DISSERTATION (AI IN ENGINEERING) - 2027/8

Module Overview

Module Overview:

The dissertation is the master-level capstone of the programme, requiring the execution of an original, independent research project at the intersection of AI and the engineering lifecycle. This module challenges you to move beyond implementation and toward critical advocacy, requiring the articulation of a robust personal stance on the role of AI in engineering, backed by empirical evidence and rigorous analysis.

You will navigate the full research cycle¿from problem scoping and data acquisition to model validation and ethical critique. This is a platform to apply the diverse toolsets acquired throughout the programme to a specific, high-impact engineering challenge, culminating in a professional-standard report and a rigorous technical defence.

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

SOL - School of Engineering

Module Leader

HAGEN-ZANKER Alex (Sch of Eng)

Number of Credits: 60

ECTS Credits: 30

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

• Research Methodology: Guidance on conducting independent research, literature review, and critical analysis.
• AI Applications in Engineering: Exploration of AI tools for ideation, optimization, simulation, and operational management.
• Critical Reflection: Structured reflection on project-based learning and its implications for engineering practice.
• Ethical and Societal Implications: Examination of intellectual property, accountability, bias, and societal impact of AI.
• Dissertation Writing and Presentation: Development of a structured dissertation report and defence presentation.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to:

• Require the execution of an original, independent research project at the intersection of AI and the engineering lifecycle.
• Develop the articulation of a robust personal stance on the role of AI in engineering, backed by empirical evidence and rigorous analysis.

• Dissertation Report: A comprehensive individual research document articulating project findings, critical analysis, and a reasoned stance on AI¿s impact on the engineering lifecycle. (LO1, LO2, LO3)
• ** Viva:** A formal oral defense of the research findings to a panel of academic staff and, where applicable, industry partners. Students must justify their technical choices and respond to expert critique. (LO4, LO5)
• Position Statement and Personal Reflection: A brief report that reflects on the learning experience within this module and with Academics in AI more widely. The culmination is a well-reasoned personal position statement on the role of AI in engineering (LO5)

• Research Proposal Review: Initial feedback on project scoping and methodology to ensure feasibility and rigor.
• Supervisory Meetings: regular feedback in student-led meetings with academic supervisor
• Draft Feedback Cycles: Iterative review of report sections during supervisory meetings to refine arguments and technical clarity.
• Mock Viva: Formative presentation practice in workshops to prepare for the final technical defence.

Module aims

• Enable the conduct of independent, in-depth research that pushes the boundaries of AI applications in engineering.
• Foster the ability to synthesize project data and research to support a well-reasoned personal and professional position on AI integration.
• Develop advanced skills in research design, data integrity, and the critical evaluation of AI model limitations.
• Promote a deep awareness of the ethical, societal, and legal implications (IP, bias, safety) of AI deployment in engineering.
• Refine the ability to communicate complex, technical arguments to both academic and industry stakeholders.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Support the students to develop into an authoritative expert in the area of AI in Engineering.

Promote an approach to lifelong learning

The learning and teaching methods include:

• Independent Research: Self-directed investigation under the guidance of academic supervisors, focused on the execution of the research roadmap.
• Supervisory Consultations: Regular meetings to refine research questions, troubleshoot technical hurdles, and ensure methodological rigor.
• Introductory Lectures: Highly focused sessions on advanced research methods, dissertation architecture, and the ethics of engineering research.
• Peer Defense Workshops: Collaborative sessions where students present work-in-progress, receiving and providing constructive critique to simulate a professional peer-review environment.

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: ENGM330

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: Graduates demonstrate the ability to manage complex, long-form technical projects. The viva voce specifically prepares students for high-level technical consultancy and leadership roles where justifying and defending AI strategies is a core requirement.

Digital Capabilities: Mastery of independent research within the digital ecosystem, including advanced data handling, AI model development, and the use of digital tools for academic synthesis and presentation.

Global and Cultural Capabilities: Requires an examination of the global implications of the research topic, addressing international standards, cross-border data ethics, and the cultural impact of automated engineering practices.

Sustainability: Students are encouraged to investigate AI¿s role in optimizing resources, lifecycle management, and the development of sustainable engineering solutions, reflecting on the environmental footprint of AI itself.

Resourcefulness and Resilience: The year-long project structure develops high-level technical resilience and the ability to solve unforeseen research obstacles independently.