by combining different formats (e.g., text, video, quizzes) with AI assistance.
  • Adapts and remixes openly licensed resources, evaluating legal and ethical implications of AI-generated content.
  • Applies accessibility principles to ensure resources are usable by all learners, using AI tools to assist.
Advanced
Develops innovative, high-quality digital resources leveraging AI, disseminating openly and coaching others in resource creation .
Examples:
  • Develops complex, high-quality, and innovative digital educational resources with AI.
  • Models transparent human-AI collaborative resource creation, documenting the iterative process for professional sharing.
  • Develops templates or guidelines to enable colleagues and learners to create resources responsibly with AI.
3.5. Ethics & Licensing
Understanding and applying copyright and licensing in relation to digital and AI-generated resources.
Basic
Applies copyright rules when using and sharing digital resources, distinguishing between copyrighted, openly licensed, and AI-generated content according to guidelines .
Examples:
  • Explains the difference between copyrighted and openly licensed content, acknowledging uncertainty around AI ownership.
  • Attributes the source of third-party resources used in teaching, applying guidelines correctly.
  • Informs learners when AI tools are being used in teaching or assessment, following institutional guidelines.
Intermediate
Applies licensing frameworks responsibly when managing digital resources, instructing learners about copyright, plagiarism, and ethical AI use.
Examples:
  • Determines which licence to apply to their own resources, including AI-generated components.
  • Implements strategies to instruct learners about copyright, licensing, academic honesty, and ethical AI use.
  • Organises content systematically, applying appropriate copyright protocols and privacy protections.
Advanced
Formulates institutional policies for copyright and ethical AI use, advising colleagues on licensing complexities and advocating for open practices .
Examples:
  • Formulates and advocates for institutional policies on ethical use of copyrighted, openly licensed, and AI-generated resources.
  • Develops strategies and resources to help colleagues and learners understand licensing in the age of AI.
  • Serves as an expert advisor on complex copyright and licensing issues, including those surrounding AI.
3.6. Accessibility & Sustainability
Ensuring that digital and AI-enhanced resources are accessible and sustainable.
Basic
Applies basic accessibility features (e.g., alt-text, captions) to digital resources, responding to learner requests using AI tool assistance .
Examples:
  • Explains what digital accessibility means to learners.
  • Adds alt-text to images and captions to videos when creating or modifying resources, with guidance.
  • Provides alternative formats for learners when requested, using AI tool assistance.
Intermediate
Designs accessible resources proactively using inclusivity principles, selecting AI tools that support inclusivity and cultural relevance .
Examples:
  • Applies accessibility principles proactively in designing digital resources, using AI tools to assist.
  • Uses accessibility checkers and other tools to ensure resources are compliant.
  • Examines cultural relevance and inclusivity of resources, being mindful of biases in AI.
Advanced
Advocates for institutional accessibility policies, coaching colleagues in inclusive design and evaluating AI tools for equity and access .
Examples:
  • Evaluates AI tools using accessibility testing frameworks before recommending for institutional adoption.
  • Coaches colleagues on creating accessible resources and inclusive learning environments with AI.
  • Advances communities of practice focused on making digital resources more accessible.

4. Professional Engagement

How adult educators use digital and AI tools for collaboration, communication, reflection, and professional development

