// CONTENT.GENERATION

    Gap-Driven Content Generation for Education

    CGF generates gap-targeted content from evaluation signals — questions, worksheets, and lesson plans calibrated to what each learner still needs.

    // CONTENT.GENERATION.SESSION
    gap_topic:organic_chemistry
    content_type:PRACTICE_QUESTIONS
    generated:5 questions
    difficulty:ADAPTIVE ↑
    signal_aligned:TRUE ✓
    triggered_by:EVALUATION.SIGNAL ✓
    human_review:PENDING ✓
    SYSTEM.STORES:LEARNING.SIGNALS ✓

    // THE.SYSTEM

    Two Builders. One Signal Source. Teacher Control at Every Step.

    // WORKSHEET.BUILDER
    type:QUESTION_GENERATION + FEEDBACK
    configuration:5-step guided wizard
    teacher_controls:question_type · difficulty · bloom_taxonomy
    signal_source:EVALUATION.SIGNAL ✓
    human_review:REQUIRED ✓
    output:STRUCTURED_JSON ✓
    // LESSON.PLAN.BUILDER
    type:INSTRUCTIONAL_PLAN
    configuration:5-step guided wizard
    teacher_controls:framework · strategies · enrichment_blocks
    signal_source:EVALUATION.SIGNAL ✓
    human_review:REQUIRED ✓
    output:STRUCTURED_JSON ✓

    // both suites → same signal source → teacher-configured → human review required → structured output

    // CAPABILITIES

    Built Around Teacher Control, Not Automation

    Signal-Aligned Generation

    Triggered by evaluation signals, not topic requests
    Gap topic, severity, learner context passed into generation
    Content calibrated to the exact criterion that failed

    Multi-Step Configuration Wizard

    5-step configuration before generation runs
    Teacher reviews and overrides AI suggestions each step
    Metadata → content selection → framework → LOB mapping → export

    Bloom's Taxonomy & Difficulty Control

    Teacher configures cognitive level distribution before generation
    Presets: Foundation Building / Skill Development / Critical Thinking / Advanced
    Difficulty: Beginner Friendly / Balanced / Challenging / Advanced

    Learning Objective Mapping

    Questions mapped to SMART Learning Objectives with Bloom's tags
    Teacher sets question count per objective and question type
    Every piece traceable to the learning objective it targets

    Human Review Gate

    All generated content enters review queue before delivery
    Teacher approves, edits, or rejects — rejection routes back
    No generated content reaches a learner without the gate

    // THE.SHIFT

    Most Content Tools Start with a Topic. CGF Starts with a Gap.

    CURRICULUM.SCHEDULE
    Content from topic library
    Syllabus-driven sequence
    Same difficulty for all learners
    No signal alignment
    CGF.SIGNAL.DRIVEN
    Content from evaluation signal
    Gap-priority sequence
    Teacher-configured difficulty per Bloom's level
    Signal-aligned per learner

    // SAME CURRICULUM LIBRARY. DIFFERENT GENERATION LOGIC. DIFFERENT OUTCOME.

    // HOW.IT.WORKS

    From Evaluation Signal to Reviewed, Deployed Content

    01

    Signal Capture

    CGF evaluates a learner response and identifies the specific concepts where weakness appeared

    02

    Teacher Configuration

    Gap inputs pass to Worksheet Builder or Lesson Plan Builder — teacher configures difficulty, Bloom's level, and question types

    03

    Human Review

    Generated content enters the review queue — teacher approves, edits, or rejects before delivery

    04

    Structured Learning Intelligence Output

    Approved content returns as structured JSON with concept tags, Bloom's level, and signal alignment metadata

    content.generation.output
    learner_id:std_00391
    gap_topic:organic_chemistry
    gap_severity:high
    suite:worksheet_builder
    content_type:practice_questions
    count:5
    difficulty:moderate
    bloom_level:apply
    triggered_by:evaluation_signal
    review_status:PENDING_HUMAN
    questions:[{ question_id: 'q_001', text: 'Describe the mechanism of an SN2 reaction...', concept_tag: 'nucleophilic_substitution', gap_alignment: 'organic_chemistry_mechanism', bloom_tag: 'apply', difficulty: 3.1 }]

    // GOVERNANCE.TRUST

    Built for Platforms Where Content Decisions Matter.

