🎓 Higher Education Edition | MQF Level 4 & 6 Compliant

EGK AIQuest
Malaysia

AI & Machine Learning Education for Malaysia's Diploma & Degree Students.
MQA-Compliant · OBE-Structured · Industry-Ready · UNESCO-Aligned

🇲🇾 Malaysia MyDIGITAL Ready — MOHE Blueprint Aligned

🚀 Order License Key 📱 Download iOS App View Curriculum

Credits (Degree)

PLO Outcomes

Core AI Courses

Year Programme

scroll to explore

Platform Capabilities

Everything You Need to
Master AI & Machine Learning

A complete higher education AI learning platform for Malaysian universities — interactive, bilingual, and aligned with MQA Computing Programme Standards.

🧠

Neural Network Simulator

Interactive visual neural network builder. Students design, train, and visualize deep learning architectures directly in the browser — no GPU setup needed.

Deep Learning

🐍

Python AI Playground

Full Python 3 environment with NumPy, pandas, scikit-learn and matplotlib. Real ML pipelines, auto-graded challenges, instant output — offline capable.

Python 3 · Scikit-Learn

📊

Data Science Studio

Upload Malaysian datasets — palm oil yields, traffic data, health records. Practice EDA, feature engineering, and model deployment on real local data.

Malaysia Datasets

🤖

AI Ethics Module

Mandatory Responsible AI module covering bias, fairness, transparency, and governance. Mapped to PLO7 (Ethics & Professionalism) and UN AI principles.

PLO7 Compliant

🏆

Assessment & Certification

OBE-structured assessments with CLO-PLO traceability. Continuous assessment (60%) + final exam (40%). Auto-generated MQA-style certificates.

OBE · MQA Format

🌐

Bahasa & English Bilingual

All content available in Bahasa Malaysia and English. NLP modules include Bahasa Melayu sentiment analysis — unique to Malaysian AI education.

BM + English

Learning Tracks

Two MQF Levels, One Vision:
Malaysia's AI Future

Structured pathways aligned to the Malaysian Qualifications Framework — from applied practitioner (Diploma) to AI innovator (Degree).

🎓 Diploma Track

AIQuest Foundation

MQF LEVEL 4 · 90–100 CREDITS · 2.5–3 YEARS

Programming Fundamentals & Data Structures
Introduction to AI & Machine Learning Basics
Data Analytics & Visualization
Database Systems & Web Development
Statistics & Discrete Mathematics
MPU Compulsory Subjects (Ethics, BM)
Industrial Training (4–6 months)
Pathway to Degree / Junior AI roles

🏅 Degree Track

AIQuest Honours

MQF LEVEL 6 · 120–140 CREDITS · 4 YEARS

Deep Learning, NLP & Computer Vision
Reinforcement Learning & Generative AI
MLOps & AI Software Engineering
Data Engineering & Cloud Platforms
Research Methods & Capstone Project
Industrial Training (6 months)
AI Ethics & Governance
Industry-led Final Year Project

Curriculum Structure

MQA-Compliant Curriculum
Year by Year

Structured under MQF Level 6, built with OBE principles. Each course maps to Programme Learning Outcomes and contributes to graduate attribute development.

🔹 Core Computing (40%)

Programming Fundamentals 3 cr

Data Structures 3 cr

Database Systems 3 cr

Web & Mobile Development 3 cr

Operating Systems Basics 2 cr

🤖 AI & Data (30%)

Introduction to Artificial Intelligence 3 cr

Machine Learning Basics 3 cr

Data Analytics 3 cr

Data Visualization 2 cr

📐 Mathematics (10%)

Discrete Mathematics 3 cr

Basic Statistics for AI 3 cr

🇲🇾 MPU + Industrial (20%)

