Artificial Intelligence Concepts: Practical Applications



Overview

artificial intelligence, n.

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

— Oxford English Dictionary

Artificial Intelligence (AI) is now deeply woven into our everyday lives, often in ways we barely notice. From disaster response and healthcare to sustainable development and policymaking, the ability of machines to learn, adapt and make decisions is reshaping how society functions. But these advances also raise urgent questions about ethics, fairness and the future of human decision-making. 

This course explores how AI is being applied to real-world challenges in the 21st century. Through a wide range of case studies, you’ll examine both the potential and the limitations of AI, gaining tools to think critically about its uses and implications. You’ll also explore strategies for keeping up with fast-moving developments, and learn how to apply core ideas to new contexts. 

Designed for a general audience, this course is ideal for professionals whose work intersects with AI, or anyone curious about how these technologies are changing the world. No coding or technical knowledge is required — the focus is on ideas, ethics, and real-world impact. 

This course is part of a broader series that helps you understand how AI works, how it’s already transforming society, and how to engage thoughtfully with the technologies shaping our future. 

This course does not involve any coding and instead focuses on concepts in Artificial Intelligence for a general audience.

This course has no live sessions. You will study structured materials at your own pace each week. Find out more about how our short online courses are taught.

Programme details

The course is broken down into 10 units over 10 weeks, each requiring approximately 10 hours of study time. The following topics are covered:

Unit 1: Introduction to AI concepts: practical applications

  • What is artificial intelligence?
  • Types of machine learning
  • The Business Process Model and Notation: modelling business processes

 

Unit 2: Ethical concerns raised by AI

  • The role of ethics in the development of AI and machine learning
  • Different ways of operationalising fairness in the context of AI
  • Ethical accountability for systems that learn and adapt
  • Transparency and AI systems

 

Unit 3: Replication, reproducibility and reuse in AI

  • Problems posed by replication, reproducibility and reusability of digital artefacts
  • The FAIR Guiding Principles: Findability, Accessibility, Interoperability, and Reusability
  • Applying FAIR to the reuse of digital artefacts relating to AI and ML

 

Unit 4: Staying abreast of AI developments

  • The importance of staying up to date with AI
  • Identifying key industry and research organisations and people
  • Key resources for keeping abreast of AI developments
  • Analysing popular articles and technical papers about AI

 

Unit 5: AI and the Sustainable Development Goals

  • The UN SDGs: Sustainable Development Goals
  • Applying AI to address the SDGs
  • The positive and negative impact of AI on the SDGs

 

Unit 6: Case study – Transfer learning for predicting poverty

  • Data as the new oil
  • Administrative data for public policy: identifying poverty lines and economic output
  • Exploiting multiple sources for prediction in complex environments
  • Harnessing Transfer Learning, Regression and Deep Learning

 

Unit 7: Case study – Social media for disaster management

  • The Sendai Framework for prioritising targets in disaster resilience
  • Monitoring disaster risk with GIS: Geographic Information Systems
  • The role of social networks, satellites and UAVs: unmanned aerial vehicles
  • Applications of Natural Language Processing and Latent Dirichlet Allocation

 

Unit 8: AI for fighting epidemics

  • Challenges for AI posed by epidemics and pandemics
  • Existing tools and frameworks used by organisations and nations
  • Applying AI to enhance existing frameworks for fighting epidemics

 

Unit 9: Case study – Contributions of AI towards developing vaccines

  • Proteins and vaccines: 3D molecular identification of vaccine targets
  • Cracking the problem of protein folding with deep learning
  • Enhanced prediction using Neural Networks and Gradient Descent

 

Unit 10: Case study – AI for predicting clinical deterioration

  • National Early Warning Scores: early detection in Intensive Care Units
  • Assimilating continuous and discrete vital signs for continuous monitoring
  • Retrospective analysis of risk factors from Electronic Health Records
  • Employing Gradient Boosting Models and Sequential Deep Neural Networks

Textbooks

There is no essential reading associated with this course.

Certification

Credit Application Transfer Scheme (CATS) points 

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework. All those enrolled on an online courses are registered for credit and will be awarded CATS points for completing work at the required standard.

See more information on CATS points

Digital credentials

All students who pass their final assignment will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that assignments are not graded but are marked either pass or fail. 

Fees

Description Costs
Course Fee £415.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutor

Ms Judith Harley

Judith Harley, MA, is a physics graduate and freelance computer consultant who advises on, and designs, commercial and private database, spreadsheet, and Visual Basic applications. She has taught computing courses at Oxford University for over 20 years.

Course aims

  • To introduce many and varied applications of artificial intelligence to society
  • To discuss the challenges and pitfalls faced by artificial intelligence applications
  • To evaluate the tangible impact of artificial intelligence on humanity, now and in the future

Learning outcomes

By the end of this course, students should:

  • Understand the scope and reach of artificial intelligence applications
  • Understand the conceptual, practical and ethical challenges facing AI applications
  • Have detailed knowledge of lessons learned from specific AI applications
  • Be able to generalise examples of real-world AI applications to new domains
  • Be able to assess the potential impact of AI on significant problems critically

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.

 

Application

Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an Enrolment form for short courses.

Level and demands

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

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 visit our English language requirements webpage.  

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.

Terms & conditions for applicants and students

Information on financial support