It differs from general-purpose AI use because every output is anchored to two things: an explicit instructional model such as Bloom’s taxonomy or Depth of Knowledge, and a named curriculum reference such as a NERDC performance objective. The teacher edits and signs off, and the system records that they did.
The problem in Nigerian classrooms
Two things are true at once about lesson notes in Nigerian schools. They are a genuine professional discipline, and they are also, in a great many schools, a weekly ritual performed for inspection rather than for teaching. A teacher with five classes and 60 pupils in each writes what the head of department will sign, not what will change Thursday’s lesson.
Generative AI arrived into that gap and immediately made it worse in a specific way. A chatbot will produce a lesson note that is fluent, well-formatted, plausible, and disconnected from the NERDC performance objective the teacher is actually accountable for. It reads better than what it replaced. It teaches no better, and often worse, because six activities sit at the same cognitive level and nothing checks understanding.
Pedagogy with AI is the narrow middle position: the model drafts, the teacher decides, and the record shows who decided.
Definition
Pedagogy with AI is the use of artificial intelligence to plan, adapt and evidence teaching while the teacher retains professional judgment and accountability. Three conditions separate it from unstructured AI use:
- Model-anchored. Output is generated against a stated instructional model — Bloom’s taxonomy, Depth of Knowledge, Understanding by Design, the 5E model — not against a free-text prompt.
- Curriculum-anchored. Output maps to a specific NERDC performance objective, so a lesson traces to the exact expectation it serves and scheme-of-work gaps become visible at objective level rather than topic level.
- Teacher-owned. The teacher edits, approves and signs off, and the approval is recorded. Accountability does not transfer to the model.
How it differs from adjacent products
| Approach | What the AI does | Who is accountable |
|---|---|---|
| AI tutoring | Interacts directly with the pupil | Ambiguous — the model mediates learning |
| Adaptive learning | Sequences content by prior performance | The algorithm sets the path |
| Chatbot lesson notes | Produces a document on request | Teacher, with nothing to check it against |
| Pedagogy with AI | Drafts against a stated model and NERDC objective | The teacher, explicitly and on the record |
What it looks like in a Nigerian classroom
1. Lesson notes against the objective you are accountable to
A teacher selects a NERDC performance objective, a class and an instructional model. Edves drafts a lesson sequence with the cognitive demand of each task tagged, differentiation for the ability spread in that specific arm, suggested instructional materials that a Nigerian classroom actually has, and the class activity that will evidence the objective.
The teacher edits it. What goes into the lesson note book is the teacher’s lesson.
2. Scheme of work coverage that is real
Because lessons carry objective references, a head of department can see what has genuinely been taught against the scheme of work, and specifically which objectives appear in lesson notes but never in any assessment. That third category — taught but never checked — is where most of the gap between mock results and WASSCE results lives.
3. Adaptation during the term
Continuous assessment results feed back into what the next sequence must address, so a class that has not secured a topic gets it revisited before the terminal examination rather than after it. See CBT and assessment.
4. Evidence for observation and CPD
The lesson record is the evidence an observer or a head of department would otherwise have to assemble by hand, and it feeds the CPD trail that supports TRCN licence renewal. See teacher development.
The instructional models
Edves operationalises a defined set of models rather than treating pedagogy as a free-text field. Each carries its own task shapes, scaffold logic and rubric structure.
| Model | What it structures |
|---|---|
| Bloom’s taxonomy | Cognitive-level tagging across tasks |
| Depth of Knowledge | Complexity calibration against WAEC and NECO demand |
| Inquiry-based learning | Questioning sequences and investigation design |
| Problem-based learning | Scenario-driven scaffolds |
| Project-based learning | Milestones and rubric feedback |
| Experiential learning | Hands-on cycles with structured reflection |
| Collaborative learning | Group task design for large classes |
| Differentiated instruction | Ability-spread scaffolds within one arm |
| Constructivist approach | Prior-knowledge activation and concept building |
| Competency-based learning | Mastery tracking and progression |
| Spaced learning | Retrieval scheduling for retention |
| Micro learning | Short bursts with immediate feedback |
| Blended learning | Contact and independent study orchestration |
| Multilingual pedagogy | Mother-tongue instruction and transition to English |
Language of instruction
Nigerian policy has long held that early instruction should use the language of the immediate environment before transitioning to English, and in practice most classrooms operate bilingually whatever the policy says. Edves supports lesson planning and family communication in Hausa, Yoruba and Igbo alongside English, which matters most in early primary where the gap between the language a child thinks in and the language they are tested in does the most damage.
Why the anchoring is the whole point
An unanchored model produces a lesson note that reads like a good lesson and is not one. Anchoring to a model and a curriculum objective turns a plausibility engine into something a head of department can audit — and turns lesson planning into the coverage evidence a school needs for inspection anyway.
It also protects the teacher. When a proprietor asks why an arm underperformed in the terminal examination, a record showing which lesson addressed which objective, and that the teacher approved it, is a better position than a memory and a notebook.
Large classes
Much Nigerian pedagogy advice assumes class sizes that Nigerian teachers do not have. Differentiation guidance that presumes 20 pupils is not useful at 70. Edves generates differentiation in the form of tiered tasks and grouping structures that work at scale, rather than individual plans that cannot be delivered.
Getting started
Most schools begin with lesson planning alone, because the time recovery is immediate and the risk is low. Assessment follows once staff trust the framing. Observation comes last, because it touches the most sensitive relationship in the school. Attempting all three in one term is the most common reason implementations stall.