It differs from general-purpose AI use because output is anchored to two things: an explicit instructional model, and a named NaCCA learning indicator. The teacher edits and signs off, and the system records that they did.
The problem in Ghanaian classrooms
Lesson notes are a genuine professional discipline and, in a great many schools, a weekly ritual performed for inspection rather than for teaching. A teacher with a full timetable and 50 or more learners per class writes what the head of department will sign, not what will change Thursday’s lesson.
Generative AI arrived into that gap and made it worse in a specific way. A chatbot produces a lesson note that is fluent, well-formatted, plausible and disconnected from the learning indicator the teacher is accountable for. It reads better than what it replaced and teaches no better — frequently worse, because several 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 judgement 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, SOLO — not a free-text prompt.
- Curriculum-anchored. Output maps to a specific NaCCA learning indicator, so a lesson traces to the exact expectation it serves and coverage gaps become visible at indicator level.
- Teacher-owned. The teacher edits, approves and signs off, and the approval is recorded.
How it differs from adjacent products
| Approach | What the AI does | Who is accountable |
|---|---|---|
| AI tutoring | Interacts directly with the learner | 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 against |
| Pedagogy with AI | Drafts against a stated model and NaCCA indicator | The teacher, explicitly and on the record |
What it looks like in practice
1. Lesson notes against the indicator you are accountable to
A teacher selects a learning indicator, a class and an instructional model. Edves drafts a lesson sequence with cognitive demand tagged per task, differentiation for the ability spread in that specific class, instructional materials a Ghanaian classroom actually has, and the activity that will evidence the indicator. The teacher edits it. What is taught is the teacher’s lesson.
2. Coverage that is real
Because lessons carry indicator references, a head of department can see what has genuinely been taught against the scheme of learning, and specifically which indicators appear in lesson notes but never in any assessment. That category is where most of the gap between mock results and BECE or WASSCE results lives. See curriculum.
3. Core Competencies that are evidenced
Critical thinking, collaboration, communication and creativity are easy to claim and hard to evidence. Because activities are tagged to the competency they develop and to the evidence they produce, a school can show where competency development actually happened rather than asserting it in a comment box.
4. Evidence for inspection and CPD
The lesson record is the evidence a NaSIA inspector or a head of department would otherwise assemble by hand, and it feeds the CPD trail supporting NTC licence maintenance. See NaSIA and teacher development.
The instructional models
| Model | What it structures |
|---|---|
| Bloom’s taxonomy | Cognitive-level tagging across tasks |
| Depth of Knowledge | Complexity calibration against WAEC demand |
| Understanding by Design | Backward design from the learning outcome |
| SOLO taxonomy | Progression from surface to relational understanding |
| Inquiry-based learning | Questioning sequences and investigation design |
| Project-based learning | Milestones and rubric feedback |
| Collaborative learning | Group task design for large classes |
| Differentiated instruction | Ability-spread scaffolds within one class |
| Constructivist approach | Prior-knowledge activation and concept building |
| Competency-based learning | Mastery tracking and progression |
| Spaced and micro learning | Retrieval scheduling and short practice bursts |
| Mother tongue pedagogy | First-language instruction and transition to English |
Large classes
Much pedagogy advice assumes class sizes Ghanaian teachers do not have. Differentiation guidance written for 20 learners is not useful at 55. Edves generates differentiation as tiered tasks and grouping structures that work at scale, rather than individual plans that cannot be delivered.
Workload, honestly
Drafting is faster; reviewing is not free. A teacher who accepts AI output uncritically saves time and produces worse lessons; a teacher who reviews properly saves less time than the marketing suggests and produces better ones.
The honest claim is that Edves shifts teacher time from producing a document to making decisions about it. That is a better use of the hour, and it is what a school should be able to demonstrate — including what it stopped doing.
Where AI is and is not used
- AI drafts lesson planning, differentiation, feedback language and coaching prompts.
- AI does not assign assessment marks, make employment recommendations, or interact with learners unsupervised.
- Learner data is not used to train general-purpose models — see Act 843.
- Every AI-drafted artefact records the human who approved it.