Ruiqi Chen

Yibo Meng, Guangrui Fan, Bingyi Liu et al.

This study examines whether engagement with social robots translates into improved human-directed social abilities in autistic children. We conducted an 8-week home-based randomized controlled trial with 40 children aged 5--9 using a commercial social robot (Qrobot). Families were assigned to either continued robot access or robot withdrawal. Quantitative measures and caregiver interviews assessed anxiety, social motivation, emotion inference, and empathy. Results showed that continued robot access significantly reduced anxiety, confirming strong affective benefits and high usability. However, children in the withdrawal group demonstrated greater improvements in social motivation, emotion understanding, and empathic behaviors toward caregivers and peers. Qualitative findings revealed a "handoff versus siloing" pattern: withdrawal promoted reorientation toward human social interaction, while continued access concentrated engagement within the child--robot dyad and limited transfer to real-world contexts. We interpret these results as evidence that high engagement does not guarantee social transfer.

Yibo Meng, Ruiqi Chen, Zhuoran Lu et al.

This study presents a five-year longitudinal mixed-methods study of 17 Chinese digital painters, examining how their attitudes and practices evolved in response to generative AI. Our findings reveal a trajectory from resistance and defensiveness, to pragmatic adoption, and ultimately to reflective reconstruction, shaped by strong peer pressures and shifting emotional experiences. Persistent concerns around copyright and creative labor highlight the ongoing negotiation of identity and values. This work contributes by offering rare longitudinal empirical data, advancing a theoretical lens of "identity and value negotiation," and providing design implications for future human-AI collaborative systems.

Xuewen Luo, Ding Fan, Ruiqi Chen et al.

As autonomous vehicles progress toward fully driverless mobility, a critical question emerges: who understands and responds to passengers when the human driver is absent? Existing autonomous driving systems primarily optimize predefined navigation and control objectives from external scene observations, but they remain limited in perceiving and reasoning about in-cabin human intent. In this paper, we propose Intent2Drive, a unified framework for holistic human intent understanding and human-aligned planning. Instead of treating passenger intent as explicit commands alone, Intent2Drive models intent as a latent cognitive state shaped by language, personal attributes, emotional and physical conditions, behavioral signals, and situational context. To support this formulation, we construct a Holistic Intent Dataset (HID) that provides structured supervision over both explicit and implicit intent cues. Built upon HID, our Theory-of-Mind-inspired Human Intent Reasoner (HIR) infers a Latent Human State (LHS) and further translates it into a planner-compatible Human Intent Objective (HIO). We then introduce a Hierarchical Intent-Conditioned Planner (HICP) that incorporates HIO into route-level and trajectory-level planning, enabling driving behaviors to remain aligned with passenger needs across different planning horizons. Extensive experiments show that Intent2Drive improves structured human intent inference and HIO construction while preserving competitive closed-loop planning performance. These results demonstrate a promising step toward passenger-responsive autonomous driving systems that can reason about, interpret, and act upon human intent in driverless mobility.