THE ENGINEERING LIFECYCLE AND ARTIFICIAL INTELLIGENCE - 2027/8

Module Overview

This module serves as your foundational roadmap for the AI in Engineering programme. Before diving into specialised technical skills, we start by looking at the entire lifespan of an engineering project.

You will explore how a product moves from a simple idea through to its eventual retirement, including:

• Conceptualisation: Requirements, systems architecting, and design.
• Realisation: Manufacturing, supply chains, and rigorous testing.
• Evolution: Long-term operations, sustainability, and responsible recycling.

This module is designed to help you:

• Bridge Theory and Practice: Understand the "why" behind the engineering methods you will master in later modules.
• Prepare for Projects: Build the holistic mindset needed to lead your own project modules successfully.
• See AI in Action: Critically analyse how AI tools are currently transforming engineering without losing sight of the essential human elements¿like ethics, stakeholder needs, and environmental impact.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.

Module provider

School of Engineering

Module Leader

HAGEN-ZANKER Alex (Sch of Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

This module establishes a critical foundation by examining the professional engineering landscape through the lens of modern technology. It provides the essential framework for understanding how theoretical concepts and AI applications integrate into large-scale projects.

The Engineering Journey

The curriculum follows the full arc of a system¿s existence, ensuring the groundwork is laid for the practical project modules that follow:

• Conceptualization & Architecture: Analyzing early-stage requirements and the systems architecting needed to support them.
• Design & Validation: Exploring engineering analysis and the rigorous verification processes that ensure safety and performance.
• Production & Logistics: Investigating the complexities of manufacturing and the global supply chain.
• Operations & Sustainability: Managing the "in-use" phase of engineering, including maintenance, decommissioning, and the ethics of disposal or recycling.

The Wider Engineering Context

Beyond technical stages, the content focuses on the wider context that drives project expectations and success of engineering project. This addresses the vital intersection of decision processes, business goals, regulatory standards, stakeholder engagement, environmental sustainability, risk management and ethical implications.

Assessment pattern

Alternative Assessment

NONE

Assessment Strategy

The assessment strategy is designed to:

• Practice and demonstrate the versatile ability to integrate theoretical knowledge of both AI systems and engineering lifecycle processes.
• Demonstrate depth of understanding and ability to apply to real life challenges
• Develop a level of confidence and soundness of argument that can withstand robust interrogation and engage with different points of view.

• Coursework (Sector Analysis & Strategic Proposal): Students will critically evaluate a complex, real-world engineering sector. The assessment requires the synthesis of technical and non-technical data to identify systemic processes, as well as barriers and opportunities that constrain and justify the strategic integration of AI solutions. Students must produce a professional-standard roadmap for the development of a digital thread within that sector that balances technical innovation with business viability, regulatory compliance, and ethical risk management. (LO4, LO5)
• Written Examination: A formal examination designed to test the ability to critically appraise the engineering lifecycle and its governing frameworks. Students must demonstrate a deep integration of theoretical knowledge, evaluating how AI-driven optimization aligns with stakeholder requirements, sustainability mandates, and global engineering standards. (LO1, LO2, LO3)

• Case Study "Pitch" Feedback: Short, informal in-seminar presentations of early findings to peers and academic conveners followed by discussion and feedback.
• Case Study Peer-Review Workshops: Structured sessions where students critique draft proposals, focusing on the feasibility of AI integration and the robustness of the governance logic.
• Mock Exams: Practice with high-level, scenario-based questions that require evaluative essay responses rather than factual recall.

Module aims

• Equip students with knowledge of the engineering lifecycle and its wider context, including business strategies, stakeholder dynamics, and regulatory requirements.
• Develop a strong appreciation of the interaction between engineering skills and knowledge and AI capabilities.
• Foster a critical understanding of AI's role in engineering processes, including its limitations, ethical considerations, and societal impact.
• Enable students to evaluate the potential of AI-driven methodologies in real-world engineering challenges

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Building a theoretical framework that supports both future technical skill development and project-based work.
• Fostering an informed, critical and holistic perspective on the role of AI in engineering practice.

The learning and teaching methods include:

• Interactive Lectures: These sessions establish the theoretical groundwork for the engineering lifecycle. Content focuses on the intersection of AI applications with technical aspects of engineering as well as the wider engineering context.
• Collaborative Seminars: These forums facilitate deep-dive discussions into the complexities of the field. Emphasis is placed on evaluating stakeholder engagement strategies, ethical dilemmas in automation, and the current technical and practical limitations of AI systems.
• Guided Independent Study: This component encourages the transition from theory to application. Research is directed toward investigating AI¿s impact within specific engineering contexts.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENGM326

Other information

The School of Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Employability: This module bridges the gap between academic theory and industry requirements by embedding business acumen and regulatory awareness into the engineering lifecycle. By analyzing how AI-driven systems operate within commercial constraints, a professional foundation is built for roles involving system architecture, project management, and technology leadership.

Digital Capabilities: A central focus is placed on the strategic integration of AI. Rather than viewing technology in isolation, the module explores the digital maturity required to optimize engineering processes. This includes understanding the data structures, automation tools, and algorithmic frameworks that drive modern industrial innovation.

Global and Cultural Capabilities: Engineering challenges are addressed through a global lens, emphasizing the importance of stakeholder engagement across diverse geographical and cultural contexts. The curriculum examines how international standards and varying cultural expectations shape the development and deployment of AI systems.

Sustainability: The commitment to the environment is woven into the study of the entire lifecycle, from ethical sourcing and manufacturing to circular economy principles. The content promotes the design of resource-efficient solutions, focusing on how AI can minimize waste and manage the responsible decommissioning or recycling of complex systems.

Resourcefulness and Resilience: Through the study of governance and risk management, the module develops critical thinking and adaptability. By evaluating the limitations of AI and the unpredictability of engineering lifecycles, a mindset of resilience is fostered, preparing for the navigation of technical setbacks and shifting regulatory landscapes.

Programmes this module appears in