Contact person: AI-Driven EdTech App & Web Development

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Location: KUALA LUMPUR, Malaysia

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"Project Brief – AI-Powered Education Modules (Phase 1)

Platform:

Mobile app (Flutter, Android & iOS)

Web-based dashboard (React / Vue / Angular — developer’s choice)

Backend: Node.js / Python with REST APIs

Database: PostgreSQL / MySQL (with offline sync layer)

Offline–Online sync capability for all modules

Scope Overview

We are building 5 integrated AI modules for an EdTech platform to enhance student learning, teacher productivity, and parental insights.
Modules to be developed in Phase 1:

Module A — Adaptive Learning Paths

Module B — AI Question & Exam Generator

Module C — Knowledge Gap Analysis

Module D — Automated Essay Grading

Module E — Mood Tracking (EMA)

Module Details
Module A — Adaptive Learning Paths (Personalised Recommendations)

Objective: Recommend a ranked sequence of topics/activities (notes, latihan tubi, assignments, recordings) that maximizes mastery and engagement.

Key Features:

Personalisation using IQ/EQ profiles, topic mastery, attendance, and engagement signals

Sequence model (DKT/SAKT) + contextual multi-armed bandit for activity selection

Spaced repetition scheduling

Explainable recommendations (top 3 reasons displayed to student)

Inputs: IQ/EQ data, mastery estimates, practice performance, class timetable, attendance logs, streaks, device constraints.

Outputs: JSON array of top N recommendations with {topicId, activityType, expectedGain, confidence, reasons[]}

APIs:

GET /ai/adaptive/recs?userId=&subjectId=&n=10

POST /ai/adaptive/feedback

Offline/Sync: Cache next 20 recs; queue feedback and reconcile on reconnect.

KPIs: +X% mastery gain, reduced time-to-mastery, higher retention.

Module B — AI Question & Exam Generator

Objective: Generate syllabus-aligned questions & mock exams in BM/EN with difficulty control.

Key Features:

Template-driven MCQs/short answers with distractor banks

LLM-assisted drafting with quality filters (plagiarism, hallucination, bias scan)

IRT calibration from pilot responses

Teacher UI for human-in-loop review

Outputs:

Item: {stem, options, answerKey, rationale, difficulty, tags, lang}

Paper: {items[], blueprint, timing, markingScheme}

APIs:

POST /ai/qgen/item

POST /ai/qgen/paper

POST /ai/qgen/validate

Offline/Sync: Cache generated sets; sync metadata later.

KPIs: Teacher acceptance rate, <1% item error rate, predictive validity for difficulty.

Module C — Knowledge Gap Analysis

Objective: Identify weak skills per student and suggest improvement sequence.

Key Features:

Bayesian Mastery Model + Topic Knowledge Graph

Diagnostic mini-tests for low-confidence areas

Class heatmap for teachers; simplified summaries for parents

Outputs: {topicId, mastery, confidence, blockers[], nextBest[]}

APIs:

GET /ai/gaps/student/{id}?subjectId=

GET /ai/gaps/class/{classId}

Offline/Sync: Store snapshots for dashboard; merge on sync.

KPIs: Correlation with future performance; reduced reteach time.

Module D — Automated Essay Grading (BM & EN)

Objective: Score essays (100–600 words) with rubric-based subscores + feedback.

Key Features:

Pre-checks for plagiarism, AI-generated likelihood, length, language ID

Fine-tuned transformer for scoring: content, coherence, grammar, vocab, mechanics

Teacher override + calibration feedback loop

Outputs: {overall, subscores{}, feedback[], confidence, flags{}}

APIs:

POST /ai/essay/score

POST /ai/essay/explanations

Offline/Sync: Cache last 10 submissions; sync later.

KPIs: ≥ 0.75 quadratic weighted kappa vs human grading, <5s processing time.

Module E — Mood Tracking (EMA)

Objective: Capture mood/energy/stress signals to personalise learning support.

Key Features:

Opt-in emoji-based mood survey 2–3×/week

Passive engagement signals (optional)

Aggregates for parents/teachers without raw journal text

Outputs: {mood, energy, stress, context, timestamp}

APIs:

POST /ai/mood/entry

GET /ai/mood/summary?window=30d

Offline/Sync: Queue entries and sync by server time.

KPIs: Completion rate, correlation with engagement dips.

General Requirements

Authentication & Roles: Student, Teacher, Parent, Admin

UI/UX: Mobile-first design, simple navigation for students

Data Privacy: PDPA-compliant storage; encrypted in transit & at rest

Explainability: All AI outputs should include reasoning where applicable

Offline Support: All modules must work offline and sync later

Scalability: Backend should handle 100,000–500,000 concurrent live users for live classes

Deliverables

Fully functional API layer for each module

Mobile app integration (Flutter) for Student/Parent/Teacher dashboards

Web dashboard for teachers/admins

Documentation: API specs, deployment steps, and user manuals

Testing: Unit tests + integration tests for all modules

Deployment on AWS (provided account)

Timeline

Week 1–2: Architecture setup, DB schema, and API stubs

Week 3–6: Module A & B core logic + integration

Week 7–9: Module C & D

Week 10: Module E

Week 11: Testing, bug fixes, documentation

Week 12: Final deployment" (client-provided description)