SAQA

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SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Postgraduate Diploma in Artificial Intelligence

SAQA QUAL ID	QUALIFICATION TITLE
119095	Postgraduate Diploma in Artificial Intelligence
ORIGINATOR
MANCOSA Pty (Ltd)
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY			NQF SUB-FRAMEWORK
-			HEQSF - Higher Education Qualifications Sub-framework
QUALIFICATION TYPE	FIELD		SUBFIELD
Postgraduate Diploma	Field 10 - Physical, Mathematical, Computer and Life Sciences		Information Technology and Computer Sciences
ABET BAND	MINIMUM CREDITS	PRE-2009 NQF LEVEL	NQF LEVEL	QUAL CLASS
Undefined	120	Not Applicable	NQF Level 08	Regular-Provider-ELOAC
REGISTRATION STATUS		SAQA DECISION NUMBER	REGISTRATION START DATE	REGISTRATION END DATE
Registered		SAQA 158/22	2022-04-21	2025-04-21
LAST DATE FOR ENROLMENT		LAST DATE FOR ACHIEVEMENT
2026-04-21		2029-04-21

In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise.

This qualification does not replace any other qualification and is not replaced by any other qualification.

PURPOSE AND RATIONALE OF THE QUALIFICATION

Purpose:
The purpose of this Postgraduate Diploma in Artificial Intelligence is to provide learners with advanced skills and knowledge spanning a wide range of areas in Artificial Intelligence (AI). Artificial Intelligence aims to make computers and information systems more "intelligent" to solve complex problems and provide more natural and effective services to human beings. Artificial Intelligence has been a source of innovative ideas and techniques in information technology fields.

This qualification provides a comprehensive understanding of the core areas of Artificial Intelligence (AI). Learners will obtain both theoretical and practical knowledge and skills needed to design, build and apply AI systems in various organisational contexts. The growth of AI and its use in the development of systems that can reason and respond to increasingly complex situations has exploded. This advancement results in an increased demand for individuals who can create complex systems that can partner with, rather than replace or even augment, human users. The qualification aims to serve this demand by producing qualified learners who have exceptional technical skills to create new AI systems and understand the nature of the human environments in which the systems they build will be deployed.

The qualification is designed for information technology (IT) graduates and people working in information technology-related fields and provides the knowledge and skills IT professionals need to work with big data.

The qualification aims to facilitate an exploration of state-of-the-art knowledge of the core areas of AI, production of insightful and innovative ideas, solutions to complex problems and optimal application of AI systems. This will be the corollary of the knowledge depth obtained from this broad-based and industry-focused curriculum.

Upon completion of this qualification, qualifying learners will be able to:

Evaluate the organisational problems and needs to build and apply appropriate AI solutions.

Identify and examine the modern tools, techniques and methodologies in AI, and apply them to build intelligent systems.

Examine the trends and emerging technologies in AI and critically apply them to enhance AI strategies and approaches.

Critically assess the legal, ethical and environmental issues pertaining to the design, deployment and usage of AI systems.

Critically evaluate organisational strategies, policies and procedures in respect of AI and ensure they address all facets of AI and are integrated and aligned to enable the organisation to achieve its AI aspirations.

The graduate attributes will include the following:

Ability to solve real-world problems by applying specialised knowledge of artificial intelligence and to develop new artificial intelligence methodologies and applications.

Collaborate and work effectively with other people on artificial intelligence projects.

Make autonomous ethical and professional decisions in the development and deployment of artificial
intelligence systems that critically contribute to the development of ethical standards in the field of artificial intelligence.

Analyse complex data and conduct a comprehensive review of leading and current research in various artificial intelligence areas to produce significant insights.

Integrate trends, new insights and emerging technologies into artificial intelligence systems and applications to build more intelligent and optimal solutions.

Define and critically examine complex problems in the field of artificial intelligence and approach them with curiosity, critical thinking and creativity.

