International Journal of Human-Computer Studies最新文献_第5页

Creating text-based AI clones of myself: Exploring perceptions, development strategies, and challenges 创造基于文本的我的AI克隆体：探索认知、发展策略和挑战

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-24 DOI: 10.1016/j.ijhcs.2025.103692

Donggun Lee , Suyoun Lee , Hyunseung Lim, Hwajung Hong

AI clones are evolving to include digital representations of real world individuals as chatbots. While often used to replicate famous figures, as the technology becomes more accessible, it is crucial to understand whether everyday users would create their own clones and how they interact with them. In this study, within the scope of AI-generated personas and their role in representing users’ needs and identities, we focus on personas that directly reflect the qualities of real humans. We define this as AI self clones—conversational AI representations that reflect their human creators—and examine how creators construct and engage with them. We conducted a 7-day study in which participants (N=12) created and interacted with their text based AI self clones using CloneBuilder, a web-based authoring interface for configuring and tuning AI self clones. The system enables individuals to create AI representations that encapsulate their unique personality, values, and interaction style. Our findings reveal that each participant developed a clone tailored to their personal circumstances. As the participants iteratively refined and tested their clone, their direction and expectations of AI clones evolved from performing specific roles to becoming entities that facilitated self exploration and relationship formation. Unexpected responses from the clone prompted self reflection and identity questioning. Overall, this paper explores the motivations for creating these clones, the strategies participants use to build and refine them, and the moments of emotional connection and break out experiences that emerge during the crafting process, along with key design implications, challenges, and ethical considerations in developing AI self clones.

人工智能克隆正在进化，包括现实世界中个人的数字表示，如聊天机器人。虽然这种技术经常被用来复制名人，但随着技术变得越来越容易获得，了解日常用户是否会创建自己的克隆以及他们如何与之互动是至关重要的。在本研究中，在人工智能生成的人物角色及其在代表用户需求和身份方面的作用范围内，我们关注直接反映真实人类品质的人物角色。我们将其定义为人工智能的自我克隆——反映其人类创造者的对话式人工智能代表——并研究创造者如何构建和与它们互动。我们进行了一项为期7天的研究，其中参与者（N=12）使用CloneBuilder（用于配置和调整AI自克隆的基于web的创作界面）创建并与基于文本的AI自克隆进行交互。该系统使个人能够创建包含其独特个性、价值观和交互风格的AI表示。我们的研究结果表明，每个参与者都根据自己的个人情况定制了一个克隆体。随着参与者不断完善和测试他们的克隆体，他们对人工智能克隆体的方向和期望也从执行特定角色演变为成为促进自我探索和关系形成的实体。来自克隆人的意外反应引发了自我反思和身份质疑。总体而言，本文探讨了创造这些克隆的动机，参与者用于构建和完善它们的策略，以及在制作过程中出现的情感联系和突破体验，以及开发AI自我克隆的关键设计含义，挑战和道德考虑。

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引用次数: 0

Players’ museum authoring game experience based on art expertise level: Case study of Occupy White Walls 基于美术专业水平的玩家博物馆创作游戏体验：以《占领白墙》为例

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-22 DOI: 10.1016/j.ijhcs.2025.103698

Joosun Yum , Yong Won Choi , Seoyoung Kang , Young Yim Doh

This study explores differences in the virtual museum authoring game Occupy White Walls based on players’ art expertise levels. We identified key distinctions between novice and expert players through thematic analysis of behavior observation, think-aloud, and semi-structured interviews with 12 players. Four themes emerged: authoring focus, authoring autonomy, asset recommendations, and asset manipulation. Experts spent more time on exhibition planning, emphasizing storytelling and autonomy in displaying artwork. In contrast, novices focused more on constructing virtual museum spaces, facing challenges in planning and curation while investing significant effort in spatial design. These differences highlight the role of art expertise in shaping gameplay experiences. The study underscores the need for expertise-sensitive game design in virtual museum-authoring environments. By analyzing player interactions in OWW, we propose recommendations for adaptive design strategies to foster engagement and personalization, ensuring a more inclusive and rewarding experience for players of varying expertise levels.

本研究探讨了虚拟博物馆创作游戏《占领白墙》中基于玩家艺术专业水平的差异。我们通过对12名玩家的行为观察、大声思考和半结构化访谈，确定了新手和专家玩家之间的关键区别。出现了四个主题：创作焦点、创作自主权、资产建议和资产操作。专家们把更多的时间花在展览策划上，强调展示艺术品的故事性和自主性。相比之下，新手更注重构建虚拟博物馆空间，在空间设计上投入大量精力的同时，也面临着规划和策展方面的挑战。这些差异突出了美术专业知识在塑造游戏体验中的作用。这项研究强调了在虚拟博物馆创作环境中对专业知识敏感的游戏设计的必要性。通过分析《OWW》中的玩家互动，我们提出了适应性设计策略的建议，以促进用户粘性和个性化，确保为不同专业水平的玩家提供更具包容性和奖励性的体验。

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引用次数: 0

Who has your back? Countering dark patterns in online shopping using interpersonal and AI-delivered support 谁会支持你？利用人际关系和人工智能提供的支持，打击网上购物中的黑暗模式

