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Our API enables you to assess and evaluate both questions and answers according to IELTS examination standards. Whether for Academic or General Training, this API allows you to submit questions and answers in various multimedia formats, providing scores, feedback, improvement suggestions, and objective error detection.
Customize the evaluation with your own rubrics for tailored assessments.

IELTS

Evaluation API

Question Types:

Currently, this API is available exclusively for evaluating Writing Skills for IELTS Academic and General Training.

Key Features

  • Automated Grading : Efficiently grade IELTS writing tasks with high accuracy.
  • Detailed Feedback : Receive comprehensive feedback, including suggestions for improvement and objective error detection.
  • Customization : Customize the evaluation with your own rubrics.
  • Multimedia Support : Submit questions and answers in text, audio, image, and video formats.
  • Scalability : Handle large volumes of evaluations during peak times without compromising speed or accuracy.
  • Integration : Seamlessly integrate with other educational tools and platforms to create a cohesive evaluation ecosystem.

Endpoints

1

Submit Evaluation Request

Submit a request for evaluation directly by providing questions and respective answers in a json format. The API generates an evaluation report based on the provided data, and returns an acknowledgment along with an evaluation ID.
2

Get Specific Evaluation Report

Retrieve a specific evaluation report using its unique identifier (ID). Clients can access past evaluations or reference specific reports by providing the evaluation ID.
3

Get All Evaluation Reports

Retrieve all the evaluation report or for a specific set of handwritten notes. Clients can provide parameters such as student name, class, subject, and date of evaluation to filter the results.

Sample Request

FAQ

It performs rubric-based scoring with objective error detection and actionable feedback mapped to IELTS standards. The engine supports multimodal inputs (text, images of handwriting, audio, video) and targets 95% accuracy with an optional human-in-the-loop review. It’s configurable for CBSE/ICSE/GDPR alignment and can be delivered as a white‑label experience.
Use POST /v1/ielts/evaluation to submit the prompt and response, then retrieve the outcome with a GET by ID. The API accepts text, audio, image, and video and returns objective errors, holistic scores, and improvement guidance. Endpoints are REST/JSON for straightforward orchestration.
The evaluation engine accepts typed text, images of handwriting, audio, and video for unified scoring. It applies consistent rubrics, flags objective errors, and provides holistic band descriptors aligned to exam expectations. A human-in-the-loop path covers unusual prompts or edge cases.
You can route sensitive or ambiguous submissions through an optional human‑in‑the‑loop workflow before finalizing results. This keeps quality high while the core engine targets 95% accuracy on automated scoring. It ensures educator oversight without heavy operational burden.
Scoring targets 95% accuracy based on domain rubrics, real exam patterns, and continuous calibration. For edge cases—such as handwriting quirks or mixed media—human review can be invoked without disrupting throughput. Auditability supports reliable QA over time.
Results include objective error flags, holistic scoring, and improvement guidance complemented by model answers. These are mapped to exam standards so learners see band‑aligned descriptors and concrete next steps. The same structures power consistent reporting and remediation flows.
Workflows and reporting can be configured to be CBSE/ICSE/GDPR aligned, with standardized scoring and auditability. Institutions can enable compliant exports and exam reporting without custom ML ops. This helps maintain privacy and board expectations across deployments.
Providers can embed a branded UI or use APIs to enforce uniform evaluation across clients, with analytics that roll up cleanly. Reporting and exports are configurable for CBSE/ICSE/GDPR alignment, and human‑in‑the‑loop can be reserved for premium or sensitive cohorts. This keeps band descriptors and rubrics consistent at scale.