EGK AIQuest
Malaysia

The emergence of Artificial Intelligence and Machine Learning as foundational technologies of the twenty-first century has created an urgent and unprecedented demand for skilled graduates in Malaysia. The nation's commitment to the MyDIGITAL Blueprint, the IR4.0 National Policy, and the continued evolution of the Malaysia Higher Education Blueprint 2015–2025 collectively demand a new generation of AI-literate, ethically grounded, and technically proficient professionals.

EGK AIQuest Malaysia is a higher education programme proposal developed by EGK Microelectronic Solutions Group Sdn. Bhd. — builders of Malaysia's pioneering EGK CodeQuest coding education platform. This proposal presents a complete, MQA-compliant, OBE-structured academic programme framework for two qualification levels: a Diploma in Artificial Intelligence and Machine Learning (MQF Level 4) and a Bachelor of Artificial Intelligence and Machine Learning Honours (MQF Level 6).

Malaysia's digital economy is projected to contribute up to 22.6% of GDP by 2025. The World Economic Forum estimates that AI and data-related roles will account for more than 85 million job displacements and 97 million new positions globally by 2025. Within Malaysia, demand for AI practitioners has consistently outpaced supply across financial services, manufacturing, healthcare, and government sectors.

Existing computing programmes in Malaysia address general software engineering and information systems. A dedicated AI and Machine Learning programme — designed around Malaysia's local datasets, Bahasa Malaysia NLP challenges, palm oil agricultural AI, and smart city applications — fills a critical gap in the nation's talent pipeline.

Programme Educational Objectives describe what graduates are expected to achieve within 3–5 years of graduation. These are distinct from Programme Learning Outcomes (PLO) and are aligned with the institution's mission and national development goals.

Outcome-Based Education (OBE) is the foundational philosophy required by the Malaysian Qualifications Agency for all higher education programme accreditation. The OBE model operates in a hierarchical structure: institutional mission informs Programme Educational Objectives (PEO), which are operationalised through Programme Learning Outcomes (PLO), which are demonstrated at the course level through Course Learning Outcomes (CLO), assessed through structured assessments, and quantified through Student Learning Time (SLT).

PLOs are measurable statements describing what a graduate should know, do, and be upon completing the programme. The following 9 PLOs are aligned to the MQF domain structure and MQA Computing Programme Standards:

The following demonstrates the OBE structure for the core Machine Learning course (3 credits), showing how CLOs map to PLOs and are assessed through aligned methods.

The Diploma in AI & ML provides a foundational technical pathway, preparing graduates for entry-level AI support roles or articulation into the degree programme.

EGK AIQuest Malaysia employs an OBE-aligned assessment strategy where every assessment task directly evaluates one or more CLOs, which in turn map to PLOs. The assessment design follows MQA's recommended split of continuous assessment (40–60%) and final assessment (40–60%), ensuring consistent measurement of graduate attribute attainment throughout the programme.

MQA requires a structured week-by-week teaching plan for all courses. Below is the complete plan for the core Machine Learning course.

Under the Malaysian Qualifications Framework, 1 credit = 40 hours of Student Learning Time (SLT). For a 3-credit course, the total SLT is 120 hours, distributed across scheduled and unscheduled learning activities.

EGK AIQuest Malaysia directly supports all five system aspirations of the Malaysia Higher Education Blueprint: access, quality, equity, unity, and efficiency. The programme's bilingual delivery (Bahasa Malaysia + English), emphasis on ethics and national identity, industry-embedded capstone projects, and structured industrial training are direct responses to the Blueprint's call for holistic, entrepreneurial, and balanced graduates.

The curriculum is calibrated against Malaysia's industrial and economic transformation priorities:

The following matrix summarises CLO-to-PLO alignment for core degree courses. A ✔ indicates a strong mapping where the course's CLOs directly address the stated PLO. This table is mandatory for MQA accreditation submission.

The following table provides SLT calculations for all Year 3 and Year 4 AI core courses. Each row represents a complete course. Total SLT = credits × 40 hours per the Malaysian Qualifications Framework.