Planner Results
This summary appears above the form after calculation.
Interpretation
Action Notes
Planner Inputs
The page stays in a single main column, while the calculator fields use three columns on large screens, two on medium screens, and one on mobile screens.
Planner Graph
The chart compares projected weekly teacher hours and student hours across the planning window.
Weekly Plan Table
| Week | Teacher Hours | Student Hours | Milestones This Week | Focus | Load Tag |
|---|---|---|---|---|---|
| Submit the planner form to generate weekly recommendations. | |||||
Example Data Table
This sample shows one possible planning setup for an AI and machine learning course.
| Scenario | Classes/Week | Students | Teacher Capacity | Student Capacity | AI Gain | Planning Weeks | Expected Outcome |
|---|---|---|---|---|---|---|---|
| ML Foundations Cohort | 4 | 32 | 30 hrs | 24 hrs | 18% | 8 | Balanced weekly plan with moderate milestone density. |
| Fast Project Sprint | 5 | 28 | 26 hrs | 20 hrs | 10% | 6 | Higher risk and tighter late-week load. |
| Capstone Support Term | 3 | 18 | 24 hrs | 18 hrs | 25% | 10 | Strong coordination and lower overload risk. |
Formula Used
1. Base teaching hours
Base Teaching Hours = (Classes × Hours per Class) + (Classes × Prep Hours) + (Students × Grading Hours) + Support Hours + Admin Hours
2. Complexity multiplier
Complexity Multiplier = 1 + ((Complexity − 5) × 0.06)
3. Priority multiplier
Priority Multiplier = 1 + ((Priority Weight − 3) × 0.05)
4. AI assistance multiplier
AI Multiplier = 1 − (AI Assistance Gain ÷ 100)
5. Teacher weekly load
Teacher Weekly Load = Base Teaching Hours × Complexity Multiplier × Priority Multiplier × AI Multiplier × (1 + Buffer%)
6. Student weekly load
Student Weekly Load = (Class Hours + Assignment Hours + Revision Hours + Project Hours) × Complexity Multiplier × Priority Multiplier × (1 + Buffer%)
7. Capacity ratios
Teacher Ratio = Teacher Weekly Load ÷ Teacher Available Hours
Student Ratio = Student Weekly Load ÷ Student Available Hours
8. Planner efficiency
The efficiency score starts from 100 and subtracts overload, imbalance, and milestone pressure. It then adds support from AI assistance and forecast confidence.
9. Coordination index
The coordination index rewards balanced teacher and student loads, reasonable milestone density, AI support, and better forecasting confidence.
10. Risk score
The risk score increases when capacity ratios, urgency, milestone density, and forecast uncertainty rise together.
How to Use This Calculator
- Enter the teacher’s weekly capacity and the students’ weekly study capacity.
- Add the class schedule, session duration, preparation time, grading time, support hours, and admin hours.
- Enter student assignment, revision, and project hours per week.
- Set milestones, planning weeks, complexity, priority, deadline timing, AI assistance gain, forecast confidence, and extra buffer.
- Press Calculate Planner to show the results above the form.
- Review the interpretation, action notes, chart, and weekly plan table.
- Use the CSV or PDF buttons to export the summary and weekly roadmap.
FAQs
1. What does this planner calculator measure?
It estimates weekly teacher workload, per-student study demand, milestone pressure, coordination quality, planner efficiency, and scheduling risk. It is useful for structured academic planning in AI and machine learning courses.
2. Why is AI assistance gain included?
AI assistance can reduce repetitive teacher work such as draft feedback, reminders, rubric support, and material preparation. The tool models that reduction as saved teacher time, not reduced student learning effort.
3. Does the student load apply to the whole class?
No. Student load is calculated per student per week. This helps compare expected weekly effort against realistic study capacity for one learner in the course.
4. What is a good planner efficiency score?
A score above 75 usually indicates a healthy plan. Scores from 60 to 75 suggest monitoring. Scores below 60 often show overload, imbalance, weak buffers, or too many milestones in a short window.
5. How should I interpret the coordination index?
The coordination index reflects how well teacher capacity, student effort, milestone density, and forecast confidence align. Higher values suggest the plan is easier to manage and communicate consistently.
6. Why does deadline timing affect risk?
Short deadlines compress work into fewer days. That creates heavier weekly spikes, faster feedback cycles, and less room for revision, which raises pressure for both teachers and students.
7. Can I use this tool for non-technical courses?
Yes. The model is general enough for many courses. You can still use it outside AI and machine learning by adjusting complexity, milestone count, study hours, and AI assistance assumptions.
8. What should I change when the plan looks overloaded?
Reduce milestone density, extend the timeline, cut weekly assignment hours, add automation for teacher tasks, or increase planned buffers. Even small reductions can improve balance and schedule quality quickly.