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MEEN41490

Academic Year 2026/2027

AI in Engineering (MEEN41490)

Subject:
Mechanical Engineering
College:
Engineering & Architecture
School:
Mechanical & Materials Eng
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr David Garzón Ramos
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This project- and practice-based module is designed to provide students with the toolkit to specify, commission and evaluate AI systems in engineering and enterprise applications. Throughout the trimester, students will learn where AI is worth applying, what it takes to make it work, and what it can realistically deliver. Working in teams, students will gain hands-on experience developing a proposal for an AI-based solution to a societal problem, weighing sustainability and feasibility.

About this Module

Learning Outcomes:

1. Critically evaluate whether artificial intelligence is a viable solution to an engineering problem and identify evidence that would support or rule out a given AI approach.
2. Identify types of data used in engineering practice and the characteristics and limitations of models that are built from them.
3. Build research- and evidence-based arguments to discuss technical and commercial claims about an AI system.
4. Assess the ethical, legal, and professional obligations of applying AI in an enterprise and industrial context.
5. Devise economically and environmentally sustainable AI-based solutions that can help address societal challenges aligned with the UN Sustainable Development Goals, and communicate them to audiences with diverse expertise.

Indicative Module Content:

Some topics covered by this module include approaches to AI (e.g., symbolic, discriminative, generative, physical), tools for developing AI solutions (e.g., preparing data, running pre-trained models). The module will also discuss developing AI solutions with commercialisation potential, AI regulation, data protection, and the assessment of technical feasibility.

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Competent

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Proficient

Anticipatory

 The ability to understand and evaluate multiple scenarios for the future – possible, probable and desirable; to create one’s own visions for the future; to apply the precautionary principle; to assess the consequences of actions; and to deal with risks and changes. Competent

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Proficient

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

Make cities and human settlements inclusive, safe, resilient and sustainable 1

Conserve and sustainably use the oceans, seas and marine resources for sustainable development 1
 

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

40
Autonomous Student Learning

50
Lectures

36
Total

126

Approaches to Teaching and Learning:
Lectures, practical sessions, individual and group work, presentation, critical writing.

AI in this module
Students are actively encouraged to engage critically with AI tools as part of their learning in this module. Appropriate use may include brainstorming, summarising material, testing ideas. Any use of AI must be cited appropriately.

Requirements, Exclusions and Recommendations
Learning Recommendations:

Basic programming and statistics is recommended.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Exam (In-person): In-class test on the module content. Week 10 Alternative linear conversion grade scale 40% No
25
 No
Quizzes/Short Exercises: Individual exercises on topics raised in guest specialist sessions. Format varies by session. Week 3, Week 7, Week 12 Graded No
15
 No
Group Work Assignment: Three-minute pitch, with slides, of a proposed AI-based solution addressing a sustainability challenge. Week 4 Graded No
5
 No
Group Work Assignment: Data collection plan for a proposed AI-based solution addressing a sustainability challenge, with a sample of the data. Week 6 Graded No
5
 No
Group Work Assignment: Poster on a proposed AI-based solution addressing a sustainability challenge, presented and defended in class. Week 11 Graded No
20
 No
Group Work Assignment: Team proposal for an AI-based solution addressing a sustainability challenge. Final report on the evidence behind it and how far the team took it. Week 15 Graded No
30
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - Autumn: All Weeks Fri 09:00 - 10:50
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 09:00 - 09:50
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