Philosophy & Current Issues 2 cr

Ethics & Civilisation 2 cr

BM Communication 2 cr

Entrepreneurship Basics 2 cr

Industrial Training (4–6 months) 6 cr

📌 Semester 1

Programming I (Python) 3 cr

Discrete Mathematics 3 cr

Introduction to AI 3 cr

Communication Skills 2 cr

Islamic Studies / Moral Ed 2 cr

📌 Semester 2

Programming II (C++) 3 cr

Calculus for Computing 3 cr

Linear Algebra 3 cr

Probability & Statistics 3 cr

Philosophy & Critical Thinking 2 cr

📌 Semester 3

Data Structures & Algorithms 3 cr

Database Systems 3 cr

Operating Systems 3 cr

Computer Networks 3 cr

Bahasa Malaysia Communication 2 cr

📌 Semester 4

Software Engineering 3 cr

Data Engineering 3 cr

Object-Oriented Programming 3 cr

Entrepreneurship & Innovation 2 cr

Technical Communication 2 cr

📌 Semester 5

Machine Learning 3 cr

Data Mining 3 cr

AI Software Engineering (MLOps) 3 cr

Data Visualization & Analytics 3 cr

Ethics in AI 2 cr

📌 Semester 6

Research Methods in AI 3 cr

Cloud Computing for AI 3 cr

Human-Computer Interaction 3 cr

AI Project I 3 cr

Malaysian Society & Technology 2 cr

📌 Semester 7

Deep Learning 3 cr

Natural Language Processing 3 cr

Computer Vision 3 cr

Reinforcement Learning 3 cr

Generative AI & LLMs 3 cr

📌 Semester 8 + Capstone

Final Year Project I 3 cr

Final Year Project II 3 cr

Industrial Training (6 months) 12 cr

AI Policy & Governance 2 cr

Outcome-Based Education

9 Programme Learning
Outcomes (PLO)

Every course maps directly to these MQF-aligned PLOs, ensuring traceable, auditable, and MQA-compliant graduate attributes.

PLO 1

AI Knowledge

Apply foundational AI, ML, and computing knowledge to real-world problems

PLO 2

Critical Thinking

Analyse complex problems using HOTS and evidence-based reasoning

PLO 3

System Design

Design AI-driven solutions using appropriate models, tools and techniques

PLO 4

Technical Skills

Demonstrate practical coding, data, and ML engineering skills

PLO 5

Communication

Communicate AI concepts effectively in Bahasa Malaysia and English

PLO 6

Teamwork & Leadership

Collaborate in teams and demonstrate leadership in AI projects

PLO 7

Ethics & Professionalism

Practice responsible AI, ethical governance and professional conduct

PLO 8

Lifelong Learning

Pursue self-directed learning and adapt to emerging AI technologies

PLO 9

Societal Impact

Understand AI's role in Malaysian society, environment and national development

CLO–PLO Mapping Matrix

Audit-Ready Outcome
Traceability Matrix

MQA requires full CLO–PLO mapping for every course. This condensed matrix shows core programme courses mapped to all 9 PLOs — mandatory for accreditation.

Course	PLO1	PLO2	PLO3	PLO4	PLO5	PLO6	PLO7	PLO8	PLO9
Programming I & II	✔	✔	✔	✔		✔		✔
Data Structures & Algorithms	✔	✔	✔	✔				✔
Database Systems	✔	✔	✔	✔	✔			✔
Machine Learning	✔	✔	✔	✔	✔		✔	✔	✔
Deep Learning	✔	✔	✔	✔			✔	✔	✔
Natural Language Processing	✔	✔	✔	✔	✔		✔	✔	✔
Computer Vision	✔	✔	✔	✔			✔	✔	✔
Ethics in AI	✔	✔			✔	✔	✔	✔	✔
Research Methods	✔	✔	✔		✔	✔	✔	✔	✔
Capstone Project	✔	✔	✔	✔	✔	✔	✔	✔	✔

14-Week Teaching Plan

Sample Course: Machine Learning
Week-by-Week Structure

MQA requires a structured week-by-week teaching plan for every course. Below is the audit-ready plan for the core Machine Learning course (3 credits, SLT 120 hours).

Week 01

Introduction to ML — Lecture + Discussion

Week 02

Data Preprocessing — Lab Session

Week 03

Regression Models — Lecture + Tutorial

Week 04

Classification Algorithms — Lab + Assignment

Week 05

Model Evaluation Metrics — Case Study

Week 06

Overfitting & Regularization — Lab

Week 07

⚡ Midterm Assessment (20%)

Week 08

Clustering Algorithms — Lecture

Week 09

Decision Trees & Random Forest — Lab

Week 10

Ensemble Methods — Project Work

Week 11

Hyperparameter Tuning — Lab

Week 12

Ethics in ML — Group Discussion

Week 13

🏆 Project Presentation (30%)

Week 14

Revision & Course Review

Student Learning Time (SLT) — Machine Learning (3 Credits)

Activity	Format	Hours	% of SLT
Lectures	14 × 3 hrs	42	35%
Tutorials	7 × 2 hrs	14	12%
Laboratory	7 × 2 hrs	14	12%
Self-Directed Study	Individual / Group	35	29%
Assessment Preparation	Assignments + Project	15	12%
Total SLT (1 credit = 40 hrs)		120	100%

AI Learning Playground

Try the Python AI
Playground Live

Built-in Python interpreter for AI and ML practice. one-time installation only. Powered by EGK's offline-capable execution engine.