Rationale:
The advent of The Fourth Industrial Revolution (4IR) has thrust societies into a new world of technological advancement and rapid change. The 4IR is being powered by technologies, such as Artificial Intelligence (AI). These societal changes are manifested in the way people live, work and relate with one another and the environment. At the Salzburg Global Seminar, Sparvell (2018) admitted that AI is bridging social and cultural divides in workplaces and people's everyday lives in today's societies. AI is transforming government, delivering health care, creating art and creating new jobs. Frantz (2019) mentioned that even though repetitive jobs will be lost due to intelligence systems, many more new jobs will be created in the same vein. The new jobs are more engaging and less repetitive, hence creating a perfect opportunity for workers to focus on the parts of their jobs that may be the most satisfying to them. This in turn increases productivity and builds strong economies. In order to create a workforce that is capable to drive this new economy, the qualification is designed to produce graduates that have the most in-demand knowledge and expertise of AI.

Lauterbach (2018) observed that AI brings layers of problem-solving into any business environment. Training people and equipping them with the next generation's skills means that societies are empowered to solve the most complex problems. Lauterbach further argues that given that the nature of jobs is changing at a rapid pace, a broader societal dialogue is needed around education and preparing for the future.

The World Economic Forum (2019) announced that AI and Cloud Computing are the two most in-demand hard skills in 2019. This qualification coincides with the expectation of the World Economic Forum, for appropriate qualifications to be created to meet the skills demand. As technology continues to change at a rapid pace, AI has become a part of the fabric of society and a glue that holds society together as technology becomes more ubiquitous and pervasive. Dogson and Gann (2017) stressed that AI is a technology whose time has come, as it has surpassed human abilities in every aspect. They also indicated that AI is a new scientific infrastructure for research and skills acquisition that academic institutions must embrace and lead, lest they become increasingly irrelevant and eventually redundant. How the institutions of learning respond to this AI revolution will profoundly reshape education, innovation and society itself.

The qualification goes to the core of AI by incorporating knowledge areas, such as machine learning, computer vision and computational intelligence that adequately equip learners with the essential skills and competencies to be able to build AI-powered intelligent solutions for the highly dynamic and increasingly complex problems of our time.
The institution facilitates distance delivery, which allows learners to either retain their full-time employment or seek employment to complement their academic journey. Learners are provided with a plethora of support from e-learning platforms to assist the learning journey, an information management system that manages the learning process and provides learners with the necessary academic material and support, face-to-face workshops and consultation with lecturers.

The Fourth Industrial Revolution (4IR) is profoundly permeating and affecting all elements of contemporary societies and economies. The combination of technologies in AI and Machine Learning (ML) requires a suite of skills that can match the complexity and the innovativeness inherent in these intelligent systems. Presently, AI is increasingly integrated into people's everyday lives with personal assistants. AI applications and intelligent machines like Siri, Alexa, Watson, Cortana, LinkedIn, and Google AI Assistant are all popular applications that are used to conduct everyday tasks. These assistants can be used to pull information from the web, turn on home appliances, set reminders, talk to each other, and so much more. These types of machine learning and intelligent systems assistants are ever-evolving, so the demand for AI specialists and professionals is at an all-time high for this market and industry. The AI skill appetite cuts across various platforms, whether Microsoft Windows, iOS, an open-source platform, Google, or Android, the demand for skills remains high.

In many sectors of the South African economy, machines have already taken over monotonous jobs. Manufacturing and banking are prime examples. The primary goals of implementing AI solutions in these sectors include speeding up repetitive procedures by using robots or machines instead of humans, out-smarting human brains through "learning" and memory, and recognising patterns to make decisions instantly and efficiently. The purpose of the qualification is to ensure that learners are equipped to develop these solutions and be relevant in today's AI-augmented workplace. AI has also become a specialisation out of the traditional computer science and information technology qualifications, this is because AI has increasingly become a giant paradigm shift in modern computing and therefore requires a deeply scientific and logical approach to design computer systems that think and learn.

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING

Recognition of Prior Learning (RPL):
The institution has an approved Recognition of Prior Learning (RPL) policy applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications. RPL may be applied for access, credits from modules and credits for or towards the qualification.

RPL for access:

Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.

To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations should they be allowed entrance into the qualification.

RPL for exemption of modules:

Learners may apply for RPL to be exempted for modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

RPL for credit:

Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.

