Programme Title
Master in Artificial Intelligence for Industry 4.0
Course Code

Programme Includes:(Apprenticeship/ Placement or Internship)

Taught Units Only (No Apprenticeship/No Placement/ No Internship)
MQF Level

Level 7
Type(refer to Appendix 1 for Parameters)

Accreditation Status(Note: If the Type is Award or Qualification, therefore by default, the Accreditation Status should be Accredited)

Accredited via MCAST’s Self Accreditation Process (MCAST holds Self-Accrediting Status as per 1st schedule of Legal Notice 296/2012)
Mode of Delivery

Blended Learning
Duration (hours or years)

3 Years
Total Number of Credits

Total Learning Hours (25 Total Learning Hours for each ECTS)

2250 Hours
Target Audience

Target Group (the type of learners that the educational institution anticipates joining this programme)

Programme Fees

(Applicable for Non-EU and Non-EEA Countries) – the fee mentioned here is indicative.  Please refer to, as well as to MCAST’s MG2I International Section for any updated fees as well as terms of payments.
Date of Next Student Intake

Language of Instruction

The official language of instruction at MCAST is English. All notes and textbooks are in English (except for language courses, which will be in the respective language being instructed). International candidates will be requested to meet English language certification requirements for access to the course.
Application Method

Applications to full-time courses are received online via the College Management Information System. Applicants can log-in using Maltese Electronic ID (eID) in order to access the MCAST Admissions Portal directly and create one’s own student account with the identity being verified electronically via this secure service.

Non-EID applicants need to request account creation though an online form after that they confirm that their local Identification Document does not come with an EID entitlement. . Once the identity is verified and the account is created on behalf of the applicant, one may proceed with the online application according to the same instructions applicable to all other applicants.

For more information about how to apply online for a course at MCAST, please visit:

Information for Non-EU Citizens

Non-EU candidates require a study visa in order to travel to Malta and join the course applied for. For further information re study-visa please access
Address where the Programme will be Delivered

MCAST has four campuses as follows:

MCAST Main Campus
Triq Kordin,
Paola, Malta

All courses except for courses delivered by the Institute for the Creative Arts, the Centre of Agriculture, Aquatics and Animal Sciences and the Gozo Campus are offered at the Main Campus address (above).

Courses delivered by the Institute for the Creative Arts, the Centre of Agriculture, Aquatics and Animal Sciences, or the Gozo Campus, are offered in one of the following addresses as applicable:

Institute for the Creative Arts
Mosta Campus
Misraħ Għonoq Tarġa Gap,

Institute of Applied Sciences
Centre of Agriculture, Aquatics and Animal Sciences,
Luqa Road, Qormi

Gozo Campus
J.F. De Chambray Street
MCAST, Għajnsielem

In the case of courses delivered via Online Learning, students will be following the programme from their preferred location/address.

Programmes delivered via Blended Learning, and which therefore contain both an online and a face to face component shall be delivered as follows:

Face to Face components – as per above address instructions
Online components – from the student’s preferred address.

Course Description/Deskrizzjoni tal-Kors


The implementation of Artificial Intelligence (AI) within industry 4.0 influences all industries including manufacturing, public sector, education and many more, particularly since industry 4.0 focuses heavily on interconnectivity, automation, machine learning and real time data. The MCAST Master in AI for Industry 4.0 programme focuses on the key knowledge required for solving business challenges and yielding competitive advantage through the application of Artificial Intelligence technologies. It provides the theoretical and practical knowledge to work across industries and implement AI where needed. Graduates will be well versed in the Fundamentals of AI, Machine Learning, Neural Networks, Social Implications, Ethics, Regulation and Business Analysis proficiencies. The MCAST Master in AI for Industry 4.0 is taught by industry experts and leading academics who are actively engaged in successful careers in their respective fields.


