Aung Pyae

Aung Pyae

Text-to-3D generative AI systems create navigable environments from natural language prompts, but unlike text-to-image generation, evaluation requires embodied exploration of spatial coherence, scale, and navigability. We present the first empirical study of a commercial text-to-3D platform, combining think-aloud protocols, behavioral observation, and validated measures of usability, presence, and engagement. We report three findings. First, asymmetric expressibility: users readily convey semantic intent (themes, atmosphere) but struggle to specify spatial structure (layout, scale), reflecting a language-to-space limitation rather than a skill deficit. Second, episodic presence: immersion arises when expectations align with outputs but does not accumulate into sustained place illusion. Third, structural iteration breakdowns: refinement fails due to interaction barriers - poor discoverability, opaque feedback, and high temporal costs - rather than user limitations. Together, these dynamics form a reinforcing cycle in which spatial mismatches persist, producing episodic presence and ongoing sensemaking. We reframe text-to-3D interaction as negotiated meaning-making rather than linear prompting, and argue that effective systems require hybrid input modalities, transparent feedback, and low-cost iteration.

Aung Pyae

More-than-human design challenges anthropocentric assumptions by foregrounding non-human entities as stakeholders, yet designers face an epistemic boundary: they cannot directly access non-human experience. We present an exploratory study examining how generative AI -- specifically a text-to-3D world generation platform producing navigable environments -- may function as a speculative mediator in more-than-human design. Through a qualitative study with five participants from engineering and sustainability backgrounds engaging with AI-generated worlds derived from non-human traces, we investigate how instant exploration -- navigating generated environments within seconds -- shapes reflection, iteration, and provisional treatment of outputs. Our findings suggest that navigating AI-generated environments supports reflection-in-action distinct from evaluating static representations, while designers' epistemic stances oscillate between treating outputs as generative provocations and as authoritative representations. We propose technologically-amplified backtalk and productive provisionality as preliminary lenses for understanding how navigable AI-generated 3D environments can surface anthropocentric assumptions in more-than-human design.

Aung Pyae

Recent advancements in artificial intelligence (AI) have broadened the applicability of AI-generated images across various sectors, including the creative industry and design. However, their utilization in educational contexts, particularly among undergraduate students in computer science and software engineering, remains underexplored. This study adopts an exploratory approach, employing questionnaires and interviews, to assess students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks such as presentations, reports, and web design. The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits. However, concerns regarding the lack of technical precision, where the AI fails to accurately produce images as specified by prompts, moderately impact their practical application in detail-oriented educational tasks. These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues, while also setting quality standards for AI-generated images to enhance their educational use. Enhancing the capabilities of AI tools to meet precise user specifications could foster creativity and improve educational outcomes in technical disciplines.