    HUMAN.IN.THE.LOOP

    Teacher review required at every generation event
    No content reaches learners without explicit approval
    Rejection routes back with reviewer notes for regeneration

    TEACHER.CONFIGURED.BOUNDS

    Academic teams set content policies before generation runs
    Difficulty bands, Bloom's levels, curriculum standards configured
    System generates strictly within teacher-defined parameters

    FULL.AUDIT.TRAIL

    Every generated piece carries signal-to-content traceability
    Which evaluation signal triggered it, which gaps it targeted
    Who reviewed it, when approved, configuration parameters applied

    HUMAN.IN.THE.LOOP

    Policy-aware. Human-verified. Infrastructure-grade.

    CGF's content generation system is human-in-the-loop by design. Teachers remain the control point — AI handles volume, humans handle governance. This is not an automation layer. It is content generation infrastructure with built-in human authority at every control point.

    // GOVERNANCE

    Human-in-the-Loop at Every Control Point

    Every piece of content generated by CGF requires human approval before it reaches a learner. Teachers and academic teams define the generation bounds — gap thresholds, content types, difficulty ranges, curriculum standards — and the system generates within those policies. Nothing is delivered without passing through the review gate.

    Content Review Gate

    All generated content enters review queue before delivery
    Reviewer approves, edits, or rejects before any learner sees it
    Rejection routes back for regeneration with reviewer notes

    Policy-Governed Generation

    Academic teams configure gap thresholds and content types
    Difficulty bands, Bloom's levels, curriculum standards set upfront
    System generates within those bounds — not beyond them

    Signal-to-Content Audit

    Every piece: which evaluation signal triggered it
    Which gaps it targeted, who reviewed it, when approved
    Full traceability from evaluation event to deployed content

    // COMMON.QUESTIONS

    CGF Content Generation produces gap-targeted practice questions, worksheets, and lesson plans from evaluation signals — not topic libraries. It works through two API suites: Worksheet Builder and Lesson Plan Builder. Both require teacher configuration before generation runs and human review before any content reaches a learner.

    What is CGF Content Generation?

    CGF Content Generation is a system that generates practice questions, structured feedback, and lesson plans directly from evaluation signals. It works through two suites: Worksheet Builder (gap-targeted questions and feedback) and Lesson Plan Builder (structured instructional plans for teachers). All generated content requires human review before it reaches any learner.

    How does Worksheet Builder differ from Lesson Plan Builder?

    Worksheet Builder generates practice questions and feedback calibrated to individual or cohort-level learning gaps — typically for assessment or revision. Lesson Plan Builder generates structured instructional plans for teachers to deliver gap-targeted teaching. Both are triggered by the same evaluation signals and both require human review before content reaches learners.

    How much control does the teacher have over what gets generated?

    Full control. Before generation runs, teachers configure content type, question format distribution, Bloom's taxonomy target level, and difficulty band. AI recommendations are pre-selected but every decision is overridable. The system generates within the bounds teachers set — nothing more.

    Is content generation automated or does it require human involvement?

    All content generated by CGF requires human approval before delivery. Teachers and academic leads review and approve every generated piece. Content policies — difficulty range, Bloom's level, question format, topic constraints — are set by the academic team before generation runs. Human-in-the-loop is a structural requirement, not an optional setting.

    How does content generation integrate with our existing platform?

    Content generation is available via REST API through AI Studio's Worksheet Builder and Lesson Plan Builder suites. Generated content is returned as structured JSON with concept tags, Bloom's level, difficulty, and signal alignment metadata — ready for injection into any LMS, assessment platform, or custom learner interface.

    // NEXT.STEP

    See Gap-Driven Content Generation in Action

    Content built from evaluation signals closes real gaps — not assumed ones. See Worksheet Builder and Lesson Plan Builder in the evaluation infrastructure pipeline.

    See how evaluation becomes infrastructure.