AI-Assisted Design Development of Stone Memory 2.0

Introduction: Role of AI in the Design Process

Artificial Intelligence was introduced as a design assistant to analyse the existing Stone Memory proposal, identify potential improvements, and explore alternative architectural possibilities. AI was not used to replace the design process, but to expand exploration and support critical decision-making.

AI Design Analysis

AI Prompt 01: Design Evaluation

Tools Used: ChatGPT / Gemini

Gemini Promtpt:

Gemini Outcome

ChatGPT Promtpt:

ChatGPT Outcome

Gemini Findings

ChatGPT Findings

AI Concept Exploration 

Exploration 01: Massing Development 

Orginal massing study

AI Generated massing study

AI was used to explore different interpretations of the original stone massing. The generated options maintained the idea of permanence while introducing carved voids, courtyards, and more porous spatial relationships.

Exploration 02: Material & Atmosphere

The AI exploration expanded the understanding of stone beyond a visual material. It suggested ways stone could influence atmosphere through texture, shadow, temperature control, and interaction with natural light.

Original Design

AFTER AI IMAGES

Original Design

AFTER AI IMAGES

Exploration 03: Spatial Experience

AI helped visualise alternative visitor experiences by exploring transitions between enclosed exhibition areas and open reflective spaces.

AI Prompt Development

Prompt 01: Concept Refinement 

AI Purpose:

This prompt was used to evaluate whether the original stone concept could be developed beyond a visual material into a stronger narrative element.

Prompt 02: Spatial Experience 

AI Purpose:

This prompt explored how visitors could experience the building through movement, sequence, and emotional connection.

Prompt 03: Environmental Response 

AI Purpose:

This prompt helped identify ways to transform stone from a symbolic material into a functional environmental element.

Critical Selection Process 

Although AI generated multiple design possibilities, not all outputs were directly applied. Each suggestion was evaluated based on site context, architectural feasibility, environmental performance, and alignment with the original design intention. 

AI Workflow Reflection

Through the AI-assisted workflow, the design process became more exploratory and iterative. AI provided alternative perspectives that helped identify overlooked opportunities within the original proposal. However, final decisions remained guided by architectural judgement, ensuring that the revised design maintained its connection to memory, place, and user experience.