Course Description

The Foundation in Artificial Intelligence course provides a comprehensive introduction to the principles, techniques, and applications of AI, equipping participants with the knowledge needed to understand and implement AI solutions responsibly and effectively. The course covers key topics including the benefits, challenges and risks of applying AI and the roles and responsibilities of humans and machines.

The course sessions are interactive with group discussions and sample exam questions to reinforce learning and prepare candidates for the BCS examination.

Exam Certification

Yes. During this course you’ll receive all the training you need to prepare for the BCS Foundation Certificate in Artificial Intelligence multiple choice examination.

The exam will be taken remotely using an online proctoring service. This 1 hour 'closed book' exam consists of 40 multiple-choice questions with a pass mark of 26/40.

Course Materials

Participants receive a detailed course manual covering all syllabus topics, supported by a glossary of essential AI terminology and concepts.

For virtual courses a printed copy of the latest edition of the comprehensive course manual will be sent to your home address in good time for the start of your course. Our delegates tell us that having access to a physical document is beneficial as both a reference document and for taking notes during the course. In addition, a link will be emailed to you to enable you to access an electronic copy of the same comprehensive manual for convenient future reference.

Target Audience

Anyone with an interest in understanding artificial intelligence, especially those seeking to explore AI’s potential or implement AI-driven solutions in their work environments.

It is anticipated that this certification will appeal to individuals undertaking a range of job roles including, but not limited to: engineers, scientists, web developers, service designers, project and programme managers, chief technology officers, business strategists, consultants, and organisational change practitioners.

Course Objectives

Upon completion of this course participants shall be able to demonstrate knowledge and understanding of:

• Fundamental concepts, terminology, and historical development of artificial intelligence (AI), including its links to human intelligence and robotics.
• Ethical, legal, and sustainable considerations in the development, deployment and responsible use of AI, including the responsibilities of humans and machines.
• Foundational technologies that enable AI, such as machine learning, robotics and intelligent systems that leverage large-scale data analysis, with practical insights into building a machine learning toolbox.
• The importance of data quality, visualisation, and big data in powering AI applications, and the functionality and practical uses of generative AI and large language models.
• How to identify and evaluate AI opportunities in organisations, including building a business case and involving key people.
• Approaches to assessing and managing the risks, benefits, and governance strategies associated with AI projects.
• The societal and economic impact of AI, including emerging career opportunities and the ethical implications of artificial consciousness.

Course Outline

Introduction to AI

• Core artificial intelligence concepts and terminology
• Key milestones in the development of AI
• Types of AI
• The impact of AI on society
• Sustainability measures to help reduce the environmental impact of AI

Ethical and legal considerations

• Ethical concerns with AI, including bias and privacy
• Guiding principles in ethical AI development
• Strategies for addressing ethical challenges in AI projects
• The role of regulation in AI
• Risk management in AI

Enablers of AI

• Common examples of AI
• The role of robotics in AI including robotic process automation (RPA)
• Machine learning (ML)
• Neural networks
• Deep learning including supervised and unsupervised learning
• Large language models (LLMs)
• The purpose and use of generative AI including LLMs

Finding and using data in AI

• The importance of data quality in AI
• The risks associated with handling data in AI and how to minimise them
• The purpose and use of big data including structured, semi-structured and unstructured data
• Data visualisation techniques and tools
• How data is used to train AI in the machine learning process

Using AI in organisations

• Identifying opportunities for AI in an organisation
• The contents and structure of a business case
• Identifying and categorising stakeholders relevant to an AI project
• Identifying the risks, costs and benefits associated with a proposed solution
• Governance activities required when implementing AI

Future planning and impact

• Roles and career opportunities presented by AI
• AI application in the real world
• Benefits and challenges of AI in future: societal impact, environmental impact, economic impact
• Potential future advancements and direction of AI
• Consciousness and its impact on ethical AI