AI & Digital Literacy for Educators
Judgement. Evidence. Responsible Practice.
The AI & Digital Literacy for Educators (AIDLE) programme is a progressive professional development journey designed to help educators use artificial intelligence critically, responsibly and effectively.
The programme does not focus on collecting AI tools or learning particular platforms. Instead, it develops the professional judgement required to decide whether AI-generated information can be trusted, whether a technology should be used, how AI should enter learning and assessment, how learners and data should be protected, and when a decision must remain with an authorised human.
Across eight modules, participants move from understanding how generative AI works and where it can fail, through evaluating technologies, protecting learning, redesigning assessment, safeguarding learners and managing data responsibly, to creating a governed personal AI-in-Practice Plan.
The RezOne Difference
AI literacy should not be reduced to knowing how to write prompts. Educators also need to recognise where AI-generated information may be unreliable, where automation may weaken learning, where technology may introduce new risks, and where professional authority cannot legitimately be delegated.
Claims about AI are investigated rather than accepted through enthusiasm or suspicion
Technology remains subordinate to educational purpose and professional responsibility
AI may assist, but professional decisions remain attributable to authorised humans
Frameworks are applied to authentic educational scenarios and professional practice
Efficiency is evaluated according to the learning it preserves or removes
Professional functions are prioritised over rapidly changing products and brands
The objective is not to produce educators who use more AI. It is to develop educators who know when, why and under what conditions AI deserves to be used.
Your AIDLE Learning Journey
AIDLE is intentionally cumulative. Each module introduces a distinct professional question, control and boundary, progressively strengthening the educator’s ability to move from technological capability to defensible professional judgement.
The Full AIDLE Progression
The programme develops eight connected professional capabilities.
Can I trust what the AI generated?
Should I use this AI tool at all?
Is its use entitled to enter this learning experience?
Exactly where should AI enter — and where must it stop?
What entitles me to attribute this demonstrated capability to the learner?
Is this appropriate, fair and safe for these particular learners?
Is this decision mine to make?
What will I delegate, what will I refuse, and how will I remain accountable?
Module 1 · What AI Is, and What It Is Not
Participants begin by developing an accurate understanding of how generative AI produces language and why fluent output must not be confused with knowledge, understanding or factual reliability.
- Understand the predictive foundations of Large Language Models
- Distinguish fluency from factual accuracy
- Recognise fabrication, confident error, sycophancy, staleness and silent drift
- Understand why asking AI to verify itself is not independent verification
- Apply the Human Gate before AI-generated material influences teaching or learner understanding
Key principle: Generation is not verification. Fluency is not evidence.
Module 2 · The AI Tool Landscape in Education
Participants move from being software consumers to structured professional evaluators. Technologies are considered according to educational function rather than brand.
- Purpose: What named educational problem does this solve?
- Safeguarding: Could a learner be harmed through this use?
- Data Position: What happens to information entered into the system?
- Cost: What is the actual financial and operational cost?
- Deskilling: What professional capability could diminish through automation?
Participants reach one of four defensible outcomes: USE · USE WITH CONDITIONS · ESCALATE · REJECT.
Key principle: Do not begin with the tool. Begin with the educational purpose.
Module 3 · Critical Digital Pedagogy
This module examines whether technology is genuinely entitled to enter a learning experience. Participants distinguish between teaching WITH, ABOUT and AGAINST technology.
- Identify who benefits from a proposed technology use
- Ask what activity or effort is being removed
- Distinguish useful efficiency from displacement of meaningful learning
- Protect productive difficulty where it contributes to capability
- Recognise when automation creates professional distance from learner thinking
Critical questions: Saved for whom? Saved from what? What fills the newly empty space?
Key principle: Technological efficiency is not automatically educational effectiveness.
Module 4 · Curriculum Integration
Participants move from evaluating AI to deliberately designing learning sequences in which AI has a precise, bounded and pedagogically justified role.
- Protect the original learning objective through the Unmoved Objective
- Distinguish productive difficulty from accidental difficulty
- Specify exactly what AI will do, who will use it and for how long
- Identify where AI use must deliberately stop
- Preserve pupil voice, independent reasoning and diagnostic integrity
- Model transparent and responsible AI disclosure
Key principle: AI may change the route. It must not quietly change the learning destination.
Module 5 · Assessment, Integrity and the Limits of Detection
AIDLE approaches generative AI in assessment as a question of validity and evidence, rather than relying on technological suspicion or automated detection.
- Distinguish a submitted product from evidence of independent learner capability
- Understand why AI-detection scores are not proof of authorship or misconduct
- Identify where assessment design creates a Product–Capability Gap
- Use process evidence, controlled evidence, oral defence and comparative critique
- Respond to suspected misuse through evidence gathering and professional dialogue
Core framework: Construct → Evidence → Attribution → Judgement.
Key principle: Assessment integrity is protected by valid evidence, not technological suspicion.
Module 6 · Bias, Ethics and Safeguarding
Participants adopt a pupil-first approach to auditing AI-generated educational material and learner-facing AI use.
- Examine representation within AI-generated material
- Identify assumptions embedded within content
- Recognise meaningful absences or omissions
- Judge suitability for the actual learners involved
- Identify potential harm and safeguarding implications
- Recognise when an AI-related concern must enter an established safeguarding route
Audit framework: Representation → Assumptions → Absences → Suitability → Harm.
Key principle: The relevant question is not simply “Is this AI biased?” but “Is this material appropriate, fair and safe for these learners?”
Module 7 · Pupil Data and Data Protection
This module develops the ability to recognise data-protection risks arising from the use of generative AI and, crucially, to recognise when the decision is no longer the educator’s to make.
