Defined objective
The user provides the material, level, learning target, and what kind of evidence the quiz should reveal.
Create question sets around a topic, lesson, or source text with planned controls for difficulty, format, answer keys, feedback, and learning objectives.
Create a mixed quiz on cell division for a 9th-grade review.
Use recall items for vocabulary, sequencing questions for process knowledge, and one explanation prompt comparing mitosis and meiosis. Keep the answer key separate and connect every question to the review objective.
Question generation should begin with an objective, not a random topic label. Boardesa is designed to balance recall, understanding, application, and explanation, then turn results into a focused review action.
Each workflow is designed to turn context into a response the learner or educator can inspect, adapt, and use.
The user provides the material, level, learning target, and what kind of evidence the quiz should reveal.
Question types and difficulty are selected deliberately rather than produced as a repetitive list.
The answer key explains reasoning and helps identify which concept should be reviewed next.
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.
Name the knowledge or skill the learner should demonstrate by the end.
Decide question count, format, difficulty, and the mix of recall and application.
Check accuracy, ambiguity, coverage, answer options, and alignment with the source.
Group errors by concept and convert them into a targeted review or follow-up quiz.
A short blueprint prevents repetitive questions and makes the answer key more useful.
No response is sent to an AI provider. This builder only demonstrates how context will be framed.
Create a self-study quiz using a mix of recall and explanation questions. Explain every answer and group the final review advice by topic rather than only giving a score.
The current experience is a product preview. These use cases define the intended scope for later secure AI implementation.
Low-stakes retrieval before an exam, lesson, or independent review session.
A short check aligned with the lesson objective and suitable for rapid teacher review.
A structured starting set that teachers can edit, categorize, and reuse.
Separate student and teacher views with reasoning, common errors, and review notes.
These criteria describe the intended product standard. They do not claim that an unconnected demo already performs live AI processing.
Every question tests something the user intended to assess.
The set does not overfocus on one easy subtopic.
Wrong options reflect realistic misconceptions without being deceptive.
The learner can understand what each item is asking.
Answers and explanations are checked before classroom or high-stakes use.
Performance leads to a clear review recommendation.
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.
This page describes the planned capability. Live AI processing is not enabled in the current MVP.
Source text input is planned for the secure AI version; the current page is a preview.
Generated quizzes must be reviewed, especially for specialized, technical, or high-stakes material.
Difficulty, question type, count, audience, and learning objective are planned controls.
Yes. The planned workflow includes short answer, explanation, matching, sequencing, and mixed formats.
Export formats are a later product decision. No classroom platform integration is active in this MVP.
Explore the local workspace demo now. Secure AI processing and billing will be connected only after the product experience and protection layers are ready.