Credit shall be appropriate to the context in which it is awarded and accepted.

Entry Requirements:
The minimum entry requirement for this qualification is:

Bachelor of Computer and Information Sciences, NQF Level 7.
Or

Bachelor of Computer Information Systems, NQF Level 7.
Or

Bachelor of Science in Computer Science and Information Systems, NQF Level 7.
Or

A relevant Bachelor's degree in the related field, NQF Level 7.
Or

Advanced Diploma in Computer Systems Engineering, NQF Level 7.
Or

Advanced Diploma in Computer Science, NQF Level 7.
Or

A relevant Advanced Diploma in the related field, NQF level 7.

RECOGNISE PREVIOUS LEARNING?

QUALIFICATION RULES

This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 120 Credits.

Compulsory Modules, Level 8, 120 Credits

Multi-Agent Systems, 20 Credits.

Computational Intelligence, 20 Credits.

Programming, 20 Credits.

Machine Learning, 20 Credits.

Computer Vision, 20 Credits.

Research Project, 20 Credits.

EXIT LEVEL OUTCOMES

1. Demonstrate the ability to use a range of specialised skills to identify, analyse and address complex or abstract problems drawing systematically on the body of knowledge and methods appropriate to a field, discipline or practice.
2. Demonstrate knowledge of and engagement in an area at the forefront of a field, discipline or practice; an understanding of the theories, research methodologies, methods and techniques relevant to the field, discipline
or practice; and an understanding of how to apply such knowledge in a particular context.
3. demonstrate the ability to critically review information gathering, synthesis of data, evaluation and management processes in specialised contexts in order to develop creative responses to problems and issues.
4. Demonstrate the ability to use a range of specialised skills to identify, analyse and address complex or abstract problems drawing systematically on the body of knowledge and methods appropriate to a field, discipline or
practice.
5. Evaluate organisational strategy, policies, and procedures in respect of AI and ensure they address all facets of AI and are integrated and aligned to enable the organisation to achieve its AI aspirations.

ASSOCIATED ASSESSMENT CRITERIA

Associated Assessment Criteria for Exit Level Outcome 1:

Evaluate organisational problems and needs to build and apply appropriate AI strategies and solutions.

Assess methodologies for elicitation and assessment of organisational artificial intelligence needs to ensure suitability and efficacy.

Interrogate and understand the importance and benefit of applying appropriate strategies towards the identification and consideration of organisational artificial intelligence needs.

Associated Assessment Criteria for Exit Level Outcome 2:

Identify and examine the modern tools, techniques, and methodologies in AI, and apply them to build intelligent systems.

Assess appropriate methodologies for building and implementing artificial intelligence systems to ensure suitability and efficiency.

Evaluate intelligent systems in Artificial Intelligence to ensure optimal performance.

Associated Assessment Criteria for Exit Level Outcome 3:

Identify and discuss trends and emerging technologies in Artificial Intelligence to ensure understanding and awareness.

Apply trends and emerging technologies to existing processes and systems in Artificial Intelligence.

Examine new strategies and approaches in Artificial Intelligence to ensure the production of desired results.

Examine the trends and emerging technologies in AI and critically apply them to enhance the AI strategies and approaches.

Associated Assessment Criteria for Exit Level Outcome 4:

Assess the legal, ethical, and environmental issues pertaining to the design, deployment and usage of AI systems.

Identify and assess standards of application and usage of artificial intelligence systems and processes to gain an understanding of the application.

Discuss frameworks for deployment of artificial intelligence systems to increase comprehension of AI systems.

Discuss problems and approaches at the cutting edge of research, from a Machine Learning and Artificial Intelligence perspective.

Discuss frameworks for securing information and systems to develop a better understanding of the external environmental impact on the AI systems.

Associated Assessment Criteria for Exit Level Outcome 5:

Assess organisational policies and procedures with respect to AI to understand the integration.

Define and evaluate organisational strategies with respect to artificial intelligence to enable the organisation to achieve its AI aspirations.

Integrate, align and critically assess organisational objectives pertaining to artificial intelligence to enable the organisation to achieve its AI aspirations.