- Identify precisely what data would enter an AI system
- Establish whose information is involved
- Investigate the relevant processing position
- Recognise lawful-basis and organisational-authority questions
- Distinguish recognising a problem from possessing authority to resolve it
- Escalate appropriately through the organisation’s authorised process
Core framework: Purpose → Data → Processing Position → Lawful Basis → Authority → Escalation.
Key principle: Professional competence includes knowing when the decision is no longer yours to make.
Module 8 · Your AI-in-Practice Plan
The final module brings the programme together by converting AIDLE principles into a specific, defensible and actionable professional AI plan.
- Identify a small number of specific professional AI uses
- Define the exact task or workflow position being delegated
- Specify human verification and professional accountability
- Align assessment, safeguarding and data-protection boundaries
- Identify at least one genuinely useful task that will deliberately remain human
- Schedule review so that approval does not silently become permanent trust
Core framework: Purpose → Task → Boundary → Verification → Accountability → Review.
Key principle: A professional AI plan defines not only what technology will do, but what it will never be allowed to decide.
The Evidence-to-Judgement Cycle
AIDLE does not teach educators what to believe about artificial intelligence. It teaches participants how to investigate a claim, gather relevant evidence, challenge assumptions and determine what conclusion the evidence actually supports.
What do I currently think?
What needs to be tested?
What evidence should I examine?
What is actually supported?
What contradicts or qualifies my assumptions?
What conclusion is professionally justified?
What should I do?
What evidence would change my decision?
Neither technological enthusiasm nor technological scepticism is evidence. AIDLE develops the professional discipline to allow evidence to constrain the decision.
The Human Decisions Framework™
Throughout the programme, the Human Decisions Framework™ keeps professional judgement, accountability and authority with the educator.
- AI may generate information, but generation is not verification
- AI may identify patterns, but signals are not automatically evidence
- AI may make recommendations, but recommendations do not create authority
- AI may support reasoning, but professional judgement remains human
- AI may automate tasks, but accountability cannot automatically be delegated
- Decisions beyond the educator’s authority must be escalated through the appropriate human process
Human judgement remains the governing layer.
How You Will Learn
AIDLE combines structured course content with investigation, practical activities, professional reflection and application to authentic educational situations.
Structured materials develop each module’s core concepts and professional distinctions
AI supports questioning, evidence gathering and reflection without replacing the learner’s judgement
Apply AIDLE frameworks to realistic teaching, assessment, safeguarding and data decisions
Build evidence of professional reasoning progressively across the programme
Access guidance where clarification, feedback or professional support is required
Examine how evidence changes your professional judgement and future practice
Portfolio & Assessment
Assessment focuses on professional judgement rather than technological sophistication. Participants progressively build evidence demonstrating how they investigate claims, evaluate technology, justify educational decisions and retain appropriate professional accountability.
- Authentic activities drawn from professional educational practice
- Evidence-based evaluation rather than unsupported opinion
- Clear distinction between claims, evidence, judgement and decisions
- Justified use, conditional use, redesign, escalation or rejection where appropriate
- Reflection on what evidence would change a professional decision
- Final AI-in-Practice Plan demonstrating governed professional use
The programme deliberately allows negative decisions. A technology may be rejected where the evidence does not justify its use. Recognising that a decision requires escalation is also treated as evidence of professional competence.
Who This Programme Is For
AIDLE is designed for educators and education professionals who want a practical, critical and professionally responsible approach to artificial intelligence.
- Teachers across primary, secondary, further and adult education
- Teaching assistants and learning-support professionals
- Curriculum and subject leaders
- Digital, IT and educational-technology leaders
- Assessment and quality-assurance professionals
- School and college leaders
- Safeguarding, inclusion and pastoral professionals
- Educators responsible for introducing or evaluating AI within professional practice
Programme Delivery
The programme is delivered online and is designed to combine structured learning with flexibility for practising educators.
| Programme Element | Delivery |
|---|---|
| Programme structure | 8 progressive professional learning modules |
| Learning format | Online blended professional development |
| Learning activities | Interactive content, investigation, professional scenarios and reflection |
| Portfolio | Progressive professional-practice evidence across the programme |
| AI support | Governed AI Learning Coach operating within the AIDLE learning framework |
| Human support | Tutor, assessment and learner-support routes where required |
Frequently Asked Questions
Do I need technical AI experience before starting?
No. The programme begins by establishing an accessible professional understanding of what generative AI is, how it produces outputs and why those outputs require appropriate verification.
Does the programme teach specific AI products?
AIDLE is deliberately organised around professional functions rather than commercial brands. Products and interfaces change quickly; professional educational responsibilities endure.
Will I be expected to use AI?
Participants will examine and use AI where this serves the learning purpose, but the programme does not assume that AI should always be adopted. Deliberate non-use, restriction, escalation and rejection are legitimate professional outcomes.
Can I use an AI assistant while completing the programme?
Yes, within the programme’s AI-use boundaries. AI may support questioning, investigation and reflection, but it must not replace the independent professional judgement or evidence the learner is expected to demonstrate.
Does AIDLE use AI detectors to determine learner authorship?
No. The programme teaches that statistical AI-detection scores should not be promoted into proof of authorship or misconduct. Assessment integrity is approached through valid evidence, professional dialogue and appropriate assessment design.
What will I produce by the end of the programme?
Participants develop a professional portfolio culminating in an AI-in-Practice Plan defining appropriate uses, human verification, meaningful refusals, accountability and review.
AI is changing education quickly. Professional judgement must develop with it. Join RezOne British Academy to build the confidence, evidence literacy and professional boundaries required to use AI responsibly in educational practice.