4.1. Collaboration & Networking
Using technology-enhanced platforms to connect with peers, share resources, and build professional communities.
Basic
Uses digital communication tools for professional collaboration, participating in online communities focused on AI in adult education by reading and responding to discussions .
Examples:
  • Uses email, chat, or standard collaboration tools to communicate with colleagues.
  • Joins an online community or forum focused on AI in adult education, reading posts to stay informed.
  • Participates in online professional communities by reading discussions and responding within their expertise area.
Intermediate
Contributes actively to professional networks, sharing resources and collaborating on projects using digital tools.
Examples:
  • Shares resources and information about AI with others in online communities.
  • Collaborates with peers on shared projects using digital tools, including AI for brainstorming or co-writing.
  • Provides and incorporates constructive feedback in a professional network.
Advanced
Shapes professional communities strategically, initiating collaborative projects and guiding colleagues in effective AI use .
Examples:
  • Initiates, facilitates, and sustains online communities of practice focused on AI in education.
  • Leads collaborative projects to create and share resources about AI with a wider community.
  • Reflects critically on and develops strategies for effective online collaboration with AI tools.
4.2. Professional Learning & Growth
Engaging in online professional development and using digital and AI tools to enhance professional practice.
Basic
Participates in guided professional development on digital tools, identifying personal skill gaps with guidance .
Examples:
  • Applies recommendations from a supervisor to attend a specific webinar on AI in education.
  • Describes their digital competence needs, including AI skills, to a peer or mentor.
  • Learns from colleagues through informal digital exchanges (e.g., discussion threads, shared resource comments).
Intermediate
Selects relevant professional development independently, integrating digital and AI learning into practice and reflecting on impact .
Examples:
  • Selects and participates in online courses or communities to address a self-identified skill gap in AI.
  • Completes self-selected online courses on AI in education, applying new concepts and documenting lessons learned.
  • Curates and shares useful professional learning resources about AI with their immediate team.
Advanced
Architects professional development programmes on digital and AI practices, coaching colleagues and advancing field innovations .
Examples:
  • Appraises and advances contemporary digital andragogies, including ethical use of AI.
  • Architects and leads innovative professional learning programmes on AI for other educators.
  • Guides other educators and serves as an expert source on digital and AI education practices.
4.3. Reflection & Leadership
Using digital and AI tools for self-reflection and demonstrating leadership in digital contexts.
Basic
Records reflections on teaching practice using digital tools, describing strengths and areas for improvement after lessons .
Examples:
  • Uses digital notes or a simple document to record thoughts after a lesson.
  • Describes the strengths and areas for improvement of a digital tool used.
  • Searches periodically for their professional digital footprint to monitor their online presence.
Intermediate
Analyses teaching practice systematically using digital evidence and AI analytics, adapting approaches based on documented insights .
Examples:
  • Maintains a digital portfolio or reflective blog with evidence of practice, including AI use.
  • Uses data from digital platforms and AI tools (e.g., LMS analytics) to inform reflection.
  • Shares reflections and discusses challenges with a professional peer group.
Advanced
Leads institutional reflective practice culture using AI-driven analytics, coaching colleagues in data-informed reflection and driving pedagogical innovation .
Examples:
  • Develops and implements strategies for collaborative, data-informed reflection within a team using AI tools.
  • Disseminates reflective case studies of AI implementation, presenting at conferences, publishing in professional outlets, or leading institutional showcases.
  • Coaches colleagues in developing their own systematic reflective practices with AI.
4.4. Ethical Engagement
Applying ethical principles in all digital and professional interactions, especially concerning AI.
Basic
Complies with digital conduct, safety, and data privacy standards, using basic security measures and maintaining professional boundaries .
Examples:
  • Attributes ownership of digital content (e.g., citing sources) applying guidelines.
  • Safeguards their own and others' personal data by using basic security measures.
  • Maintains a clear distinction between personal and professional digital profiles.
Intermediate
Applies ethical principles proactively in digital practice, selecting AI tools based on ethical implications and guiding learners in responsible use .
Examples:
  • Selects digital tools consciously based on their ethical implications.
  • Implements strategies to promote respectful and inclusive online behaviour among learners.
  • Guides learners in understanding the ethical dimensions of using digital tools.
Advanced
Shapes institutional digital ethics and AI policies, facilitating stakeholder dialogue and modelling transparent, equitable practices .
Examples:
  • Appraises emerging ethical challenges related to AI and advances professional discourse.
  • Formulates and advocates for institutional policies promoting digital ethics, equity, and safety in the age of AI.
  • Coaches colleagues in navigating complex ethical dilemmas in their digital practice with AI.

Comprehensive Glossary

This glossary provides definitions for key terms used throughout the framework.

