Conference Agenda

While Artificial Intelligence (AI) has demonstrated significant potential in the divergent phases of design, its role in the convergent phase (solution selection) is less understood. This study investigates how different AI-assisted selection modes impact a designer's creative efficacy. We conducted an experiment (N=40) with graduate design students, who used four distinct AI support modes—(1) Textual Description, (2) Pros/Cons Comparison, (3) AI Scoring, and (4) Direct Recommendation—to select a final design from a set of concepts. Efficacy was measured using behavioral data (selection time, choice), user satisfaction (QUIS), cognitive load (NASA-TLX), and technology acceptance (TAM). Results show that the AI Scoring mode performed significantly better, leading to the highest user satisfaction and acceptance while imposing the lowest cognitive load. Qualitative data further suggests that while designers value the efficiency of scoring, they also demand transparency in the AI's decision-making logic.

Node-based interfaces are rapidly gaining traction in AI-driven design tools, yet a clear and comprehensive design framework remains essential. This paper addresses this critical need by presenting 10 carefully distilled design principles. These principles emerged from a thematic literature review of 14 recent, impactful papers published between 2020 and 2025, specifically focusing on Human-AI Interaction and visual programming. We meticulously grouped these principles into three core categories: Interaction & Usability, Human-AI Co-Creation & Creativity Support, and Explainability & Extensibility. Crucially, we also analysed how these principles intricately interconnect at a detailed level, highlighting their dynamic relationships. Based on this foundational analysis, we introduce a hierarchical Conceptual Model. This model vividly visualizes the principles as layered foundations, systematically working towards a robust Human-AI Partnership. This framework evaluates existing node-based AI tools and guides new designs to boost usability, support creative co-creation, and build user trust.

Artificial intelligence (AI) and automated processes hold significant potential to enhance service user experiences, by augmenting or fully automating aspects of service delivery. However, if not carefully designed, these systems also risk creating unintended, widespread harms, at unprecedented speed and scale. Drawing upon trauma-informed principles and developed in collaboration with social service delivery practitioners, we designed the Trauma Informed AI Assessment Toolkit (TIAKIT). TIAKIT supports users in critically designing, reviewing, and evaluating current or planned AI service integrations. We report on the iterative design process of TIAKIT and its adaptation across three formats — analogue, digital, and design game. Findings demonstrate the value and translating trauma-informed AI principles across different material forms and interaction modalities; offering insight into medium-specific affordances, their influence on participation and engagement, and how they shape different forms of dialogue and sensitivity about AI.

The rapid advancement of AI technologies necessitates a shift from reactive adoption to speculative inquiry into long-term consequences for educational systems. Grounded in Martin Heidegger's "The Question Concerning Technology," this research examines the epistemology of AI in design education. Methodologically, it employs speculative futurology, integrating speculative design, critical design, and scenario planning to explore possible futures, culminating in a philosophical mapping framework based on Heidegger's critical stance. The findings reveal a critical temporal mismatch between rapid AI capability growth and slower educational adaptation, creating urgency for human-centered frameworks. The research concludes that design education's future depends on consciously cultivating Critical AI Literacy while preserving uniquely human capabilities e.g. intuition, judgment, and creative reasoning. This approach ensures AI serves as a medium for "world discovery" rather than merely a tool for efficiency, positioning educators to shape technology's role intentionally rather than adapt to it reactively.

AI-powered voice scams are a fast-evolving public threat, combining rapid advances in synthetic speech technologies with sophisticated social engineering tactics. These scams are difficult to detect, exploit trust and familiarity, often resulting in deeply stigmatising experiences for those targeted. While anyone can be affected by scams, building awareness and confidence in navigating AI-related risks requires approaches tailored to the needs of diverse communities, particularly those who are more digitally excluded or marginalised. To address this, we developed AI & You—a suite of accessible, modular, and adaptable public education resources, collaboratively designed with the digital advocacy group Tactical Tech. We evaluated these resources in workshops with public learning facilitators and digital mentors from Good Things Australia. Our findings reflect on the process of designing AI safety materials, and the importance of translating AI literacy resources into actionable, contextually relevant knowledge that balances safety and risk awareness with empowerment and inclusion.