AI Learning Evaluation
Education · Assessment

AI Learning Evaluator Pronunciation · Speaking · Diagnosis

Scores learner responses (voice or text) across pronunciation, fluency, content, and logic. An AI assessment engine designed to be embedded as the evaluation module of any education product.

Why

Why AI-driven assessment?

Modern education measures not just answers, but how learners speak and reason. AI makes that scalable.

🎙️

Pronunciation & speaking

Native-phoneme models score pronunciation, fluency, intonation and pace from 0 to 100, with phoneme-level corrective feedback.

🧠

LLM content grading

LLM grades the substance, logic and evidence of responses against a rubric — from single answers to full oral essays.

📈

Learning diagnostics

Strengths, weakness patterns and growth curves, exported as reports for parents and teachers.

Use cases

Where it plugs in

🏫

Language schools

Augment or replace native-teacher hours by auto-scoring recorded speaking sessions.

📚

Edtech publishing

Connect print textbook QR → voice prompt → AI scoring → digital report for a hybrid learning loop.

💻

LMS / e-learning

Add speaking and essay grading to existing LMS assignments via a single API call.

🎯

Test prep

Practice scoring for OPIc, TOEFL Speaking, HSK oral and other high-stakes speaking tests.

👶

Early education

Reading fluency, dictation, times-tables — auto-grade foundational skills.

🧑‍💼

Corporate training

Presentation drills, English pitches, mock interviews — B2B training assessment.

Pipeline

Assessment pipeline

A single learner response triggers three parallel engines that produce a unified report.

1

Collect

Learner responds via voice or text. Web, mobile, and native-app SDKs provided.

2

STT + pronunciation

Speech → text + pronunciation & fluency scoring via GOP models.

3

LLM grading

Rubric-based grading of content, logic, and grammar with cited feedback.

4

Report

Scores, feedback, error analysis and growth curves returned as JSON or PDF.

Scoring dimensions

Scoring dimensions

Learners are diagnosed across five dimensions. Weights can be tuned per domain, age, and difficulty.

Pronunciation accuracy

0 ~ 100

Deviation from native phonemes, with corrective feedback on misarticulated sounds.

Fluency

0 ~ 100

Speaking pace, pause frequency, hesitation-word detection.

Intonation & stress

0 ~ 100

Sentence-final rise/fall and stress placement (language-specific rules).

Content accuracy

0 ~ 100

Factual and semantic correctness against rubrics or model answers.

Logic & structure

0 ~ 100

Argument flow and evidence presentation in essays and presentations.

Integration

How to plug in

🔌

REST API

POST the response (wav/mp3/text) → receive score + feedback JSON within seconds.

WebSocket streaming

Live feedback during real-time conversation or oral exams.

📦

SDKs (JS / Swift / Kotlin)

Record, upload and render results in your web / iOS / Android app in four lines of code.

🔗

Webhook · LTI

LTI-compliant for Moodle / Canvas, results flow straight into the gradebook.

Tech stack

Tech stack

Built on top of our existing Voice Agent + Speech · Pronunciation API + LLM grading engine.

🎤
STT
Streaming + phoneme timestamps
📊
Pronunciation
GOP + phoneme alignment
🤖
LLM grading
Rubric · citations · multilingual
🔊
TTS
Voice feedback synthesis
📂
Analytics
Learning history · error clustering
🔐
Privacy
Regional hosting · optional audio deletion

Evaluating AI Learning Evaluator for your product?

We support PoC for language schools, publishers, LMS vendors and test-prep services.