Conference Agenda

Traditional engineering design pedagogy, built on linear, phase-gated project management models, is increasingly insufficient for preparing students for modern AI-driven design projects. This new paradigm presents a dual pedagogical challenge: 1) a process challenge, as AI projects are defined by iterative, data-dependent, and uncertain lifecycles, and 2) a social challenge, as AI shifts the engineer's role from "doer" to "orchestrator" within a complex human-AI collaboration. This paper presents an integrated 10-unit curriculum development from an Erasmus+ EU project, as a novel pedagogical solution. The framework is designed to scaffold process uncertainty, systematically bridging the gap between traditional project management and the unique, iterative lifecycles of AI projects. This scaffold then enables the teaching of new, human-centric collaborative competencies through active learning interventions, which are grounded in practice-based learning principles. These include stakeholder co-design simulations and a role-playing exercise where students must navigate the inherent conflicts between AI performance and human-centred design goals.

As generative AI increasingly permeates everyday technologies, UI/UX design must shift from optimizing usability toward shaping human–AI relationships. This paper examines how relational UI/UX for human–AI coexistence can be conceptualized through a futures-oriented design project. Rather than treating AI solely as a functional tool, the project positioned AI as both design medium and relational counterpart, influencing how future interfaces were imagined and structured. Analysis of the design process and resulting concepts reveals a shift from interface design centered on task efficiency to interface design oriented toward coexistence and mutual influence. In this reframing, UI/UX operates not merely as a surface for interaction but as a condition that configures human–AI relations. The study argues that designing for human–AI coexistence requires reconceiving UI/UX as relational infrastructure, expanding its role from functional mediation to the construction of shared futures.

This study investigates how generative artificial intelligence (AI) operates in educational spaces to determine its ability to develop educational feedback systems. This study situates AI-supported feedback within the broader paradigm shift toward hybrid human–AI socio-technical systems in design education. Qualitative methods were used to conduct semi-structured interviews with 25 students and 6 professors. The results demonstrate that students and teachers face ongoing difficulties with their current feedback system, mainly due to time constraints to provide excellent feedback. The participants envision a Human–AI collaborative process model to solve learning and teaching problems.

The rapid advancement of Generative AI is reshaping industrial design practices, calling for informed responses within design education. This study examines industrial designers' perceptions, attitudes, and behavioral intentions regarding the integration of GenAI across the stages of the Double Diamond design model, exploring its implications for design pedagogy. Using a mixed-methods approach, survey data were gathered from 70 participants, supplemented by ten semi-structured interviews with educators and practitioners in South Korea who actively use GenAI tools. Thematic analysis revealed that participants identified both significant opportunities and limitations when employing GenAI within different design stages. Key factors were identified for effectively integrating GenAI into industrial design education, including foundational design competencies, AI literacy and understanding, AI operational fluency, and ethical governance to support reflective and responsible practice. This research contributes to ongoing discussions about how industrial design education might evolve to address the challenges and harness the potential of generative technologies.