EaseBrainACADEMYA useful solution fits the system around it: people, resources, decisions and unintended effects.
Choose a manageable school or community problem. Map its stakeholders and causes, then write a one-page problem brief with measurable success criteria.
A symptom is what you notice. A cause is a possible explanation that needs evidence. A full rubbish bin might reflect collection timing, bin capacity or sorting behaviour. Avoid jumping straight to a favourite technology.
List the symptom and three possible causes.
Ask who benefits, who does the work and who bears a cost. Discovery should be voluntary, respectful and supervised. Use a facilitator-provided scenario if field access is not available.
Map at least four stakeholders and one potentially excluded perspective.
A problem brief states who has the problem, the evidence, the constraints and what improvement would look like. A measurable criterion makes later testing possible.
Write a brief and two success criteria with units or observable behaviours.
Extra bins may reduce overflow but increase collection work. If staff cannot collect them, uncollected bins can worsen the original problem. 'Bins bought' measures an activity; 'overflow events per week' measures an outcome.
Write your own answer before opening the explanation. Then improve one sentence using what you learned.
Caretakers may need more time and equipment. Include their perspective and test collection capacity before deciding that more bins solve the problem.
Add one excluded stakeholder and test whether their needs change the success criteria. A supplied scenario is a complete alternative to field interviews.
An educator reference for turning ideas into social, cultural and financial value.
Kenyan curriculum context. EaseBrain complements competency-based learning; it does not claim KICD accreditation.
The Builder's club deliveries arrive late. Draw three connected causes and label one as an assumption you would test.
Scratch space: not saved. Copy useful reasoning into your private workbook.
Choose a fictional club service, a water-data investigation, or an accessible information tool as your capstone. Keep the same problem brief through your budget, prototype, tests, revision and final defence. The crew exercises teach a method; apply that method to your chosen project and explain any adaptation. Simulation is a complete route.
50–60 minutes · Paper or browser · No purchase required
“Let’s buy more bins for the club space.”
“Who empties them, where does the waste go, and what happens when collection stops?”
Write a prediction before you reveal the worked reasoning.
Fictional system: learners create waste; vendors choose packaging; cleaners empty bins; caretaker schedules pickup; recycler accepts only sorted material. Starting chain: more waste → fuller bins → litter. Complete the missing relationships.
Draw the five actors and at least four arrows. Label each arrow with a mechanism, not just a line: e.g. missed pickup increases stored waste.
Trace a feedback loop, such as litter reducing care for the space and leading to more litter. Label this as a hypothesis to check. Identify whose work a proposed change increases.
Compare two interventions using the decision lab. Define an outcome measure, a baseline and a fair test period. Track overflow incidents, not only bins purchased.
The Strategist has three ideas for a fictional school club and a limited budget. Your job is to make a defensible choice, listen to other priorities and revise it when the constraint changes.
A score describes how well an option meets a criterion. A weight describes how important that criterion is. Multiply each score by its weight, then add. The table makes assumptions visible; the total is not proof that an option is best.
| Option / cost | Access | Benefit | Easy upkeep | Weighted total |
|---|---|---|---|---|
| Reading corner 40 tokens | 29 | |||
| Water repair 60 tokens | 34 | |||
| Science club 50 tokens | 27 |
Reading corner calculation now: 4 × 3 + 3 × 3 + 4 × 2 = 29.
With the supplied scores, weights 3, 3, 2 give totals 29, 34, 27. Weights 1, 1, 5 give 27, 20, 22. The ranking flips because priorities changed; the options did not become objectively better or worse. If you edited scores, your results may differ.
Selected cost: 100 / 100 tokens. 0 tokens remain.
The table scores individual options; it does not optimise project bundles. At 100 tokens, reading + water fits. Water + science costs 110. At 80, no pair fits. Consider dependencies and overlapping benefits before combining projects.
Who chose the weights? What supports the scores? Ask someone with a different priority to challenge your recommendation. Explain what you would stop, keep or redesign with 80 tokens.
Inputs reset when you leave. Download your reasoning or copy it to your private workbook.
Go deeper: compare stakeholders and test uncertain assumptions with a mentorA pickup rota could reduce overflow without more bins, but only if someone can perform the work. The Builder adds a collection-capacity assumption and checks with the caretaker before recommending a change. The causal map is a model to test, not proof.
Finding the answer is not evidence of mastery. Explain it, test a changed input and record your own work.
Pickup becomes unavailable for a week. Adapt the intervention and explain its new limits.
Actor | Need | Influence | Burden Arrow | Mechanism | Evidence or assumption Feedback loop: Option A / B: Outcome measure | Baseline | Test period:
Choose a twist. Use paper, a sketch or a conversation.
Optional practice. Copy useful observations into your workbook; choosing a twist does not record completion.
Compare ideas with someone you know. Sharing is optional.
Work through Why more bins may not solve waste with a focused prompt. Your notes stay private unless you choose to paste an excerpt here.
Use fictional examples. Leave out names, contact details, passwords and private records.
Requires an active learning profile. Your provider may charge you; API billing is separate from ChatGPT. EaseBrain has no paid-key fallback.
Key clears after each attempt. EaseBrain saves only a daily request count, not your key, prompt or answer. OpenAI’s data policies apply. Up to 500 output tokens and 20 attempts a day; no automatic retries. These limits do not guarantee a spending ceiling.