Assignments run continuously. Most platforms evaluate and forget. CGF captures signals from every submission and builds persistent learning memory.
Thousands of submissions. Your stack processes them and returns scores.
The signals — what each student understands, where their reasoning broke down — are never captured.
Every assignment evaluation starts from zero.
Thousands of assignments processed. Each produces a score. None accumulates into a longitudinal record of what each student understands.
Each assignment evaluated in isolation. No system connects today's result to last week's — no pattern detection, no recurring gap identification.
Continuous volumes mean continuous load. Human evaluators lose consistency across the stack — the student who submits Friday gets a different standard.
Written responses are the richest signals in your product. Your current stack scores and discards them. That intelligence is gone.
The platform that wins isn't the fastest grader. It's the one whose evaluation layer compounds over time.
That requires evaluation infrastructure built for continuity — not a tool that runs per submission.
CGF evaluates continuously and builds a persistent, growing record of what every learner understands.
Assignment submitted. CGF receives the response, rubric, and learner context. Multimodal input supported.
Response evaluated with 93% reliability (ICC). Concept signals extracted — mastered, developing, gap-flagged.
Signals added to learner's memory profile. Concept map updated. Trends detected — improving, plateauing, or recurring gap.
API returns the evaluation system result and updated learner state. Your personalisation engine receives current and cumulative signal data.
{
"learner_id": "STU_4421",
"assignment_id": "ASN_0089",
"term_sequence": 47,
"subject": "Class 9 Mathematics",
"score": 18,
"max_score": 25,
"signals": {
"concept_mastered": [
"linear equations",
"substitution method"
],
"concept_gap": [
"simultaneous equations",
"graphical method"
],
"gap_recurrence": "3rd consecutive assignment",
"trend": "IMPROVING on algebra, PERSISTENT gap on graphical methods"
},
"memory_update": "PERSISTENT",
"learner_signal_total": 184
}93% reliability in total scoring alignment (ICC) — maintained across daily volumes and the full assignment term.
Up to 60% reduction in teacher evaluation time (Phase 1 validated; India AI Impact Summit 2026 Compendium, MeitY).
90%+ accurate feedback delivered instantly — the learning loop closes at submission, not at the bottom of the marking stack.
Continuous assignment volumes make consistent evaluation impossible for humans — variance accumulates over weeks. CGF applies your rubric at submission 1 and submission 47. Teachers remain the control point for overrides.
The same rubric standard applied at week one is applied at week twelve. Evaluation quality doesn't degrade with volume or time.
Teachers review flagged evaluations and override at any point. CGF handles volume; teachers handle judgment calls.
Full evaluation log across the term — per student, per assignment, per concept. Exportable for reporting and compliance.
By week eight, your platform knows what each student has mastered and what remains a persistent gap — no additional assessments needed.
Your personalisation engine gets longitudinal signal data, not point-in-time scores. Adaptive paths reflect what each student has been showing across the term.
Up to 60% reduction in teacher evaluation time across the continuous assignment cycle. Time returned to teaching, not marking.
Students receive accurate feedback at submission, not three days later. The learning loop continuous assignment is designed to create actually closes.
Scale exam evaluation with the same infrastructure that runs your assignments. Signals from both feed the same memory layer.
See use case →Once the memory layer is built, it feeds personalised test generation. CGF closes the loop.
See use case →The product powering continuous assignment evaluation — feature set, integration specs, and API documentation.
See the product →Coaching & Test-Prep Networks
Evaluator inconsistency at scale drives student disputes and trust erosion.
See use case →LMS / ERP Platforms
Your platform generates assignments but has no signal layer — clients need learning data you can't currently provide.
See use case →EdTech Platforms
Evaluating at volume but building no learning memory — your personalisation engine has nothing to compound on.
See use case →Institutions & Public Programs
Manual assignment evaluation at institutional scale has no audit trail and no learning memory.
See use case →// COMMON.QUESTIONS
Continuous assignment evaluation builds learning memory across a term — not just scores at submission. CrazyGoldFish captures concept signals from every assignment, structures them into a persistent learner profile, and maintains 93% scoring reliability (ICC) at daily evaluation volumes. Every submission advances the memory layer your personalisation engine needs.
How does CrazyGoldFish handle continuous assignment evaluation at scale?
CrazyGoldFish processes every assignment submission through an evaluation pipeline that scores the response, extracts concept-level signals, and updates the learner's persistent memory profile. At continuous assignment volumes — hundreds or thousands of submissions daily — it maintains 93% scoring reliability (ICC) across the full assignment cycle, not just individual batches.
How does CGF build a longitudinal learning profile from assignment submissions?
Each CGF evaluation returns a signal payload — what the student understood, where their reasoning broke down, and which concepts are recurring gaps. These signals are structured and accumulated on the learner's profile with every assignment evaluated. Over a term, CGF builds a concept map that shows mastery trajectories, persistent gaps, and improvement trends — without any additional assessments.
Can CGF evaluation connect to our personalisation engine?
Yes. CGF is infrastructure, not a standalone tool. The evaluation API returns both the current assessment result and the updated learner signal state. Your personalisation engine consumes this output and adapts content, difficulty, and paths based on what each student's memory profile shows — not just their last score.
How does CGF maintain consistency across a full term of assignments?
CrazyGoldFish applies the same rubric standard at assignment 1 and assignment 47. It achieves 89.7% adjudicated correctness vs. teacher 82.8% (+6.9%) — consistently outperforming human graders who drift across large volumes. 87% of responses land within ±6 marks of the human benchmark throughout the term.
Why not just have teachers grade assignments continuously?
Teacher evaluation of continuous assignments at scale degrades in three ways: throughput hits a ceiling, consistency drifts across the stack, and feedback arrives too late to close the learning loop. CGF removes all three failure modes — evaluating at submission, maintaining consistent standards, and returning feedback instantly. Teachers remain the control point for overrides and edge cases; CGF handles the volume.
CGF evaluates your assignments continuously, captures the signals, and builds the longitudinal memory layer your personalisation engine needs.