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-20 DOI: 10.1016/j.ijhcs.2025.103697

Chia-Hsin Lee , Hsuen-Chi Chiu , Tzu-Ching Lai , Chien Wen Yuan

Online shopping interfaces often employ dark patterns to influence user behavior, leading to impulsive buying decisions. This study aims to enhance consumer protection by exploring how interventions incorporating various support sources (interpersonal, AI-delivered, or self) with message types (cognitive vs. affective) can mitigate the impact of dark patterns on impulsive buying behavior. Grounded in the Stimulus-Organism-Response (S-O-R) framework, this study theorizes intervention designs through a 3 × 2 between-subjects experiment (n = 363), examining how different sources and formats of support influence user responses to manipulative design. Mediators like emotional state, argument quality, and image appeal were included in the model. The findings indicate that AI-delivered support can reduce impulsive buying intentions, particularly when persuasive content and appealing visuals are integrated, highlighting the potential of well-designed machine-mediated interventions. Practically, the findings inform the development of AI-driven interventions that can be embedded into shopping platforms to promote more ethical consumer experiences. This research advances theory by demonstrating that AI outperforms interpersonal supports in reducing shopping impulses, offering insights for interface design for addressing dark pattern influences.

在线购物界面通常采用暗模式来影响用户行为，导致冲动购买决策。本研究旨在通过探索将各种支持来源（人际、人工智能交付或自我）与信息类型（认知与情感）相结合的干预措施如何减轻黑暗模式对冲动购买行为的影响，从而加强消费者保护。本研究以刺激-有机体-反应（S-O-R）框架为基础，通过363个被试之间的3 × 2实验（n = 363）将干预设计理论化，考察不同来源和形式的支持如何影响用户对操纵性设计的反应。模型中包括情绪状态、争论质量和形象吸引力等中介因素。研究结果表明，人工智能提供的支持可以减少冲动的购买意愿，特别是当有说服力的内容和吸引人的视觉效果相结合时，这突出了精心设计的机器介导干预的潜力。实际上，研究结果为人工智能驱动的干预措施的发展提供了信息，这些干预措施可以嵌入到购物平台中，以促进更合乎道德的消费者体验。这项研究通过证明人工智能在减少购物冲动方面优于人际支持来推进理论，为解决黑暗模式影响的界面设计提供了见解。

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引用次数: 0

User judgment of an AI model is biased by its description: A study in a job interview training context 用户对人工智能模型的判断会因其描述而产生偏差

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-19 DOI: 10.1016/j.ijhcs.2025.103691

Sharon Lynn Chu , Marcin Karcz , Amal Hashky , Neha Rani , Theodora Chaspari , Winfred Arthur Jr. , Eric D. Ragan

The growth of artificial intelligence (AI) has introduced new AI-powered systems into many aspects of everyday life. Although many such systems may be embedded in contexts where long-term use is justified, there are also many cases where usage of such systems can be brief or limited to a single session. In those cases, initial information provided to the user about the AI model is important since users may not have enough engagement with the system to develop a mental model over time through use. Instead, users may simply rely on first impressions. However, little is known about how given information about the AI model in a system affects user judgment of the system. This work investigates this question within the context of job interview training. We conducted a controlled experiment where the description of the AI model within a simulated job interview training system was manipulated to describe the model as being either basic or more advanced. Participants in the condition where the AI model was described as more sophisticated and advanced reported significantly higher levels of agreement with the model outputs, more favorable ratings, and a greater willingness to use the system output.

人工智能（AI）的发展将新的人工智能驱动系统引入了日常生活的许多方面。尽管许多这样的系统可能嵌入在需要长期使用的环境中，但也有许多情况下，这些系统的使用可能是短暂的或仅限于一次会话。在这些情况下，提供给用户关于AI模型的初始信息是很重要的，因为用户可能没有足够的参与系统，无法通过使用一段时间来开发心理模型。相反，用户可能仅仅依赖于第一印象。然而，关于系统中关于人工智能模型的给定信息如何影响用户对系统的判断，人们知之甚少。这项工作在面试培训的背景下调查了这个问题。我们进行了一项对照实验，在模拟工作面试培训系统中对人工智能模型的描述被操纵，将模型描述为基本或更高级。在人工智能模型被描述为更复杂和先进的情况下，参与者报告了与模型输出显著更高的一致性，更有利的评级，以及更大的使用系统输出的意愿。

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引用次数: 0

User personas, ideation and large language models: A post-hoc study 用户角色、构思和大型语言模型：一项事后研究

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-17 DOI: 10.1016/j.ijhcs.2025.103690

Stefano De Paoli

Covering the full ideation of design with Large Language Models (LLMs) and user interview data remains an underexplored area in the current scholarship. This paper begins to address this gap and investigates the integration of LLMs in a user-centered design process, creating user personas based on qualitative interview data. This work further explores using these personas for deriving scenarios, and functionality requirements, also with LLMs. First, LLMs are used to identify key themes of users from interviews, subsequently synthesising these into personas. Second, personas are expanded into scenarios and associated functionalities for a digital platform, simulating the ideation phase of a design process. The findings illustrate how LLMs can potentially streamline these early design stages. An evaluation shows that the process discovers a list of functionalities which are, to a reasonable extent, comparable to those that human researchers have produced separately.