Accountability (in AI)
The principle that humans remain responsible for the outcomes and impacts of AI systems.
Adaptive learning systems
AI-powered systems that adjust content, pace, or difficulty based on individual learner performance and needs.
AI analytics / Learning analytics
The collection, analysis, and reporting of data about learners and their contexts to understand and optimise learning.
AI anxiety
Concern or stress related to the use or implications of AI technologies.
AI chatbots
AI-powered conversational interfaces that can respond to queries, provide information, or facilitate learning interactions.
AI hallucination
When AI systems generate plausible-sounding but factually incorrect or fabricated information.
AI literacy
The knowledge and skills required to understand, use, and critically evaluate AI technologies and their outputs.
AI teaching assistants
AI tools that support educators by automating tasks such as answering student queries, providing feedback, or generating learning materials.
AI tools
Tools where artificial intelligence capabilities are the primary function — particularly generative AI (e.g., ChatGPT, Gemini, Claude), adaptive learning systems, and AI analytics.
AI-generated content
Text, images, audio, video, or other materials created by artificial intelligence systems.
Algorithmic bias
Systematic errors in AI outputs that reflect prejudices in training data or system design, potentially leading to unfair outcomes.
Alt-text (Alternative text)
Descriptive text added to images to convey their content to users who cannot see them (e.g., screen reader users).
Andragogy / Andragogical
The theory and practice of adult learning and education. Andragogical approaches recognise that adult learners are self-directed, bring prior experience, and are motivated by relevance to their professional or personal goals.
Anonymisation / De-identification
Removing or obscuring personal identifiers from data so individuals cannot be identified.
Asynchronous learning
Learning that occurs at different times for different learners, allowing flexibility (e.g., recorded lectures, discussion forums, self-paced modules).
Attribution
Giving credit to the original creator when using their work, as required by many open licences.
Authentic assessment
Assessment tasks that reflect real-world applications and contexts relevant to learners' professional or personal lives.
Blended learning
An instructional approach combining face-to-face teaching with online/digital learning activities.
Bloom's Revised Taxonomy
A hierarchical classification of cognitive skills (Remember, Understand, Apply, Analyse, Evaluate, Create) used to structure learning objectives.
Captions
Text displayed on video content that transcribes spoken dialogue and relevant audio information.
Collaborative learning
An educational approach where learners work together in groups to solve problems, complete tasks, or create knowledge.
Community of practice
A group of people who share a professional interest and learn from each other through regular interaction.
Competency
A combination of knowledge, skills, and attitudes required to perform effectively in a specific context.
Continuing Professional Development (CPD)
Ongoing learning activities that professionals undertake to maintain and enhance their skills and knowledge.
Copyright
Legal rights that protect original creative works from unauthorised use or reproduction.
Creative Commons licensing
A standardised system of licences that allows creators to grant permissions for others to use their work under specified conditions.
Critical evaluation
The systematic assessment of information sources for credibility, accuracy, relevance, and potential bias.
Curation
The process of selecting, organising, and maintaining collections of digital resources for specific educational purposes.
Currency, Authority, Accuracy
Common criteria for evaluating information quality: how recent it is, the credibility of the source, and factual correctness.
Data privacy
The right of individuals to control how their personal information is collected, used, and shared.
Data protection
Policies and practices that safeguard personal data from unauthorised access, use, or disclosure.
Differentiated instruction
Tailoring teaching methods, content, or assessments to meet diverse learner needs, abilities, and preferences.
Digital accessibility
Designing digital content and tools so they can be used by people with diverse abilities, including those with disabilities.
Digital divide
The gap between those who have access to digital technologies and those who do not.
Digital fatigue
Exhaustion or burnout resulting from excessive use of digital devices and platforms.
Digital footprint
The trail of data and online presence left by an individual's digital activities.
Digital literacy
The ability to find, evaluate, use, and create digital content effectively and responsibly.
Digital portfolio
A collection of digital evidence (e.g., artefacts, reflections, achievements) demonstrating professional competence and growth.
Digital tools
Technology-based applications and software used in teaching and learning, including tools with embedded AI features (e.g., LMS platforms, presentation software, collaboration tools).
Digital wellbeing
A state of health and balance in one's relationship with digital technologies, avoiding negative impacts like fatigue or anxiety.
Disinformation
Deliberately false information spread with intent to deceive or manipulate.
Domain / Strand
A major category of competencies in the framework (e.g., Assessment, Digital Literacy & Resources).
Dreyfus Model of Skill Acquisition
A model describing five stages of expertise development: Novice, Advanced Beginner, Competent, Proficient, Expert.
Ethics by design
Integrating ethical considerations into the design and development of technologies from the outset.
Formative assessment
Ongoing assessment used during instruction to monitor learner progress and inform teaching adjustments.
Generative AI
AI systems that can create new content such as text, images, audio, or video based on patterns learned from training data (e.g., ChatGPT, Gemini, DALL-E).
Human-centred AI
An approach to AI development and use that prioritises human values, rights, agency, and wellbeing.
Inclusive design
Creating products, services, and environments that are usable by as many people as possible, regardless of ability or circumstance.
Information literacy
The ability to identify, locate, evaluate, and effectively use information from various sources.
Informed consent
Agreement given by individuals after being clearly informed about how their data will be used.
Learner-centred facilitation
An approach that prioritises learner needs, interests, and active participation over instructor-led content delivery.
Learning Management System (LMS)
A software platform used to create, deliver, manage, and track educational content and learner progress (e.g., Canvas, Moodle, Blackboard).
Misinformation
False or inaccurate information spread without intent to deceive.
Open Educational Resources (OER)
Teaching, learning, and research materials that are freely available for use, adaptation, and redistribution.
Openly licensed content
Materials released under licences that permit free use, sharing, and often modification (e.g., Creative Commons).
Peer coaching / Mentoring
A collaborative professional development approach where colleagues support each other's learning and growth.
Peer feedback / Peer assessment
A process where learners evaluate and provide feedback on each other's work.
Platforms
Digital environments or ecosystems that host multiple tools and enable user interactions (e.g., LMS platforms like Canvas or Moodle, collaboration platforms like Microsoft Teams).
Progression levels
Stages of competency development (Basic → Intermediate → Advanced) indicating increasing expertise and autonomy.
Reflective practice
The process of systematically examining one's own teaching experiences to improve future practice.
Rubric
A scoring guide that defines criteria and performance levels for assessing learner work.
Scaffolded learning / Scaffolding
Providing structured support to learners that is gradually removed as they develop competence.
Self-assessment
The process of learners evaluating their own performance, progress, or learning against defined criteria.
Sub-strand
A specific area of competency within a broader domain (e.g., "Design & Planning" within "Teaching, Learning & Empowering Learners").
Summative assessment
Assessment conducted at the end of a learning period to evaluate overall achievement against standards.
Synchronous learning
Learning that happens in real-time, where participants interact simultaneously (e.g., live webinars, video conferences).
Transparency (in AI)
The practice of openly communicating how AI systems work, what data they use, and how decisions are made.
Universal Design for Learning (UDL)
A framework for designing flexible learning experiences that accommodate diverse learner needs from the outset.