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EEEN20140

Academic Year 2026/2027

Engineering Mastery with Artificial Intelligence (EEEN20140)

Subject:
Electronic & Electrical Eng
College:
Engineering & Architecture
School:
Electrical & Electronic Eng
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Assoc Professor Paul Cuffe
Trimester:
Autumn and Spring (separate)
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

In this module you will spend three hours each week in face-to-face workshop sessions, conducting sustained one-to-one tutorial dialogues with artificial intelligence chatbots, about the technical engineering material you have studied during your degree so far.

The purpose of this module is to foster deep technical mastery. A modular engineering degree teaches subjects in separate boxes, and it is possible to pass each module while never quite joining them up. This module exists to close that gap: to revisit technical material you have already covered, to probe how well you actually understand it, and to build the connective tissue between subjects to make them hang together as a unified whole.

This type of learning is inherently personalised; each student has different skills gaps and different tutoring needs. Fortunately, it now seems that Large Language Models are capable of acting in the role of an erudite, patient, personalised tutor with inexhaustible disciplinary knowledge. The goal of this experimental elective module is to harness that capability: to create a structure and environment that motivates and rewards thoughtful engagement with these learning resources.

A subsidiary purpose of this module is to teach the skills that allow you to solicit useful information from a Large Language Model. These skills include: the self-awareness to take honest stock of what you know and pursue the gaps; the dialogue skills to ask precise questions and steer a conversation toward genuine understanding; and the scholarly skepticism to verify confident-sounding outputs against textbooks, papers, and your own lecture notes.

AI tools are controversial in education, and understandably so; used lazily, they produce the illusion of understanding while actual deep thinking skills atrophy. Used with care and discipline, they can offer something universities have never been able to provide every student: an individual tutor with endless patience. This module is designed to teach the skills that unlock that better outcome.

About this Module

Learning Outcomes:

On successful completion of this module, students will be equipped to:

LO1. Audit their own understanding of technical material, identify specific gaps and misconceptions, and formulate a concrete plan to address them. This self-assessment habit underpins all professional development and lifelong learning.

LO2. Conduct a sustained technical dialogue to build understanding: posing precise questions, following up on incomplete answers, and probing apparent inconsistencies. A professional engineer must use these communication skills when consulting a colleague, an outside expert, or an AI chatbot.

LO3. Verify technical claims against authoritative sources (textbooks, papers, lecture notes, datasheets), and document instances where confident-sounding material proved wrong, incomplete, or unverifiable.

LO4. Map connections between concepts encountered in separate contexts, and explain how ideas from one engineering subject constrain, enable, or reappear in another.

LO5. Translate explanations and study guidance into active learning actions (working through exercises, writing and running code, consulting library and primary sources) and using the results to test and consolidate their own understanding.

LO6. Independently learn an unfamiliar technical topic, blending AI tutoring and traditional sources; self-directed learning is an essential professional skill to stay current across a long engineering career.

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

Collaboration

 The ability to learn from others; to understand and respect the needs, perspectives and actions of others (empathy); to understand, relate to and be sensitive to others (empathic leadership); to deal with conflicts in a group; and to facilitate collaborative and participatory problem solving. Competent

Critical Thinking

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

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Novice

Strategic

 The ability to collectively develop and implement innovative actions that further sustainability at the local level and further afield. Not addressed

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. Not addressed

Self-awareness

 The ability to reflect on one’s own role in the local community and (global) society; to continually evaluate and further motivate one’s actions; and to deal with one’s feelings and desires. Competent

Normative

 The ability to understand and reflect on the norms and values that underlie one’s actions; and to negotiate values, principles, goals, and targets, in a context of conflicts of interest and trade-offs, uncertain knowledge and contradictions. Novice

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. Not addressed

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Proficient

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

Wellbeing

 Wellbeing is having the resources and skills to meet life's challenges, including attributes such as personal development skills, resilience, stress management, strengths, lifestyle skills, nutrition, physical activity, sleep, alcohol/substance use, academic skills, time management, goal setting, interpersonal skills, group work, communication. Not addressed

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

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 5

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all 3
 

Student Effort Hours:
Student Effort Type Hours
Computer Aided Lab

36
Autonomous Student Learning

90
Total

126

Approaches to Teaching and Learning:
This module is delivered in a studio style; we come together as a collective, three hours a week, to motivate and energise each other in our self-directed learning. Attendance will be taken.

Students work individually on their laptops with staff circulating to guide and challenge.

Reflective learning is the core approach: each student takes stock of what they actually know, finds the gaps, and works on them through one-to-one tutorial dialogue with AI systems, using material from their prior modules. Short framing sessions teach the techniques (intentional questioning, verification, study planning) and the practice continues outside class.

Learning is documented in a portfolio: dialogue transcripts, knowledge audits, study plans, errors caught, exercises worked.

On AI use: it is the module's central activity. But AI is the instrument of learning, never the substitute for attaining it.

Requirements, Exclusions and Recommendations

Not applicable to this module.


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
Portfolio: A growing body of documentation showing how the student engaged with AI tools and what they learned by so doing Week 3, Week 5, Week 9 Graded No
35
 No
Individual Project: Substantial posters summarising both what technical material was learned, and reflecting on the role AI played in that learning. Posters to be defended orally. Week 11 Graded No
50
 No
Viva Voce: A structured interview/presentation to defend the work documented within the portfolio up to week 7. Week 7 Graded No
15
 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
 

Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr John Healy Lecturer / Co-Lecturer
Dr Eoghan O'Riordan Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 11:00 - 12:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 17:00 - 17:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 13:00 - 13:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 12:00 - 13:50
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