Introduction to Artificial Intelligence (AI)

Module 1. What is Artificial Intelligence? – Foundations & History

Covers the meaning and evolution of AI, including its origins, major milestones, and how it has developed into the technologies transforming society today. Learners will also understand key definitions such as AI, machine learning, deep learning, and narrow vs. general AI.Topics include: history of AI, AI terminology, AI vs. traditional computing.

Module 2. AI Concepts & Core Techniques

Introduces the fundamental techniques that underpin modern artificial intelligence systems. This includes an overview of machine learning principles and basic data‑driven approaches used to teach computers to recognise patterns and make predictions.Topics include: supervised vs. unsupervised learning, algorithms overview, models and training basics.

Module 3. Practical AI Tools & Applications

Explores real‑world uses of AI across different domains. Learners will see how AI‑powered tools (such as AI chat interfaces and productivity assistants) work and how they can be applied in everyday tasks and business processes.Topics include: AI in healthcare, finance, automation, and creative workflows.

Module 4. AI in Action — Hands‑On Experience

Provides guided exercises and demonstrations using popular AI platforms and tools (e.g., generative AI and chatbots). Students gain practical familiarity with interacting with AI systems and interpreting their outputs.Topics include: using AI models, prompt crafting basics, evaluating model responses.

Module 5. Data Basics for AI

Introduces learners to the critical role of data in artificial intelligence. Concepts covered include data types, simple preprocessing ideas, and how quality data supports learning and reliable model performance.Topics include: structured vs. unstructured data, features vs. labels, data preparation concepts.

Module 6. Ethical Considerations & Responsible AI

Focuses on the societal and ethical implications of AI technologies. This module discusses topics such as fairness, bias, privacy, and the responsibility of developers and professionals when creating and deploying AI systems.Topics include: bias and fairness, data privacy, ethical guidelines, societal impacts.

Module 7. Future of AI & Career Pathways

Looks ahead to emerging trends and opportunities in AI including where the field is headed and how learners can build careers leveraging AI skills. This module is aimed at helping students make strategic decisions about next steps in their education or professional journeys.

Module 8. Capstone Exercise or Reflection Project

Students apply their learning to a practical task, such as designing a simple AI‑enabled solution, analysing a case study, or reflecting on ethical considerations in AI deployment. This module helps solidify understanding and demonstrate readiness for further AI study.