Artificial Intelligence: An Introduction (Online)


artificial intelligence, n.

The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this.

source: Oxford English Dictionary

Artificial Intelligence (AI) has become ingrained in the fabric of our society, often in seamless and pervasive ways that may escape our attention day-to-day. The ability of machines to sense, process information, make decisions and learn from experience is a transformative tool for organizations, from governments to big business. However, these technologies pose challenges including social and ethical dilemmas.

This course provides an essential introduction to the key topics underpinning AI, including its historical development, theoretical foundations, basic architecture, modern applications, and ethical implications. The course investigates the future trajectory of AI and considers its potential for improving the world while highlighting pitfalls and limitations. It is aimed at a general audience, including professionals whose work brings them into contact with AI, and those with no prior knowledge of AI. The course aims to confer an appreciation of the ways in which our world has already been transformed by AI, to explain the fundamental concepts and workings of AI, and to equip us with a better understanding of how AI will shape our society, so we can converse fluently in the language of the future.

Programme details

Unit 1: What is intelligence?

  • The concept of “intelligence”
  • Differences between Weak AI and Strong AI
  • An overview of the history of AI from its beginnings to the present day
  • Examples of today’s real-world applications of AI


Unit 2: Artificial intelligence and society

  • Social questions around AI research and development
  • The impact of AI on government
  • Ethical implications of AI
  • Introduction to debates on AI and social structure
  • The challenges and opportunities of AI


Unit 3: Systems and agents

  • Modelling an AI agent mathematically
  • Implementing an approximate agent function
  • Differentiating between types of AI agents
  • What different AI agents can accomplish in specific environments
  • The precise nature of an agent


Unit 4: Logic and language

  • Formal mathematical proofs versus typical arguments
  • Design of formal mathematical languages
  • Proving questions in mathematical languages
  • Undecidable proofs and undecidability in computer science
  • Incompleteness of mathematical language


Unit 5: Expert systems

  • Definition of expert systems
  • Architecture of an expert system
  • Knowledge bases and inference engines
  • The relevance of a user interface
  • The advantages and disadvantages of expert systems


Unit 6: Connectionist models

  • History and importance of artificial neural networks in machine learning
  • Components of a neural network: neurons, weights, layers, and activation functions
  • Calculating output values for neural networks by hand
  • How computers train neural networks
  • The advantages and limitations of neural networks


Unit 7: Artificial intelligence in the 21st century

  • The current trajectory of AI applications and possible future development
  • The challenges posed by the development of AI
  • The relationships of AI with sustainable development
  • Ethical implications of developing new intelligence.


Unit 8: Data science and artificial intelligence

  • A definition of data science
  • The relationship between data science and AI
  • Machine learning and Weak AI
  • Autoencoders
  • Tasks amenable to AI automation in data science


Unit 9: Machine learning and artificial intelligence

  • Defining machine learning
  • The different types of machine learning
  • Common applications of machine learning in real applications


Unit 10: Testing artificial intelligence systems

  • The importance of systems testing
  • The challenges of maintaining AI systems
  • Unintended outputs when redeploying AI systems
  • Ethical dilemmas
  • The problem of adversarial inputs
  • A guide to testing strategies


To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register when you enrol, you have up until the course start date to register and pay the £10 fee.

See more information on CATS point

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.

Assignments are not graded but are marked either pass or fail.

All students who successfully complete this course, whether registered for credit or not, are eligible for a Certificate of Completion. Completion consists of submitting the final course assignment. Certificates will be available, online, for those who qualify after the course finishes.


Description Costs
Course Fee £324.00
Take this course for CATS points £10.00


Dr Nicholas Day

Dr Nicholas Day teaches computer programming in C#, C++, Java and Python at both Buckinghamshire New University and Oxford University. Nicholas started his career as an Associate Lecturer at Buckinghamshire New University in late 2014, progressing to become a Graduate Teaching Associate in February 2020 and is now a Lecturer at the same institution, since August 2021. He completed the PGCert in Teaching and Learning in 2015 and also acquired fellowship of AdvanceHE (previously Higher Education Academy). Between 2016 and 2019, Nicholas assisted Dr Vasos Pavlika with the delivery of introductory programming courses in C++ and Java for the Department for Continuing Education at Oxford University. He was empanelled as a Department Tutor in 2019 and started delivered an Introduction to Object-Oriented Programming Using Java, later adapting the course for online delivery in 2022. He has also started researching and teaching Artificial Intelligence and Data Science material.

Nick’s scholarly interests are Computer Science Education (CSEd), Computing Education Research (CER), and online pedagogy. He completed his PhD in March 2020, which investigated the learning and teaching of computer science education, specialising in delivery of computer programming modules. Post-PhD completion, Nicholas is involved with Knowledge Transfer Partnership (KTP) applications and in discussion with data-driven companies regarding research projects and consultancy work. Nicholas also now supervises current PhD students in fields associated with Data Science and Virtual Reality, in addition to mentoring departmental colleagues who are undertaking PhD research. During the COVID-19 pandemic, Nicholas began teaching online and recording videos to increase access and engagement with educational material. He passionate about pedagogy and utilises his research findings to inform curriculum design.

Course aims

  • To introduce the concept of artificial intelligence and its different paradigms
  • To provide an understanding of the real-world potential and limitations of artificial intelligence
  • To describe the reach of artificial intelligence in society today.

Learning outcomes

By the end of this course students should:

  • Understand the concept of artificial intelligence versus human intelligence
  • Understand the foundational concepts in mathematics and logic underpinning AI
  • Be able to identify examples of real-world applications of AI
  • Be able to discuss the social, ethical and sustainability dilemmas posed by AI
  • Understand some of the challenges, limitations, and pitfalls of AI in real world applications.

Assessment methods

You will be set two pieces of work for the course. The first of 500 words is due halfway through your course. This does not count towards your final outcome but preparing for it, and the feedback you are given, will help you prepare for your assessed piece of work of 1,500 words due at the end of the course. The assessed work is marked pass or fail.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link:


Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an application form.

Level and demands

FHEQ level 4, 10 weeks, approx 10 hours per week, therefore a total of about 100 study hours.

IT requirements

This course is delivered online; to participate you must to be familiar with using a computer for purposes such as sending email and searching the Internet. You will also need regular access to the Internet and a computer meeting our recommended minimum computer specification.