Establish a credible foundation in AI by understanding how it works, where it creates value, and how it should be applied responsibly.
The EXIN BCS Artificial Intelligence Foundation course introduces the essential concepts and principles behind AI and machine learning while connecting them to real organizational opportunities, challenges, and risks. Explore intelligent agents, robotics, ethical and sustainable AI, machine learning fundamentals, and the evolving relationship between humans and intelligent systems.
- Why get trained: Understand AI principles, machine learning fundamentals, intelligent agents, ethics, opportunities, and risks
- Why it matters: Foundational AI knowledge helps professionals evaluate AI applications with greater awareness of their benefits, limitations, and responsibilities
- Who should attend: Professionals who need to understand, evaluate, or support AI adoption within their organization
Turn foundational AI knowledge into informed decisions about where AI fits, what it requires, and how people and intelligent systems can work together effectively. HRD Corp Claimable.
Overview
Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior.
The EXIN BCS Artificial Intelligence Foundation certification tests a candidate’s knowledge and understanding of the terminology and general principles of AI. This preparation guide covers:
- the potential benefits and challenges of ethical and sustainable robust Artificial Intelligence (AI)
- the basic process of Machine Learning (ML)
- Building a Machine Learning (ML) Toolkit
- the challenges and risks associated with an AI project
- the future of AI and Humans in work.
This Foundation certificate includes and expands on the knowledge taught in the EXIN BCS Essentials Certificate in Artificial Intelligence.
Skills Covered
- Describe how Artificial Intelligence (AI) is Part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’
- Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description
- Explain the Benefits of Artificial Intelligence (AI)
- Describe how we Learn from Data – Functionality, Software and Hardware
- Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together
- Describe a ‘Learning from Experience’ Agile Approach to Projects
Prerequisites
- There are no prerequisites required to attend this course.
Target Audience
- The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.
Module 1: Ethical and Sustainable Human and Artificial Intelligence (AI)
1.1 Recall the General Definition of Human and Artificial Intelligence (AI)
- The concept of intelligent agents
- Modern approach to Human logical levels of thinking using Robert Dilt’s Model
1.2 Ethics and Trustworthy Artificial Intelligence (AI), in Particular:
- The general definition of Ethics
- Human Centric Ethical Purpose respects fundamental rights, principles and values
- Ethical Purpose AI is delivered using Trustworthy Artificial Intelligence (AI) that is technically robust
- Human Centric Ethical Purpose Trustworthy Artificial Intelligence (AI) is continually assessed and monitored
1.3 Three Fundamental Areas of Sustainability and the United Nation’s Seventeen Sustainability Goals
1.4 Artificial Intelligence (AI) is Part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’
1.5 Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI)
- ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition)
Module 2: Artificial Intelligence (AI) and Robotics
2.1 Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description, and:
- Four rational agent dependencies
- Describe agents in terms of performance measure, environment, actuators and sensors
- Four types of agent: reflex, model-based reflex, goal-based and utilitybased
- Identify the relationship of Artificial Intelligence (AI) agents with Machine Learning (ML)
2.2 What a Robot is and:
- Robotic paradigms
2.3 What an Intelligent Robot is and:
- Relate intelligent robotics to intelligent agents
Module 3: Applying the Benefits of Artificial Intelligence (AI) – Challenges and Risks
3.1 Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations
3.2 Benefits of Artificial Intelligence (AI) by:
- Advantages of machine and human and machine systems
3.3 Challenges of Artificial Intelligence (AI), and:
- General ethical challenges Artificial Intelligence (AI) raises
- General examples of the limitations of Artificial Intelligence (AI) systems compared to human systems
3.4 Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects, and:
- General example of the risks of Artificial Intelligence (AI)
- Artificial Intelligence (AI) project team in particular
- A domain expert
- What is ‘fit-of-purpose’
- The difference between waterfall and agile projects
3.5 List Opportunities for Artificial Intelligence (AI)
3.6 Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs)
Module 4: Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox – Theory and Practice
4.1 Describe how we Learn from Data – Functionality, Software and Hardware
- List common open source machine learning functionality, software and hardware
- Introductory theory of Machine Learning (ML)
- Typical tasks in the preparation of data
- Typical types of Machine Learning (ML) Algorithms
- Typical methods of visualizing data
- Typical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine
4.2 Typical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML) and Artificial Intelligence (AI) Agents’ Functionality
Module 5: The Management, Roles and Responsibilities of Humans and Machines
5.1 Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together
5.2 List Future Directions of Humans and Machines Working Together
5.3 Describe a ‘Learning from Experience’ Agile Approach to Projects
- Type of team members needed for an Agile project
Dates & Locations
September 21, 2026 - September 22, 2026
September 21, 2026 - September 22, 2026
December 14, 2026 - December 15, 2026
December 14, 2026 - December 15, 2026
February 22, 2027 - February 23, 2027
February 22, 2027 - February 23, 2027
April 12, 2027 - April 13, 2027
April 12, 2027 - April 13, 2027
June 21, 2027 - June 22, 2027
June 21, 2027 - June 22, 2027
August 12, 2027 - August 13, 2027
August 12, 2027 - August 13, 2027
October 18, 2027 - October 19, 2027
October 18, 2027 - October 19, 2027
December 23, 2027 - December 24, 2027
December 23, 2027 - December 24, 2027
Exam & Certification
EXIN BCS Artificial Intelligence Foundation exam.
Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):
- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI);
- Artificial Intelligence (AI) and Robotics;
- applying the benefits of AI projects – challenges and risks;
- Machine Learning (ML) Theory and Practice – Building a Machine Learning (ML) Toolbox;
- the Management, Roles and Responsibilities of Humans and Machines – The Future of AI.
Training & Certification Guide
Frequently Asked Questions
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