conduct human-subject testing

Observational studies in social VR face a structural tension among observer visibility, data traceability, and participant autonomy—challenges inadequately addressed by conventional public-space ethics frameworks. This paper conducts a narrative literature review of HCI scholarship on ethical observational research in digital environments, synthesizing insights to propose five domain-specific ethical guidelines for public social VR settings. Its contributions are threefold: first, it introduces *observer visibility* as a core ethical dimension—an analytical innovation not previously foregrounded in VR ethics; second, it advocates replacing static, one-time informed consent with platform-enabled design interventions and community-engaged, iterative consent processes; third, it develops a pragmatic yet theoretically grounded ethical assessment framework. The resulting principles provide systematic guidance for VR research governance, platform policy development, and ethical practice by researchers.

To address challenges in clinical trials—including manual protocol execution, poor real-time responsiveness, and delayed adherence monitoring—this paper proposes a personalized participant agent system integrating finite-state machines (FSMs) with large language models (LLMs). The system employs interpretable FSMs to formalize protocol logic, while leveraging LLMs for structured data extraction and adaptive intervention decisions during conversational interactions. It further enables real-time protocol compliance verification and end-to-end behavioral audit tracing. We introduce the first closed-loop protocol execution paradigm combining “FSM-driven control” with “LLM-enhanced reasoning,” ensuring traceable participant behavior, context-aware interventions, and verifiable protocol logic. Evaluated in multicenter, time-sensitive trials, the system significantly reduces data entry error rates, improves adherence monitoring latency to sub-second granularity, and cuts human supervisory costs by over 70%.

Existing research platforms predominantly focus on simplified tasks or single-perspective analysis, limiting their capacity to support interdisciplinary empirical studies of complex human-AI collaborative decision-making. To address this gap, we introduce CREW—an open-source platform featuring a novel modular architecture designed for ecologically valid collaborative scenarios. CREW integrates cognitive experimental paradigms, real-time multimodal physiological signal acquisition (EEG, ECG, EMG), human-guided reinforcement learning benchmarks (PPO, SAC), and an extensible task framework—unifying human behavioral modeling and AI algorithm evaluation. CREW is the first tool enabling real-time, multidisciplinary, high-ecological-validity studies of human-AI teams. Within one week, we validated the platform across 50 participants: results demonstrate significant improvements in task flexibility, depth of human engagement, and rigor of algorithmic assessment. CREW thus establishes a unified experimental foundation for both foundational research and applied development in human-AI collaborative decision-making.

Social challenge studies—such as online experiments exposing participants to harmful content—lack systematic ethical guidelines; risk mitigation and oversight mechanisms remain markedly underdeveloped compared to established frameworks in medical challenge research. Method: This paper systematically adapts the mature ethical framework of medical challenge studies to computational social science, integrating interdisciplinary analysis to develop context-sensitive ethical principles for online environments and proposing a novel assessment mechanism for long-term latent harms. Contribution/Results: It establishes the first operational ethical standards system specifically designed for social challenge research, thereby addressing a critical regulatory gap. It advances institutionalized ethics review processes tailored to digital experimentation and catalyzes scholarly discourse on risk governance in digital research contexts. By bridging disciplinary divides, the work provides actionable guidance for researchers, ethics boards, and platform partners navigating ethically complex online interventions.

Expert-centered research—particularly in highly specialized domains such as immersive digital forensics—faces significant challenges, including stringent ethical review requirements, difficulties in expert recruitment, high operational costs, and confounding human factors. This paper draws on a three-year doctoral study to systematically identify and categorize unique barriers across ethical, organizational, and methodological dimensions. We propose a hybrid methodological framework integrating Human-Computer Interaction (HCI) and digital forensics perspectives, comprising contextual interviews, participatory design, in-situ prototype evaluation, and reflective journaling, underpinned by a dynamically adaptive research protocol. The study yields 12 high-fidelity, empirically grounded practice guidelines, adopted by three law enforcement agencies. Additionally, an open-source protocol template reduces startup overhead for comparable studies by over 40%, substantially enhancing reproducibility and real-world impact of expert-collaborative empirical research.

This study investigates the acceptance of healthcare robots and associated ethical challenges across cultural contexts, focusing on attitudes of 298 caregivers from the United States, Mexico, and Chile toward four task categories: logistics, positioning assistance, vital signs monitoring, and mobility support. Employing a mixed-methods approach, the research integrates quantitative analysis grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Culture-Aware Norms (CAN) model with qualitative ethical analysis based on established literature. It presents the first systematic cross-national comparison of caregivers’ perceptions of robotic roles and ethical concerns. Findings indicate that caregivers generally favor robot deployment in physically demanding and logistical tasks over those requiring intensive interpersonal interaction. Common ethical concerns—such as reliability and human oversight—are identified, alongside culturally specific differences in ethical priorities, leading to a proposed context-sensitive, socially oriented framework for responsible robot design.

This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.

This study addresses the opacity of mandatory wearable sensing systems in high-stakes institutional settings, where users struggle to understand how their behavioral data are translated into consequential judgments. Drawing on in-depth interviews and behavioral observations of 24 individuals under electronic monitoring in China’s community correction system, the research introduces the concept of “sensor literacy” to elucidate how users actively construct risk awareness, probe system boundaries, and adapt their conduct under opaque surveillance. Two adaptive patterns emerge: limited behavioral flexibility when rules are predictable, and excessive activity contraction under high uncertainty. Notably, computational habits persist even after device removal. These findings offer critical insights for enhancing transparency and human-centered design in institutional sensing systems.

This study addresses the overreliance on technical assessments in current AI safety and ethics research, which has led to insufficient understanding of human-AI interaction risks due to the neglect of empirical human-centered studies. Drawing on 93 expert surveys and 17 in-depth interviews spanning four communities—technical, socio-technical, governance, and normative—the research uncovers methodological tensions among scholars with different backgrounds, particularly highlighting the low engagement of technically oriented researchers with human-centered approaches. While the value of human research is widely acknowledged, its integration remains constrained by concerns about validity, scarce resources, methodological preferences, and inadequate infrastructural support. To counter superficial adoption—termed “human-washing”—the paper proposes actionable pathways for effectively incorporating human-centered research into AI safety practices, offering methodological guidance for interdisciplinary convergence.