Techformance – Technology, Platform, Performance

Generative design algorithms, AI-driven sustainability models, robotic fabrication techniques… the rise of artificial intelligence and advanced technologies in architecture and urbanism challenges us to rethink the role of the profession, the effectiveness of these tools, and the nature of the design process as an artistic endeavor.

As machines increasingly co-author designs—imagining, generating, and optimizing forms—we must ask: what does it mean to be an architect, designer or urban planner in this new paradigm? Are these tools enhancing creativity, or eroding the human-centered intuition that has long defined architectural practice? Do these tools act as genuine collaborators, or do they diminish the professional role to that of an operator? How do we balance their data-driven logic with the emotional and cultural layers of design?

This exhibition serves as both a platform and a stage where technology and performance converge to explore these questions. Through live demonstrations, interactive installations, and speculative provocations, the audience will witness the dialogue between human creativity and machine intelligence.

Curatorial Questions:

Roles
What are the fundamental positions and underlying ideologies of architectural and urban stakeholders within the context of the AI and Tech movement, and how do these perspectives shape our understanding of architectural and urban design practice?

Practices
In what ways do AI and Tech prompt us to reimagine architectural and urban design practices through a pragmatic lens, challenging traditional methodologies and expanding the boundaries of creative possibility?

Experience
What artistic expressions emerge from AI and Tech as a multimodal tool for creation, analysis, and evaluation, and how do these forms enrich our appreciation of the tangible aspects of architecture and urban design in an increasingly digital landscape?

Open Call:

The selection is based on several criteria:
1) relevance to the curatorial theme,
2) responses to the issues,
3) past experience of applicants and
4) technicality of the proposal.

Curatorial Result:

Among the 97 proposals collected through the open call, each presented distinct perspectives on the issue, area of implementation, and form of public engagement.

Regarding issues, 26 proposals addressed Design for Identity, 23 focused on Design for Community, 21 explored Design for Public Good, 14 examined Design for Resilience, 10 considered Design for Well-being, and 3 addressed Design for Rurbanity.

Regarding the area of implementation, 20 proposals focused on Concept Development, 19 on Research and Analysis, 16 on Prototyping, and 13 on Community Engagement. Remaining proposals addressed Feasibility studies, Preliminary design, Schematic design, Detailed design, Construction and Documentation, Tendering and Commissioning, and Maintenance and Operation.

Regarding the form of public engagement, 30 proposals featured Exhibits as Performance, 23 involved the Public as Co-Designers, 20 proposed the Public as Prototype Users, and 10 employed Exhibits as Construction Process. The remainder included the Public as Analysts and Policy-makers.

Additionally, 48 groups identified themselves as AI technology amateurs or experimenters, while experts and academic institutions accounted for 30 and 19 groups respectively.

The overall data reflects a strong interest in AI and technology among architecture and urban design practitioners. Their engagement with AI and technology applications is diverse, with experimental efforts primarily focused on early-stage work and exhibits predominantly serving expressive purposes. The curated outcomes are expected to provide baseline data for the industry, facilitating further discussions on how artificial intelligence technology can revolutionise design practices.

Critical Issues:

Design for Public Good
inclusivity; social engagement; public spaces; usage patterns; multifunctional designs; resource allocation; management efficiency; infrastructure; traffic congestion; quality of life; environmental monitoring; green spaces; real-time data; neighborhood characteristics; sociocultural essentials; mixed-use developments; design simulations; community interaction; vibrant communities; equitable communities.

Design for Resilience
environmental changes; societal changes; economic changes; sustainable design; environmental impact; regeneration; biodiversity; flexibility; evolving needs; circular economy; reuse; waste reduction; resource management; water; energy; materials; community resilience; hardships; data analysis; scenario modeling; climate adaptation; energy-efficient design; smart city; real-time monitoring; natural environment.

Design for Rurbanity
rural characteristics; rural-urban symbiosis; conservation; revitalization; vernacular tradition; resource flows; population patterns; land use; connectivity; sustainable practices; environmental change; development impacts; cultural heritage; investment areas; infrastructure; local resources; documentation; traditional building practices; resilience; sustainability; community outreach; decision-making; integrative environments; quality of life; resilient communities

Design for Identity
corporates; municipalities; art; culture; branding; homogenization; local characteristics; consumer preferences; bureaucracy; participatory planning; community feedback; developments; citizens' needs; cultural expression; commodification; de-funding; local artists; visibility; interaction platforms; authenticity; stakeholder collaboration; community engagement; values.