INTEGRATED ASSESSMENT
The nature of assessment and feedback to learners serves as an important factor when evaluating learners' learning experience and the institution has adopted an effective assessment strategy that uses multiple components.

The institution has recognised a change in its learner profile and more significantly in the learning needs of learners, nationally and internationally. There have been a number of advancements in education technology that better supports learner success. In pursuit of global relevance and enhanced learner success, the institution has taken the decision to participate in this global trend and will initiate the natural progression from distance to online for its qualifications and in doing so, the institution has undertaken a review of its strategy.

Formative assessment:
Formative assessment consists of knowledge checks, reflective assessments and learning community assessment.
Knowledge checks may comprise matching columns, multiple-choice questions, true and false and drag and drop assessment methods. Reflective assessments may consist of a short essay, long essay/case studies and posting comments/peer review.

A formative process through which learners can experience assessment as a part of learning, rather than as a separate evaluative process. This includes short essays and long essays/cases studies. The projects and case studies (formative assessments) at this level are structured in such a way that it requires learners to practice and demonstrate a number of higher-order skills, including the ability to articulate, evaluate and appropriately use evidence. These projects generally have a strong research focus geared toward preparing the learner for Master's level study. The scheme of work includes assignments based on the learning material and learners are given feedback. The process is continuous and focuses on smaller sections of the work and a limited number of outcomes.

Summative assessment:
Examinations or equivalent assessments such as a research essay or portfolio to determine a representative selection of the outcomes practised and assessed in the formative stage. Summative assessment also tests the learner's ability to manage and integrate a large body of knowledge to achieve the stated outcomes of a module. Summative assessment consists of projects, pen/paper exams, a portfolio of evidence and a dissertation.

The summative assessments carry a 60% weighting and comprise module projects or examinations, which requires learners to demonstrate immediate connections between theories, concepts, and skills and their immediate utility in current or future career scenarios. Completing module projects provides evidence of high-level cognitive skills (i.e. creation, application, analysis, and evaluation) and requires learners to strategise and plan how to approach complex problems that are relevant to the proposed scenario.

INTERNATIONAL COMPARABILITY

The South African qualification is internationally comparable with similar qualifications offered by the following international countries.

Country: Sri Lanka
Institution: University of Moratuwa
Qualification Title: Postgraduate Diploma in Artificial Intelligence

Purpose of Qualification:
The purpose of the Postgraduate Diploma in Artificial Intelligence at the University of Moratuwa is to impart the knowledge of theory and applications of modern Artificial Intelligence technologies to devise intelligent software solutions for tomorrow's world thereby ensuring multifaceted career paths for potential candidates.

Qualification structure:

Programming Essentials for Artificial Intelligence.

Essentials of Artificial Intelligence.

Evolutionary Computing.

Distributed Computing Concepts for AI.

Artificial Cognitive Systems.

Deductive Reasoning and Logic Programming.

Mathematics for Artificial Intelligence.

Neuroscience and Neurocomputing.

Fuzzy Reasoning.

Software Agents and Swarm Intelligence.

Natural Language Processing.

Data Mining and Data Warehousing.

Cryptography and Security Mechanisms.

Semantic Web and Ontological Engineering.

Intelligent Solutions for Industry.

Similarities:
Both qualifications have a duration of one year. They embed new technologies and trends in artificial intelligence,
as well as a research project to allow learners to develop research capacity in areas of artificial intelligence. The curriculum offered by the University of Moratuwa is similar to the curriculum offered by the South African qualification, especially in areas such as Computational Intelligence, Machine Learning, Multi-agent systems and programming for artificial intelligence.

Differences:
The qualification for the University of Moratuwa contains elective modules, whereas the South African qualification contains only core modules. The South African qualification is offered via distance learning mode, whereas the University of Moratuwa's qualification is offered in contact learning mode.

Country: United Kingdom (UK)
Institution: Heriot Watt University
Qualification Title: Postgraduate Diploma in Artificial Intelligence

Purpose of Qualification:
The purpose of the Postgraduate Diploma in Artificial Intelligence is to impart the understanding and skills to develop intelligent software applications, such as those involving evolutionary computation and learning. Learners will develop skills in specialist areas with clear applications in the industry, including pattern recognition and machine learning. The final project enables learners to apply the knowledge of artificial intelligence to solve industrial problems.

