Dark patterns are commonly defined as manipulative interface designs that undermine user autonomy, with prior work evaluating their impact through predefined negative framings. However, users’ lived experiences of influential design are often more heterogeneous and situational. This paper examines how users experience and interpret expert-defined dark pattern elements in Chinese livestream e-commerce. We conducted a qualitative study using video-stimulated recall interviews based on participants’ screen recordings (N=17), capturing real behaviors and in-situ reasoning. Our findings show that user responses extend beyond a simple compliance–resistance dichotomy, unfolding through a set of behavioral strategies, composite attributional reasoning, and diverse interpretations of influence. While some designs were perceived as coercive or deceptive, others were experienced as rational persuasion. We contribute a user-centered behavioral taxonomy and a model of design influence grounded in three experiential dimensions, offering insights into how influence is interpreted in dynamic, real-world interaction contexts.

Disengagement plays an important role in the overall game experience. However, extensive game research has focused on creating engaging experiences, whereas how players disengage remains insufficiently understood. Emerging studies have outlined characteristics of disengagement in screen-based video games. Little is known about how virtual reality (VR) shapes players’ disengagement process and what strategies might support positive disengagement experiences in VR games. Therefore, we conducted a co-design workshop (n = 18) and an online survey (n = 115) with VR game players. Our findings show that disengagement in VR games is often driven by factors such as physical discomfort and emotional overload. Participants adopt different disengagement strategies depending on the situation, such as restoring physical-world awareness to assist disengagement decisions. Then, we summarize three strategies for fostering positive disengagement experiences. Finally, we discuss these strategies, such as MR-based narrative space, extending the understanding of virtual-to-real transitions from a game experience perspective.

Mixed Reality (MR) game-based learning has been widely adopted in education, enhancing knowledge retention and enriching the overall experience. While embodied learning can be supported by MR environments that provide natural sense-making and trial-and-error in situated contexts, they often lack adaptive and personalized support. This gap can be addressed by Artificial Intelligence (AI), which enables dynamic and contextualized game scenarios. We present Elementor, an MR serious game that integrates chemistry learning and a fantasy game design scenario powered by AI. For fantasy character design, we employ LoRA model training to create anthropomorphic representations of different chemical elements. For dialogue, we employ large language models (LLMs) to enable real-time generation of context-aware interactions. We hope to facilitate engaging and effective chemistry learning. The system was evaluated through an exploratory study combining quantitative questionnaires and qualitative semi-structured interviews (N=28). Results highlight the potential of AI-powered, contextualized MR serious games for improving both motivation and learning outcomes. Our contributions cover three aspects: (1) we propose a novel AI-human codesign workflow for the development of serious MR games; (2) we explore anthropomorphic storytelling with embodied interactions in educational games; and (3) we provide design insights for future MR serious games.