CONCEPTUAL DESIGN AND AI DRIVEN IDEATION - 2027/8

Module Overview

This module introduces you to the foundational stages of conceptual design, emphasizing the integration of Artificial Intelligence (AI) to ideate, evaluate, and refine engineering solutions.

Through a collaborative team project, you will translate complex engineering challenges into machine-readable formats, exploring latent spaces to uncover diverse solution sets. The module focuses on the synergy between human intuition and machine efficiency, covering automated screening, surrogate modeling, and human-in-the-loop refinement. By the end, you will not only generate innovative designs but also validate them through high-fidelity simulations, ensuring they meet the stringent demands of modern manufacturability and sustainability.

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

The curriculum centers on the "Design-Evaluate-Refine" loop, enhanced by AI capabilities:

• Problem Formulation & Constraint Encoding: Moving from vague requirements to quantified objectives and engineered technical prompts.
• Generative Ideation & Latent Space Exploration: Utilizing VAEs, GANs, and topology optimization to navigate multi-modal design inputs.
• Automated Screening & Surrogate Modeling: Implementing clustering and AI-driven filters to efficiently navigate and evaluate thousands of design permutations.
• Human-in-the-Loop Refinement: The engineer¿s role in iterative prompting, trade-off analysis, and optimizing for construction and manufacturing constraints.
• Validation & Synthesis: High-fidelity simulation and domain-specific assessment (e.g., FEA, CFD) to ensure theoretical concepts are physically viable.

Assessment pattern

Alternative Assessment

It is not possible to complete the module without substantial participation in the group work. Without substantial participation in group work the next opportunity to take part in the assessment is when the module is offered to the next cohort of students. For students who have substantially participated in the group work there is the following alternative assessment: ¿ Seminar Contributions: Alternatively assessed by reflective essay following personalised brief. ¿ Progress Presentation: Alternatively assessed by video recording of individual presentation following personalised brief. ¿ Group Project: Alternatively assessed by individual report following personalised brief.

Assessment Strategy

The assessment strategy is designed to:

• Provide an stimulating and challenging task that will require individual resilience, creativity, research and problem-solving.
• Nurture and test the ability to work collaboratively on an engineering challenge.

• Progress Presentation: A group-led oral assessment focusing on problem analysis, constraint encoding, and the initial generative strategy. (LO1, LO2, LO7)
• Technical Group Project Report: A professional-standard report detailing the end-to-end design process, from latent space exploration to final simulation-based validation. (LO3, LO4, LO5, LO6)
• Seminar Contributions: Evaluates the ability to engage in critical technical discourse and peer-review of design strategies (LO6, LO7)

• Peer-reviewed progress presentations and discussions during group sessions.
• Supervisor feedback on project progress.

Module aims

• Equip students with the ability to translate engineering problems into machine-readable formats through problem formulation and constraint encoding.
• Develop skills in generative ideation by leveraging AI to propose diverse solutions tailored to specific engineering challenges.
• Teach methods for automated screening and surrogate modelling to efficiently filter AI-generated designs.
• Foster human-in-the-loop refinement processes for trade-off analysis and iterative improvement.
• Ensure students can validate AI outputs through high-fidelity simulations and assessments for construction or manufacturability.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

The module follows a Problem-Based Learning (PBL) approach centered on a group project.

The learning and teaching methods include:

• Strategic Lectures: Introducing the workflows of AI-driven ideation and the mathematical underpinnings of generative models.
• Moderated Seminars: Student-led forums for presenting project progress, discussing ethical and technical trade-offs, and critiquing AI-generated solutions.
• Project Workshops: Supervised group sessions where teams apply technical skills to their specific engineering domain, receiving direct feedback on their design evolution.

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: ENGM331

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: Focuses on collaborative leadership and technical communication within a team¿essential for leading digital design departments.

Digital Capabilities: Mastery of cutting-edge generative platforms and the ability to integrate simulation data with AI models.

Sustainability: A core focus on resource-efficient design. Students use AI to minimize material waste and optimize for circular economy principles in manufacturing.

Resourcefulness and Resilience: Develops the "Critical Filter"¿the ability to acknowledge the limitations of AI-generated concepts and adapt design processes when automated outputs fail validation.