L-implimentazzjoni tal-Intelliġenza Artifiċjali fi ħdan l-Industrija 4.0 taffettwa l-industriji kollha inklużi l-manifattura, is-settur pubbliku, l-edukazzjoni u ħafna aktar, partikolarment billi l-industrija 4.0 tiffoka ħafna fuq l-interkonnettività, l-awtomazzjoni, it-tagħlim tal-magni u d-dejta f’ħin reali. Il-Master fl-AI għall-Industrija 4.0 tal-MCAST jiffoka fuq l-għarfien ewlieni meħtieġ biex jissolvew l-isfidi tan-negozji, liema għarfien jagħti vantaġġ kompetittiv permezz tal-applikazzjoni ta’ teknoloġiji ta’ Intelliġenza Artifiċjali. Il-kors jipprovdi l-għarfien teoretiku u prattiku biex persuna taħdem fl-industrija u timplimenta l-AI fejn meħtieġ. Il-gradwati isiru familjari sew mal-Kunċetti Bażiċi tal-AI, il-Machine Learning, in-Netwerks Newronali, l-Implikazzjonijiet Soċjali, l-Etika, ir-Regolamentazzjoni u l-profiċjenzi meħtieġa fl-Analiżi tan-Negozju. Il-Master fl-AI għall-Industrija 4.0 huwa mgħallem minn esperti fl-industrija u akkademiċi ewlenin li huma involuti b’mod attiv f’karrieri ta’ suċċess fl-oqsma rispettivi tagħhom.
Career Opportunities

This is conducive to the more senior business, management, technologist, and other senior personnel who will act as change agents in their organizations through the application of AI.
Entry requirements(Refer to Programme Specification)

A first degree (MQF Level 6) in the sciences or in the technological or social sciences domains.

Other entry requirements
A Level 5 qualification with at least three years’ work experience (evidenced) in a relevant area.

Programme Learning Outcomes (Refer to Programme Specification)

At the end of the programme the students are able to:
1.Appraise the technologies that constitute AI;
2.Identify and implement appropriate AI technology to address specific industry challenges;
3.Evaluate proposals involving AI technologies;
4.Ensure increased operational efficiency, lower costs, improved customer experience and enhance competitive advantage.
Teaching, Learning and Assessment Procedures

The programmes offered are vocational in nature and entail both theoretical lectures delivered in classes as well as practical elements that are delivered in laboratories, workshops, salons, simulators as the module requirements dictate.

Each module or unit entails a number of in person and/or online contact learning hours that are delivered by the lecturer or tutor directly (See also section ‘Total Learning Hours).

Access to all resources is provided to all registered students. These include study resources in paper or electronic format through the Library and Resource Centre as well as tools, software, equipment and machinery that are provided by the respective institutes depending on the requirements of the course or module.

Students may however be required to provide consumable material for use during practical sessions and projects unless these are explicitly provided by the College.

All Units of study are assessed throughout the academic year through continuous assessment using a variety of assessment tools. Coursework tasks are exclusively based on the Learning Outcomes and Grading Criteria as prescribed in the course specification. The Learning Outcomes and Grading Criteria are communicated to the Student via the coursework documentation.

The method of assessment shall reflect the Level, credit points (ECTS) and the schedule of time-tabled/non-timetabled hours of learning of each study unit. A variety of assessment instruments, not solely Time Constrained Assignments/Exams, are used to gather and interpret evidence of Student competence toward pre-established grading criteria that are aligned to the learning outcomes of each unit of the programme of study.

Grading criteria are assessed through a number of tasks, each task being assigned a number of marks. The number of grading criteria is included in the respective Programme Specification.
The distribution of marks and assessment mode depends on the nature and objectives of the unit in question.

Coursework shall normally be completed during the semester in which the Unit is delivered.

Time-constrained assignments may be held between 8 am and 8 pm during the delivery period of a Unit, or at the end of the semester in which the Unit is completed. The dates are notified and published on the Institute notice boards or through other means of communication.

Certain circumstances (such as but not limited to the Covid 19 pandemic) may lead Institutes and Centres to hold teaching and assessment remotely (online) as per MCAST QA Policy and Standard for Online Teaching, Learning and Assessment (Doc 020) available via link

The Programme Regulations pertaining to this Programme’s MQF/EQF level available at: link, apply.

Grading System

All MCAST programmes adopt a Learner-centred approach through the focus on Learning Outcomes. The assessment of MCAST programmes is criterion-referenced and thus assessors are required to assess learners’ evidence against a pre-determined set of Learning Outcomes and Assessment Criteria.

For a student to be deemed to have successfully passed a unit, a minimum of 50% (grade D) must be achieved.

All full time units are individually graded as follows:
A* (90-100)
A (80-89)
B (70-79)
C (60-69)
D (50-59)
Unsatisfactory work is graded as ‘U’.

Work-based learning units are graded on a Pass/Fail basis only.

Detailed information regarding the grading system may be found in the Programme Regulations pertaining to this programme’sMQF/EQF Level available at: link

Contact details for Further Learning Opportunities

MCAST Career Guidance
Tel: 2398 7135/6
Regulatory Body/ Competent Authority Contact Details (where applicable – in the case of a programme leading to Regulated Profession)

Download PIT (Public Information Template) pdf here  

Last updated: 2024-07-09