Conference Agenda

As Artificial Intelligence (AI) becomes embedded in design education and practice, global experts highlight a key challenge: preparing design students to engage with AI responsibly and contextually. This paper explores how liberal education’s interdisciplinary approach builds contextual understanding for designers to leverage AI tools effectively. ​​Informed by the philosophy of an Indian design school that is rooted in liberal education, along with insights from global design experts, the study introduces a conceptual model: CI-AID (Contextual Intelligence for AI in Design). CI-AID extends on the traditional double diamond, where interdisciplinary knowledge enables a comprehensive discovery of the context that the design problem lies within. It illustrates the role of a liberal approach in augmenting divergent thinking, and thus building deeper contextual intelligence. Ultimately, the paper elucidates how such intelligence guides designers toward more situated and deliberate engagements with AI, and can transform AI-driven design practice toward more thoughtful and enduring outcomes.

As generative AI tools increasingly permeate design education, questions arise about their impact on novice students’ creative competence. This study addresses two research questions: (RQ1) How does AI participation affect students’ creative outcomes, particularly the visual dimensions of their work? (RQ2) How do evaluators redefine competence amid AI involvement in novice design students’ creative processes? Using a mixed-method approach, Study1 analysed professional evaluations of 88 student projects, showing that AI-assisted works scored higher in creativity and visual elaboration but not in design logic, with greater score divergence.Study2 involved interviews with eight design experts, revealing tensions in evaluators’ attribution of competence and how AI reshapes judgment criteria. Findings indicate that generative AI alters students’ visual outcomes and evaluative norms, underscoring the need for a reflective, transparent integration of AI into design pedagogy. Results also suggest updating frameworks for assessing design competence in AI-augmented learning environments.

As artificial intelligence increasingly takes over the designer’s role in shaping interfaces for services and platforms, a key challenge is how to teach young designers to critically reflect on how design mechanisms shape users and their everyday lives.

Interface Cultures, Forever was a design workshop that challenged the conventional notion of the digital interface. Participants were asked to design critical interfaces - concepts that reveal tensions, paradoxes, and invisible mechanisms within digital life. Through speculative and ironic prototypes, students explored how interfaces shape behavior, emotion, and identity. The projects articulate a landscape of cultural critique, where the interface becomes both medium and message: from bodily automatism and sensory addiction to algorithmic control, mediated self-representation, temporal anxiety, and digital silence. Each prototype employs strategies of parody, over-performance, and inversion of norms, thus repositioning interface design as an act of commentary - an inquiry into the conditions of human experience within contemporary techno-social systems.

This study examines the influence of generative artificial intelligence (GenAI) on design students’ self-efficacy and perceived confidence during a logotype creation task. Through an exploratory pre–post experimental protocol, 35 undergraduate design students generated manual sketches and subsequently refined them using a GenAI tool. The results show that, while GenAI effectively transformed manual sketches into more elaborate visual representations, its impact on students’ perceived creative mastery remained moderate. Self-efficacy and confidence levels showed no significant differences after the intervention, although slight improvements were observed in aspects related to imagination, collaboration, and willingness to face challenging situations. Students assessed the utility, novelty, and relevance of GenAI cautiously, suggesting a reflective rather than fully enthusiastic adoption. However, the results reveal GenAI's potential to support the creative process and incorporate these tools as part of exploratory processes in design. Future research should investigate long-term integration of GenAI within diverse design learning contexts.

The integration of AI into UX design presents both opportunities and challenges for UX education-how to leverage AI’s efficiency while developing students’ human skills. This pilot study examines LLMs as an analytical AI co-pilot in undergraduate user research. Students (N=17) conducted two cycles of user research: one using traditional methods, and another with Claude-assisted analysis. Findings show 14 (82%) of students perceived the Claude-assisted approach as faster in synthesizing data and extracting insights. However, the results also reveal a trade-off: AI's efficiency may reduce empathy-building, with 6 (35%) students reporting diminished user connection. This research illuminates the inherent tension in AI-augmented UX education and suggests a hybrid pedagogical model that requires traditional user research while leveraging AI for efficiency, providing a critical framework for future research. This pilot study underscores the need to balance technological augmentation with fundamental human skills in UX design education for the AI age.

This study investigates how Industrial Design (ID) programs can effectively adopt and integrate Generative Artificial Intelligence (Gen-AI) into curricula, while respecting the well-established ID workflow and preserving the core ID values of originality and creativity. Through multi-perspective qualitative research (including 7 ID faculty members, 6 industry professionals, and over 70 ID students), this study maps current AI tools, roles, and use cases within the ID workflow, identifies key pedagogical challenges, and proposes strategies that bridge foundational design traditions with emerging AI opportunities. Findings introduces a progressive learning trajectory, supported by innovative teaching practices that cultivate an AI-collaborative design mindset. By emphasizing critical thinking, ethical reflection, and responsible use, this study positions AI as a complement to design heritage, enriching both ID curricula development and future career pathways for industrial designers.