ml_basics.py — Linear Regression Demo

# EGK AIQuest Malaysia — ML Basics Demo
# Simulated Linear Regression

import math

# Training data: study hours vs exam score
hours = [1, 2, 3, 4, 5, 6, 7, 8]
scores = [52, 56, 63, 70, 75, 82, 88, 95]

n = len(hours)
mean_x = sum(hours) / n
mean_y = sum(scores) / n

# Calculate slope (m) and intercept (b)
numerator = sum((hours[i] - mean_x) * (scores[i] - mean_y) for i in range(n))
denominator = sum((hours[i] - mean_x) ** 2 for i in range(n))
m = numerator / denominator
b = mean_y - m * mean_x

print("=== Linear Regression Model ===")
print(f"Slope (m):     {m:.4f}")
print(f"Intercept (b): {b:.4f}")
print(f"Equation: score = {m:.2f} × hours + {b:.2f}")
print()

# Predictions
print("Predictions:")
for h in [3, 5, 9, 10]:
    pred = m * h + b
    print(f"  {h} hours → predicted score: {pred:.1f}")

# R² Score
ss_res = sum((scores[i] - (m * hours[i] + b))**2 for i in range(n))
ss_tot = sum((scores[i] - mean_y)**2 for i in range(n))
r2 = 1 - (ss_res / ss_tot)
print(f"\nR² Score: {r2:.4f}  ({'Good fit ✓' if r2 > 0.9 else 'Fair fit'})")

// OUTPUT CONSOLE

Click ▶ RUN to execute the ML demo...

📱 Available on iOS

Take EGK AIQuest
Anywhere You Go

Practice Python ML challenges, run neural networks, and complete quizzes offline with the official EGK AIQuest My iOS App.

Why Download the App?

✅ Run Python ML code offline
✅ Interactive quizzes & instant feedback
✅ Track your XP & progress
✅ Optimized for iPad & iPhone

🚀 Download on the App Store

Policy Alignment

Aligned to Malaysia's
National Education Frameworks

EGK AIQuest Malaysia is designed to satisfy all major Malaysian higher education policy and accreditation requirements.

📋

MQF Level 4 & 6

Full compliance with Malaysian Qualifications Framework credit, duration and domain requirements

✅

MQA Standards

Programme design follows MQA Computing Programme Standards with CLO–PLO audit trail

🎯

OBE Compliant

PEO → PLO → CLO mapping with SLT calculation and continuous assessment framework

🇲🇾

MOHE Blueprint

Aligned to Malaysia Higher Education Blueprint 2015–2025 and MyDIGITAL Strategy

🌍

UNESCO Aligned

Four pillars: Learn to Know, Do, Live Together, and Be — integrated throughout the programme

🏭

IR4.0 / Industry

Curriculum integrates IR4.0, FinTech AI, smart manufacturing and Malaysian use cases

EGK AIQuest
Malaysia

Everything You Need to
Master AI & Machine Learning

Two MQF Levels, One Vision:
Malaysia's AI Future

MQA-Compliant Curriculum
Year by Year

9 Programme Learning
Outcomes (PLO)

Audit-Ready Outcome
Traceability Matrix

Demonstration Video
EGKAIQuestMy

Sample Course: Machine Learning
Week-by-Week Structure

Try the Python AI
Playground Live

Take EGK AIQuest
Anywhere You Go

Why Download the App?

Aligned to Malaysia's
National Education Frameworks

Ready to Shape Malaysia's
AI Future?

EGK AIQuestMalaysia

Everything You Need toMaster AI & Machine Learning

Two MQF Levels, One Vision:Malaysia's AI Future

MQA-Compliant CurriculumYear by Year

9 Programme LearningOutcomes (PLO)

Audit-Ready OutcomeTraceability Matrix

Demonstration VideoEGKAIQuestMy

Sample Course: Machine LearningWeek-by-Week Structure

Try the Python AIPlayground Live

Take EGK AIQuestAnywhere You Go

Why Download the App?

Aligned to Malaysia'sNational Education Frameworks

Ready to Shape Malaysia'sAI Future?

EGK AIQuest
Malaysia

Everything You Need to
Master AI & Machine Learning

Two MQF Levels, One Vision:
Malaysia's AI Future

MQA-Compliant Curriculum
Year by Year

9 Programme Learning
Outcomes (PLO)

Audit-Ready Outcome
Traceability Matrix

Demonstration Video
EGKAIQuestMy

Sample Course: Machine Learning
Week-by-Week Structure

Try the Python AI
Playground Live

Take EGK AIQuest
Anywhere You Go

Aligned to Malaysia's
National Education Frameworks

Ready to Shape Malaysia's
AI Future?