Qualification structure:

Artificial Intelligence and Intelligent Agents.

Intelligent Robotics.

Data Mining and Machine Learning.

Research Methods and Project Planning.

3D Graphics and Animation.

Advanced Interaction Design.

Advanced Software Engineering.

Big Data Management.

Computer Games Programming.

Biologically Inspired Computation.

Conversational Agents and Spoken Language Processing.

Software Engineering Foundations.

Similarities:
The UK qualification contains modules such as Artificial Intelligence and Intelligent Agents, Biologically Inspired Computation, Advanced Software Engineering, Data Mining and Machine Learning, 3D Graphics and Animation and Research Methods and Project Planning, which relate favourably with content in the South African qualification, such as Multi-Agent Systems, Computational Intelligence, Programming, Machine Learning, Computer Vision and Research Project respectively. In addition, both qualifications have a research project, which enables further development and consolidation of skills introduced in the taught courses, applying them to a challenging practical problem in the subject areas of Artificial Intelligence.

Differences:
The South African qualification has a duration of one year, whereas the Heriot Watt University's qualification has a duration of nine months. The Postgraduate Diploma in Artificial Intelligence offered by Heriot Watt University contains elective modules, whereas the South African qualification contains only core modules. The South African qualification is offered via distance learning mode, whereas Heriot Watt University's qualification is offered via contact learning mode.

Country: Singapore
Institution: Emeritus Institute of Management
Qualification Title: Postgraduate Diploma in Machine Learning and Artificial Intelligence

Purpose of Qualification:
Artificial intelligence and machine learning algorithms are transforming systems, experiences, processes, and entire industries. It's no wonder that business leaders see these data-driven technologies as fundamental for the futureand that practitioners fluent in both fields are in high demand. The Emeritus Institute of Management is fascinated by the world-changing potential of Artificial Intelligence, and Postgraduate Diploma in Machine Learning and Artificial Intelligence qualification is purposed to help learners understand the fundamentals of Artificial Intelligence and Machine Learning and how to apply them to solve complex real-world problems.

Qualification structure:

Applied Artificial Intelligence.

Intelligence Agents and Uninformed Search.

Heuristic Search.

Introduction to Artificial Intelligence.

AI applications: Natural Language Processing.

Constraint Satisfaction Problems.

Reinforcement Learning.

Applied Machine Learning.

Supervised Learning.

Unsupervised Learning.

Similarities:
The modules and content in the Emeritus Institute of Management's qualification are related to modules and content in the South African qualification. For example, Intelligence Agents and Uninformed Search, Heuristic Search, Reinforcement Learning, AI applications: Natural Language Processing and Applied Machine Learning are related to the South African qualification modules, such as Multi-Agent Systems, Computational Intelligence, Programming, Machine Learning and Computer Vision.

Differences:
The Emeritus Institute of Management's qualification has a duration of nine months, whereas the South African qualification has a duration of 12 months. The South African qualification is more practical and research-oriented to the extent that it offers programming as a core module to provide a firm grounding in Python, while the Postgraduate Diploma in Machine Learning and Artificial Intelligence for Emeritus Institute of Management expects learners to possess knowledge of Python programming.

ARTICULATION OPTIONS

This qualification allows possibilities for both vertical and horizontal articulation.

Horizontal Articulation:

Bachelor of Science Honours in Applied Mathematics, NQF Level 8.

Bachelor of Science Honours in Computer Science, NQF Level 8.

Bachelor of Engineering in Computer and Electronic Engineering, NQF Level 8.

Bachelor of Engineering in Computer Engineering, NQF Level 8.

Vertical Articulation:

Master of Science in Machine Learning and Artificial Intelligence, NQF Level 9.

MODERATION OPTIONS

N/A

CRITERIA FOR THE REGISTRATION OF ASSESSORS

N/A

NOTES

N/A

LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION:

NONE

PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION:

This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here.

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