EDUSKIT / SOLUTIONS
AI teaching:
planning, explanations, quizzes and summaries
Map AI workflows before, during and after class, with clear inputs, intended outputs and teacher review at each step.
Updated · eduskit
At a glance
AI teaching can be organized around lesson planning, explanations, quiz creation, teaching assistance and summaries. eduskit demonstrates these five workflows. Define the source material, intended output and teacher review for each step, so a generated answer is not treated as a verified teaching conclusion.
Map the workflow before, during and after class
A specific teaching task makes output quality easier to evaluate than an undefined chat entry point. The table below is a workflow planning example.
| Workflow | Inputs to prepare | Output and review focus |
|---|---|---|
| Lesson planning | Course materials, teaching goals and learner background | Draft lesson plan and resources; check concepts, difficulty and pacing |
| Explanations | A student question and relevant lesson context | Draft explanation; check concepts, reasoning and relevance |
| Quiz creation | Lesson concepts and practice goals | Draft questions and answers; check ambiguity, correctness and difficulty |
| Teaching assistance | A defined classroom task and available materials | Question or routine support; define when the teacher takes over |
| Lesson summaries | Permitted lesson content and learning priorities | Review materials; check omissions, inaccurate conclusions and information that should not be retained |
Where should teacher review happen?
Set publication rules before showing outputs to students. Plans, questions and summaries can be drafts for teacher editing; live explanations need a correction and handover process. The website demonstrates AI outputs, not measured subject accuracy or learning outcomes.
- Knowledge: check terminology, formulas, facts and reasoning against the course materials.
- Teaching fit: check learner background, difficulty, wording and lesson objectives.
- Answer validation: independently review the solution, expected answer and marking criteria.
- Correction: make sure teachers can identify and replace errors before unreviewed content enters study materials.
Define course materials and data boundaries
Before integration, list which materials may be processed, what must not be sent and who may access the outputs. These are planning questions, not claims of a specific eduskit data policy or certification.
- Review usage rights, intended learner level and version of the course materials.
- Provide the context needed for the task, without unrelated personal information or entire records.
- Confirm the model service, processing location, retention requirements and access permissions.
- Define correction and deletion workflows for materials, student questions and generated outputs.
Evaluate AI with repeatable teaching tasks
Use a fixed set of real course tasks, recording the material version and teacher assessment. A workflow demonstration does not replace classroom validation.
- Include concept explanations, common misconceptions, practice questions and lesson summaries.
- Record correctness, use of permitted course material and missing reasoning steps.
- Compare teacher editing effort with the existing workflow to identify useful applications.
- Include unusual inputs, insufficient source material and questions the model cannot answer reliably.
- Refine the task scope from teaching feedback and keep the human review the workflow requires.
About this guide
This guide uses capabilities presented on the eduskit website to support requirements and solution planning. Validation items are project evaluation suggestions. Confirm APIs, platform versions, capacity, pricing and delivery responsibilities during integration.