Unstructured problem
The learner brings an equation, word problem, diagram description, or a specific step that no longer makes sense.
MATH AI SOLVERSee the reasoning,
See the reasoning,
not only the result.
A step-by-step math workspace designed to separate interpretation, method, calculation, and verification. This preview demonstrates the learning flow; live solving, image input, and graphing are not connected yet.
Secondary studentsUniversity learnersTutors and teachers
BOARDESA AI · LOCAL PREVIEW
YOUR REQUEST
Find the turning points of f(x) = x³ − 3x² − 9x + 5
GUIDED RESPONSE
A structured route to the solution
First identify what a turning point means, differentiate the function, solve for critical values, test the derivative on each interval, and interpret each coordinate as a local maximum or minimum.
Clarify the goalMake steps visibleChoose the next action
Sample output · no live AI request
CONTEXT FIRSTVISIBLE WORKFLOWREVIEWABLE OUTPUTLEARNER OWNERSHIP
01 Product principle
A correct result is useful.
A checkable method is better.
The planned math mode treats each solution as a sequence of decisions. It should name the method, preserve intermediate work, make assumptions visible, and finish with a check. That structure helps learners locate mistakes instead of receiving an unexplained final line.
From request to useful resultA learning outcome,
A learning outcome,
not a content dump.
Each workflow is designed to turn context into a response the learner or educator can inspect, adapt, and use.
Visible reasoning
Boardesa separates interpretation, strategy, working, and verification so each decision can be reviewed.
Transferable understanding
The session ends with a similar practice task or a check question—not only a result to copy.
How it should work
A visible path from request to result.
Every mode uses a workflow that can be understood, checked, and improved. The user remains responsible for the final decision and any high-stakes verification.
Read the problem
Identify what is given, what is asked, relevant constraints, and the most useful representation.
Choose a method
Explain why a formula, theorem, graph, or transformation fits before using it.
Work in visible steps
Keep algebra, substitutions, units, and reasoning separated so mistakes are easier to locate.
Check and transfer
Verify the result, then use one related question to test whether the method can be applied again.
Interactive product preview
Build a clearer math request
Choose the context that a future secure solver should receive before it responds.
LOCAL PREVIEW
No response is sent to an AI provider. This builder only demonstrates how context will be framed.
SESSION BRIEF3 context choices
PLANNED REQUEST
I am working at secondary-school level. Help me understand a concept using guided hints. Show the method, pause at decision points, and finish with one check question.
Built for real education tasksUse it where
Use it where
structure matters.
The current experience is a product preview. These use cases define the intended scope for later secure AI implementation.
Algebra
Equations, inequalities, functions, transformations, and the reasoning behind each manipulation.
Calculus
Limits, derivatives, integrals, optimization, and interpretation—not only symbolic work.
Geometry
Coordinate methods, relationships, proof planning, and future diagram-aware workflows.
Statistics
Probability, distributions, summaries, assumptions, and interpretation of results.
04 Quality standard
What a strong output
should make visible.
These criteria describe the intended product standard. They do not claim that an unconnected demo already performs live AI processing.
Method named
The response identifies the method and why it applies.
Work preserved
Intermediate lines remain visible enough to inspect.
Assumptions surfaced
Domains, units, signs, and conditions are not hidden.
Result checked
Substitution, estimation, units, or behavior is used to verify the answer.
Notation readable
Expressions are organized for scanning rather than compressed into prose.
Next task included
A related problem tests whether the learner can transfer the method.
Responsible by designAI should support judgment,
AI should support judgment,
not hide it.
01 Outputs should be reviewed against source material, course rules, and professional judgment.
02 Future API keys, uploads, and model calls will remain behind protected backend infrastructure.
03 The interface will distinguish user input, source material, generated guidance, and final decisions.
04 Demo interactions on this page stay in the browser and do not contact an AI provider.
Common questions
Know what the tool is—and is not.
This page describes the planned capability. Live AI processing is not enabled in the current MVP.
Does it solve from photos?+
Image and PDF input are planned capabilities, not active in this MVP. They will require protected upload handling and careful extraction checks.
Will graphing be available?+
The product architecture allows a future graph panel, but no live graph engine is connected yet.
Can students copy answers?+
The intended experience emphasizes method, checks, and guided practice. Users remain responsible for academic integrity and course rules.
Can it verify my own working?+
That is a core planned workflow: compare the learner’s steps with a sound method, locate the first divergence, and explain the correction.
Will every answer be correct?+
No AI system should be assumed infallible. Important results should be checked against course material, a calculator, or a qualified teacher.
BOARDESA AI · PRODUCT PREVIEWStart with the goal.
Start with the goal.
Build from there.
Explore the local workspace demo now. Secure AI processing and billing will be connected only after the product experience and protection layers are ready.