Design for Well-being
liveability; post-pandemic; adaptations; ageing populations; mental health; psychology; high-density areas; accessibility; population data; green areas; communal facilities; amenities; safety; community needs; inclusive neighborhoods; public health scenarios; adaptable spaces; social interaction; environmental factors; robust evaluation methods; AI analytics

Design for Community
participation; decentralization; information transparency; inclusion; equity; digital platforms; access barriers; local communities; data analysis; decision-making; trust; public-private partnerships; mobilize resources; marginalized groups; needs.

Areas for Experimentation

Concept Development
The initial stage focuses on brainstorming design possibilities based on client needs, site conditions, and contextual factors. AI-driven tools analyze historical data and design precedents to help designers explore innovative strategies. Sketches, models, and digital renderings visualize concepts, allowing for early experimentation with forms and materials.

Research and Analysis
Research encompasses zoning regulations, environmental impact, and cultural context. AI algorithms analyze large datasets to identify community trends, informing design decisions. Site analysis, using Geographic Information Systems (GIS), reveals geographical, social, and climatic conditions that influence the design. Academic studies in sociology and environmental psychology provide insights into community behaviors.

Community Engagement
Engaging the community throughout the process ensures designs meet local needs. This stage involves workshops, public meetings, and feedback sessions to gather input from residents. AI platforms analyze community sentiment in real-time, enabling designers to respond to concerns dynamically. Research on participatory design methods enhances this engagement, fostering collaboration and promoting social equity.

Feasibility Study
This stage assesses project viability by analyzing financial aspects, site conditions, regulatory constraints, and market demand. AI assists in modeling financial scenarios and predicting project outcomes, helping stakeholders make informed decisions about proceeding.

Preliminary Design
Initial design proposals focus on layout, spatial organization, and aesthetics. Feedback from clients and users allows for iteration and refinement. Experimentation with materials and construction methods, supported by research on sustainability, tests innovative ideas.

Schematic Design
Schematic drawings and models are created based on client feedback, considering structural and mechanical systems. AI simulates the design intention, building performance and energy efficiency, achieves interdisciplinary collaboration and optimizes designs.

Detail Design
Comprehensive specifications and detailed drawings are prepared to integrate every project aspect. Advanced tools assist in creating precise models that align with the overall vision and comply with building codes.

Prototyping
Prototyping enables feedback-based design exploration through 1:1 physical models and immersive simulations. This process helps architects test building performance and engage communities with tangible representations. Collaborative design practices are enhanced through cloud-based platforms, contributing to a knowledge base for future projects.

Construction Documentation
Detailed construction documents, including drawings and specifications, provide clear instructions for builders. AI can automate parts of this process, reducing errors and improving efficiency, critical for translating design into construction instructions.

Bidding and Commissioning
This stage involves reviewing bids and negotiating contracts, ensuring selected contractors align with the project requirements and budget. AI streamlines the bidding process by analyzing contractor performance data and predicting costs.

Construction Administration
During construction, architects oversee the project to ensure adherence to design intent. Regular site inspections for resolving immediate problems are essential. AI technologies, such as drone scanning and Building Information Modeling (BIM), monitor progress in real-time, providing valuable data for decision-making.

Post-Occupancy Evaluation
Upon completion, designers evaluate how well the design meets goals, gathering user feedback and assessing performance data. AI analyzes this data to identify patterns for improvement, creating knowledge databases that promote learning and adaptation.

Sustainability Assessment
Sustainability assessment evaluates the environmental impact of designs using AI tools and frameworks like Life Cycle Assessment (LCA). For example, AI simulates scenarios to predict long-term sustainability based on energy efficiency and material use.

Regulatory Compliance
Ensuring compliance with regulations is critical. Architects must stay informed about laws and codes, often collaborating with legal experts such as Authorised Persons. In the future, AI can assist in navigating the permitting process by automating documentation and streamlining workflows.

Maintenance and Operation
Post-construction, clients develop maintenance plans outlining routine checks and repairs to ensure functionality. AI facilitates predictive maintenance by analyzing performance data and alerting managers to potential and immediate issues, optimizing energy use and occupant comfort.