AI Didactics: Keeping Control Across the 6 AI Domains
AI is entering education through explanations, exercises, feedback, learning pathways and school administration. This growth brings opportunities - but also one essential question: How do we make sure AI strengthens learning, without taking over thinking or decision-making?
The answer lies in AI didactics: the professional choices teachers and school teams make whenever AI plays a role in lessons, assessment, communication or school policy.
TL;DR Summary
The 6 AI domains show where AI in education is used. AI didactics determines how schools and teams use AI safely, purposefully and human-centrically.
Domains= practice; Didactics = professional choices.
What AI Didactics Means
AI didactics requires teachers and school leadership to consciously decide:
- Why AI is used: aligned with learning goals and school vision
- How AI is integrated: task design, guidance, gradual support
- What AI is allowed to generate: quality, nuance, reliability
- When human judgement is essential
- What pupils must learn about AI: critical, safe and responsible use
AI didactics is not about tools - it is about professional practice.
See also: AIEd Bloom - which thinking steps remain fundamentally human?
AI Didactics Across the 6 AI Domains
1. Lesson Planning & Learning Material Development
AI provides ideas, explanations and draft materials. Teachers link these outputs to learning goals, check accuracy and quality, adapt them to class needs and work transparently when AI has contributed.
School leadership ensure clear quality standards and transparency agreements.
Practical example:
AI generates 10 practice questions → the teacher selects the best 3, rewrites them and checks for misconceptions.
Didactic outcome: The design of learning always remains with the teacher.
2. Assessment, Feedback & Evaluation
AI can detect error patterns and generate preliminary feedback. Teachers decide when AI may support marking, evaluate the accuracy and usefulness of AI feedback, and teach pupils to check AI-generated comments critically.
School leadership safeguard assessment reliability, fairness and privacy.
Practical example:
AI writes a feedback → the teacher refines nuance and removes incorrect assumptions.
Didactic outcome: Judgement and assessment remain human responsibilities.
3. Differentiation & Personalised Learning
AI can suggest levels, pacing and learning routes. Teachers judge whether a route makes didactic sense, adjust proposals based on motivation and wellbeing, and ensure AI-generated pathways do not increase inequality.
School leadership secure policies on equity and accessibility.
Practical example:
AI suggests acceleration → the teacher sees signs of stress and chooses repetition, clarity and calm.
Didactic outcome: AI personalises; human professionals safeguard fairness.
4. Learner Support & Instructional Help
AI offers explanations, examples and simulations. Teachers teach pupils to approach AI explanations critically, use AI to support thinking steps (not replace them), offer step-by-step guidance in multimodal tasks and safeguard safe data input.
School leadership ensure safe processes and clear support arrangements.
Practical example:
A pupil asks AI for a maths solution → the teacher asks the pupil to explain why the solution is correct.
Didactic outcome: AI helps pupils, but teachers ensure they actually think.
5. Communication, Organisation & Administration
AI can create draft letters, notes and summaries. Teachers decide when AI is appropriate, correct tone and content, check for nuance, and communicate transparently with parents and pupils.
School leadership ensure quality standards and privacy requirements.
Practical example:
AI writes a class notice → the teacher rewrites unclear phrasing and adjusts the tone.
Didactic outcome: AI drafts; humans safeguard clarity and relationships.
6. School Development, Policy & Safeguarding
AI can produce dashboards and highlight trends. Teams and school leadership align AI use with their learning vision, decide how AI fits within subject didactics and school strategy, train staff in critical AI use, and evaluate through PDCA whether AI genuinely improves learning.
Practical example:
A dashboard shows declining engagement → the team links this to observations and adjusts instruction or support.
Didactic outcome: AI supports insight; schools develop and decide.
| Domain | Key AI-didactic responsibility |
|---|---|
| Lesson Planning | Goal-aligned design & quality control |
| Assessment | Reliability & human judgement |
| Differentiation | Equity & pedagogical interpretation |
| Support | Gradual guidance & critical thinking |
| Communication | Accuracy, tone & transparency |
| School Development | Vision, training & quality assurance |
Meta Insight
The six domains show where AI enters education. AI didactics determines how, within each domain:
- AI is used for what it does well,
- human judgement and pedagogical wisdom remain central,
- risks are mitigated,
- learning stays the focus.
In other words:
“AI for learning - never in place of learning”
From Insight to Practice
Would you like to strengthen your team's AI-didactic skills - from lesson design and assessment to safe input, quality control and school-wide vision?
Symbio6 supports schools with team training, practical formats and professional guidance to help you use AI wisely, human-centrically and learning-aligned. We'd be happy to explore what your next step could be.