# Building Shiksha: What I Learned Creating a Real-Time AI English Coach in 10 Days

> Source: <https://dev.to/sp_gamer_e8a7ce484708618f/building-shiksha-what-i-learned-creating-a-real-time-ai-english-coach-in-10-days-jn6>
> Published: 2026-08-15 18:51:07+00:00

#
Building Shiksha: An AI English Coach for Indian Learners

For many Indian learners, the biggest barrier to speaking English fluently isn't a lack of vocabulary or grammar rules learned in school—it's **speaking anxiety** and the fear of making mistakes in front of peers or teachers.

Over the past 10 days, as part of the **10 Days of Voice Agents — Voice for Bharat Edition** under the **Learning & Literacy** track, I built **Shiksha**: an interactive, real-time AI English Communication Coach designed to provide friendly, judgment-free spoken practice.

##
🌟 Why Voice?

Text chatbots don't build spoken confidence. Reading and typing are passive activities, whereas real-world conversations require instant auditory processing, cognitive framing, and spoken articulation.

Shiksha gives learners a low-latency, empathetic voice partner that understands **Hinglish** (code-mixed Hindi and English), allowing them to practice daily presentations, grammar rules, and workplace conversations without embarrassment.

##
🏗️ High-Level Architecture

User Speech (WebRTC / SIP) ──► LiveKit Audio Ingest

│

▼

Speech-to-Text (STT)

│

▼

LLM + Tools (agent.py + db.py)

│

▼

Murf Falcon (Ultra-Low Latency TTS)

│

▼

Audio Output ◄────────────── WebRTC Audio Sink

##
🚀 Key Features Built Over the 10 Days

-
**Ultra-Low Latency Indian Voice:** Powered by **Murf Falcon TTS**, Shiksha delivers natural, culturally resonant Indian English voice output with near-instant response times.
-
**Persistent Conversational Memory (SQLite):** Retains learner names, historical presentation goals, and specific practice needs across calls (`agent_memory.db`

).
-
**Curriculum-Driven Vocabulary Tools:** Dynamically fetches context-specific vocabulary drills from `exercises.json`

and evaluates sentences live.
-
**Outbound Daily Practice Telephony (LiveKit SIP):** Initiates automated daily check-in calls straight to a learner's phone.
-
**Human-in-the-Loop Escalation & Privacy Guardrails:** Detects severe learner frustration or explicit requests for human mentors, requests explicit permission, and logs sanitized support tickets with clear reference IDs.
-
**Call Analytics Dashboard:** A real-time Next.js dashboard displaying aggregated metrics (Total Calls, Successful Drills, Incomplete Calls) with zero personal transcripts exposed.
-
**Multi-Agent Specialist Handoff:** Dynamically transitions the call from Shiksha (general coach) to **Arjun** (Grammar Specialist with a distinct male voice persona) for complex syntactic queries without dropping the WebRTC session.

##
🛠️ Hardest Technical Challenges & Fixes

###
1. Hindi/Devanagari Pronunciation Glitches in TTS

-
**Issue:** Romanized Hindi text caused phonetic glitches in English voice models.
-
**Fix:** Structured the system prompt to output pure Hindi terms in native **Devanagari script** (`नमस्ते!`

), allowing Murf Falcon to pronounce localized nuances cleanly.

###
2. Next.js Dashboard Real-Time Cache vs. SQLite

-
**Issue:** Call logs updated in SQLite, but the Next.js `/dashboard`

served cached numbers.
-
**Fix:** Enforced dynamic rendering with `export const dynamic = "force-dynamic"`

and `export const revalidate = 0`

at the top of the dashboard page.

###
3. Context Preservation During Specialist Handoff

-
**Issue:** Switching agents risked losing conversational context, requiring the user to repeat themselves.
-
**Fix:** Implemented dynamic prompt-state switching in the same LiveKit session loop, passing the `handoff_reason`

and recent turns directly into Arjun's context.

##
💻 How to Run the Project Locally

###
1. Clone Repository & Setup Backend