LGC Concept AI 🎓🤖

Learning Concepts. Writing Exams. Building Confidence.

LGC Concept AI is an Anna University–oriented AI learning assistant built to help engineering students understand theory deeply, structure answers correctly, and write confident 13-mark responses in the expected university exam format.

The system prioritizes concept clarity, exam relevance, and mental confidence, making it especially effective for slow learners, first-generation engineers, and theory-heavy subjects.

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🚀 Project Vision

Many engineering students struggle not because concepts are impossible, but because explanations are:

• Too abstract
• Not exam-oriented
• Poorly structured for Anna University evaluation

LGC Concept AI solves this gap by behaving like a patient senior or tutor who explains concepts:

• Clearly
• Step-by-step
• In a marks-aware, exam-ready structure

The goal is not just answer generation, but learning that survives exam pressure.

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✨ What’s New in Version 2.0

Version 2.0 introduces a mode-driven learning system, separating learning behaviors clearly and intentionally.

🔹 Fast Learn Mode (New)

• Designed for quick clarity and last-minute revision
• Provides key takeaways only
• No long explanations or exam structuring
• Ideal when time is limited

⚠️ Not suitable for deep learning or coding questions
👉 Users are guided to Learn Mode when depth is required

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🔹 Learn Mode (Enhanced)

• Fully exam-oriented
• Strict Anna University question scope enforcement
• Structured answers suitable for 13-mark questions
• Aspect-aware answering:
  • Definition
  • Construction
  • Working
  • Comparison
  • Applications
  • Advantages / Limitations

New in v2.0:
➡️ Core Points / Mental Model Extraction
After a full explanation, students can extract 5–7 memory-friendly core points to reinforce understanding and revision.

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🔹 Clear Doubt Mode (Standardized)

• Designed for micro-clarifications
• Answers only the specific doubt
• Short, direct, and focused
• Avoids re-teaching the entire topic

Perfect when:

• You mostly understand the concept
• You’re stuck at one small point
• You need clarity without overload

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🔹 Verify Understanding (Teach-Back Mode — Core Philosophy)

One of the strongest pillars of LGC Concept AI is learning by explaining.

In Teach-Back Mode:

1. The student explains a concept in their own words
2. The AI:
   • Encourages first
   • Checks conceptual correctness
   • Identifies missing logic
   • Points out mistakes briefly
   • Motivates the student to retry

“If you can explain it clearly, you understand it.”

This mode verifies real understanding, not memorization.

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🧠 Answer Structure (Anna University Preferred)

In Learn Mode, responses follow a marks-aware structure:

• Definition (≈2 marks)
• Construction / Components (≈3 marks)
• Working Principle (≈4–5 marks)
• Applications
• Advantages & Limitations
• One clearly marked analogy (not for exam writing)

Answers strictly match what is asked — nothing extra, nothing missing.

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🔁 Learning Experience Design

• Scroll-based long answers
• Previous responses remain visible
• Continuous learning flow (not form-based)
• Reduced cognitive load and exam anxiety

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🏗️ Tech Stack

Frontend

• React + Vite
• Clean, distraction-free UI
• Mobile-friendly layout

Backend

• Node.js + Express
• Mode-based routing
• Prompt isolation per learning mode

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🤖 AI Strategy (Mode-Wise)

LGC Concept AI uses mode-isolated prompts and behaviors to prevent learning intent from being mixed.

Each mode is deliberately constrained.

Learn Mode

• Model: NVIDIA Nemotron
• Behavior:
  • Full exam-oriented explanations
  • Aspect-aware (definition, working, applications, etc.)
  • Strict Anna University scope control
  • Supports Core Points / Mental Model extraction

Fast Learn Mode

• Model: NVIDIA Nemotron
• Behavior:
  • Key takeaways only
  • No long explanations
  • No exam structuring
  • No analogies
• Purpose:
  • Quick clarity
  • Last-minute revision
  • Time-constrained learning

Fast Learn is intentionally not designed for coding or deep theory. Users are guided to Learn Mode when depth is required.

Clear Doubt Mode

• Model: NVIDIA Nemotron
• Behavior:
  • Answers only the specific doubt
  • Short, direct, and focused
  • No re-teaching

Verify Understanding (Teach-Back Mode)

• Reasoning-focused evaluation
• Encourages first, then evaluates
• Identifies mistakes and missing logic
• Does not re-teach the concept

This separation prevents mode-bleeding and preserves learning intent.

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🔐 Privacy & Cost Philosophy

• No forced subscriptions
• No hidden monetization
• Minimal data storage
• Lightweight and sustainable architecture

Learning needs investment in time and consistency, not money.

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🎯 Target Audience

• Anna University engineering students
• Slow learners struggling with theory
• Students who understand concepts but panic in exams
• Learners who want clarity over shortcuts

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📖 Future Enhancements (Planned)

• Reflection prompts (“What did I correct?”)
• Non-gamified learning streaks
• Subject-wise structuring
• Offline revision mode
• Conversational chat-like UI

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🙏 Acknowledgements

• Anna University exam pattern & evaluation style
• Open learning communities
• OpenRouter API (NVIDIA and open models)
• Brevo API (transactional email delivery)