Module: Introduction to AI

Module Specifications

Academic Year 2026 - 2027

Module Title	Introduction to AI
Module Code	CSC1196
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

This module introduces the foundations of Artificial Intelligence (AI) in a way that is accessible to non‑specialists. Learners explore how AI systems are built, what kinds of tasks they can and cannot do, the ethical issues associated with AI, critical thinking in the context of AI, prompt engineering and future possibilities relating to AI. The emphasis is on developing a basic understanding of AI and how to use it in a human-centred manner.

Learning Outcomes

1. Describe the fundamental concepts of Artificial Intelligence including machine learning, data, training, and model limitations.
2. Demonstrate the ability to use AI tools effectively and appropriately for research, writing, problem=solving, and creativity
3. Critically evaluate the reliability, relevance, and accuracy of AI outputs, mindful of ethical issues.
4. Employ, test, and refine prompts for effective AI tool interaction
5. Act responsibly when using AI technologies, with due regard for oneself and others.
6. Identify and explain trends in AI and consider how they may shape future developments.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Laboratory	25	This module will mainly be lab-based and practical.
Group work	20	The students will work on a group project in relation to AI.
Assignment Completion	20	Students will have an individual assignment to complete.
Quizzes	12	Students will have access to self-assessment quizzes throughout the module.
Independent learning	12	Students are expected to experiment ethically with AI tools during the module.
Independent Study	36	Students are expected to study the module and other resources in their own time.
Total Workload: 125

Section Breakdown
CRN	21438	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Monica Ward	Module Teacher

Section Breakdown
CRN	21438	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Monica Ward	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Loop Quiz	In in-lab quiz on the fundamental concepts of Artificial Intelligence.	10%	Once per semester
Reflective journal	Students produce three reflective journals (worth 5% each) on their initial, emerging and end-of-module thoughts on Artificial Intelligence (Week 1, Week 6, End of Semester).	15%	As required
Assignment	Assignment on the ethical issues relating to Artificial Intelligence.	15%	Once per semester
Practical/skills evaluation	Prompt engineeering task. This will take place in a lab.	10%	As required
Group project	Mini Challenge-Based Learning group project on Artificial Intelligence	30%	Once per semester
Oral Examination	This is an Interactive Oral assessment based on the Challenge-Based Learning group project on Artificial Intelligence.	20%	Once per semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Basics of AI
Brief history, how it works and a conceptual understanding of how generative AI processes information and produces outputs, covering, for example, statistical prediction, training data, and model architectures

Basic Functionality
How to use DCU-approved tools, citation, verification, possibilities and limitations

Ethical Considerations
Review of ethical issues associated with AI including, but not limited to, hallucinations (errors), bias, IP issues, sustainability issues, access, labour issues and regulation.

Critical Thinking
Focus on human skills with AI, evaluate AI-generated content, and determine if/when and how to use AI, taking into account societal impact.

Basic prompt engineering
How to design clear, intentional inputs/prompts that could be narrowed and bounded sufficiently to minimise the degree of inaccuracy and that guide an AI system to produce better-defined outputs that could be more useful, and context‑appropriate. appropriate,

AI Futures
Overview of AI futures from different perspectives - technical, societal, regulation and at a discipline level.

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None

Coursework information updated.