The overreliance on large language models (LLMs)-generated answers poses risks to the development of learners’ critical thinking. Socratic instruction, which follows “tutor asks, student answers” approach, could mitigate overreliance by engaging learners with LLM-generated questions rather than passively seeking answers from LLMs. However, learners without effective response strategies often produce superficial answers and therefore undermine Socratic instruction. To bridge the gap, we first conducted a formative study (N=20) to analyze learners’ dialogue logs and interviews, deriving 18 Scaffolding Cards as response strategies to guide learners in framing their answers. A subsequent mixed-methods study (N=34) demonstrated that Scaffolding Cards improved critical thinking, optimized cognitive load allocation, and increased learning satisfaction compared to that without scaffolds. Our work reconfigures scaffolding by incorporating state-aware, agency-preserving, and function-transparent support. We further provide actionable implications for designing responsive and personalized scaffolding to facilitate learner-LLM interaction, introducing innovative perspectives for reclaiming learner agency in LLM-driven education.

Although waiting during loading is a common online experience, the overall understanding of loading indicator designs and their impact on user experience (UX) remains limited. To address this, we conducted a systematic review of 38 articles, focusing on the following research questions: (1) What aspects of UX are affected by loading times? (2) What is the relationship between loading time and UX? (3) How can loading indicators be designed? (4) What is the impact of different loading indicator designs on UX? As a result, this study highlights the different dimensions of UX during the loading process, reveals how perceived loading time distortion affects UX, and identifies different designs of loading indicators, including those most effective in optimizing UX. This study contributes to the body of knowledge on the waiting experience, especially for those involved in optimizing indicator designs, and proposes future research directions for designing more diverse indicators.

Waiting for system loading is a common scenario that often diminishes user experience, leading to dissatisfaction. Well-established visual indicators like progress bars can not directly apply to the interactions with voice assistants (VAs) like Siri. As VAs continue to rise in popularity, this research aims to explore the design of auditory indicators, particularly human speech, for optimizing waiting experiences in Voice User Interfaces (VUIs). We first organized focus groups (N=35) to identify design considerations for speech indicators, uncovering design opportunities in integrating explanations and humor. Subsequently, we conducted an empirical study (N=30) to evaluate the effects of speech indicators with two levels of explanation and humor on the waiting experience, measured by attention, perceived time, pleasure, and overall satisfaction, during both short and long loading durations. Our findings suggest significant potential for incorporating explanations and humor into VUIs, offering actionable insights for designing effective speech indicators that improve waiting experiences.

The interaction between autonomous vehicles (AVs) and pedestrians has gained significant attention, leading to the exploration of external human-machine interfaces (eHMIs) equipped on AVs to facilitate effective communication. While existing research suggests that perceptions of vehicle appearances may influence interactions between pedestrians and AVs, a comprehensive study on the eHMIs related to AV appearance remains lacking. Therefore, we conducted a virtual reality (VR) experiment to investigate how AV appearances affect pedestrians’ acceptance regarding Awareness, Intent, and Harmony during interactions with various eHMIs. Leveraging the fuzzy set qualitative comparative analysis (fsQCA) method, we identified specific combinations of AV appearances and eHMIs that yield either high or low-performance interactions. For example, our findings reveal that text displays exhibit high performance in terms of awareness on AVs that are aggressive and ordinary. Furthermore, we distilled design guidelines to provide actionable suggestions for the design of eHMIs, fostering the acceptance of AVs among pedestrians.