IS30450 Artificial Intelligence

Academic Year 2023/2024

Artificial Intelligence systems are becoming increasingly influential in society. This module presents an overview of different approaches to AI and critically examines the assumptions behind each. The purpose of this course is to equip students with an understanding of AI without the requirement of a math or technology background. The course describes the history of AI and the contemporary landscape of AI research and practice. Differences between statistical and symbolic approaches are examined. Philosophical foundations of AI are discussed along with emerging ethical issues.

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Curricular information is subject to change

Learning Outcomes:

On successful completion of this module students should be able to:

1. Demonstrate an overall understanding of AI and its approaches
2. Critically examine different approaches to the development of AI from a philosophical and societal perspective.
3. Use an AI technology to explore an issue or address a research question
4. Understand contemporary debates in the field of AI

Student Effort Hours: 
Student Effort Type Hours


Autonomous Student Learning




Approaches to Teaching and Learning:
This module includes lectures, reflective learning, critical reading, class activities, debates and project work.
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: Project that uses an AI technique to address an issue - or a case study of the societal/ethical implications of an AI system. Unspecified n/a Graded No


Assignment: Critical evaluation of contemporary AI journal article Unspecified n/a Graded No


Assignment: Essay or report paper critically assessing a topic within the foundations of AI Unspecified n/a Graded No


Carry forward of passed components
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided to students on Brightspace.

Name Role
Megan Nyhan Lecturer / Co-Lecturer
Dr Arjumand Younus Lecturer / Co-Lecturer
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 13:00 - 14:50