The study proposes a practical procedure for integrating LLMs into qualitative design ideation workflows. The dataset used comprises 26 Open Access interviews from a previous Horizon project, from which eight personas and related scenarios are derived. To support further experimentation and practical applications, several computational resources used in performing analysis and generating LLM-based personas are shared. This enables reproducibility and encourages broader exploration of LLM-assisted design ideation.

用大型语言模型（llm）和用户访谈数据覆盖设计的完整构思，在当前的学术研究中仍然是一个未被充分探索的领域。本文开始解决这一差距，并调查法学硕士在以用户为中心的设计过程中的集成，基于定性访谈数据创建用户角色。这项工作进一步探索了使用这些角色来派生场景和功能需求，也与llm一起。首先，法学硕士被用来从访谈中识别用户的关键主题，随后将这些主题合成为人物角色。其次，人物角色被扩展为数字平台的场景和相关功能，模拟设计过程的构思阶段。这些发现说明了法学硕士如何有可能简化这些早期设计阶段。评估表明，该过程发现了一系列功能，这些功能在一定程度上可与人类研究人员单独产生的功能相媲美。该研究提出了一个将法学硕士整合到定性设计构思工作流程中的实用程序。使用的数据集包括来自先前Horizon项目的26个开放获取访谈，从中衍生出8个人物角色和相关场景。为了支持进一步的实验和实际应用，在执行分析和生成基于llm的角色时使用的几个计算资源被共享。这使得可重复性和鼓励法学硕士辅助设计思想的更广泛的探索。

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引用次数: 0

Beyond content: Multimodal emotional responses predict online moral contagion across laboratory and real-world contexts 超越内容：多模态情绪反应预测实验室和现实环境中的在线道德传染

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-17 DOI: 10.1016/j.ijhcs.2025.103689

Rui Li , Xu Liu , Yipeng Yu , Waxun Su , Yueqin Hu

Research on online moral contagion has highlighted the role of emotion in shaping the diffusion of moralized content. However, existing studies primarily focus on the emotional attributes of content rather than viewers’ actual emotional responses. How individuals’ actual reactions influence diffusion, especially whether positive or negative emotions facilitate spreading, remains unclear. This research employed multimodal measurements to capture college students’ subjective and physiological emotional experiences while viewing moralized short videos, along with their subsequent contagion behaviors. In Study 1, participants watched videos on a computer and reported their sharing intentions. Emotional responses were measured using EEG, ECG, eye tracking, and self-reports. In Study 2, participants viewed and shared videos on mobile devices to better reflect real-world contexts, with emotional responses captured via electrodermal activity, respiration, accelerometers, and gyroscopes. Machine learning models showed that actual emotional responses predicted sharing more accurately than content attributes (80% vs. 69% for intentions; 82% vs. 61% for behaviors). Short videos evoking high arousal or positive valence were more likely to be shared. Shapley value comparisons indicated that EEG, gyroscope, electrodermal activity, self-reports, and accelerometer contributed more to prediction accuracy than respiration, eye tracking, and ECG. EEG had the highest contribution, while the non-intrusive measures, gyroscope and accelerometer, may serve as substitutes for more intrusive methods. These findings extend the theoretical framework of online moral contagion by consistently demonstrating that high-arousal, positively valenced emotional responses facilitate diffusion, and also provide practical guidance for selecting effective modalities and implementing non-intrusive emotional assessments in future research and applications.

关于网络道德传染的研究强调了情感在塑造道德化内容传播中的作用。然而，现有的研究主要关注内容的情感属性，而不是观众的实际情绪反应。个人的实际反应是如何影响传播的，特别是积极或消极的情绪是否会促进传播，目前还不清楚。本研究采用多模态测量方法来捕捉大学生在观看道德短视频时的主观和生理情绪体验，以及他们随后的传染行为。在研究1中，参与者在电脑上观看视频，并报告他们分享的意图。使用脑电图、心电图、眼动追踪和自我报告来测量情绪反应。在研究2中，参与者在移动设备上观看和分享视频，以更好地反映现实环境，并通过皮肤电活动、呼吸、加速度计和陀螺仪捕捉情绪反应。机器学习模型显示，实际情绪反应比内容属性更准确地预测分享（80%对69%的意图；82%对61%的行为）。短视频唤起高唤醒或积极的效价更有可能被分享。Shapley值比较表明，脑电图、陀螺仪、皮肤电活动、自我报告和加速度计比呼吸、眼动追踪和心电图更有助于预测准确性。脑电图的贡献最大，而非侵入性的测量方法如陀螺仪和加速度计可以替代侵入性更强的方法。这些发现扩展了网络道德传染的理论框架，一致地证明了高唤醒、积极的情绪反应促进了传播，并为未来的研究和应用中选择有效的模式和实施非侵入性情绪评估提供了实践指导。

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引用次数: 0

Movement instability and body image factors: Unobtrusive real-time detection of loss of control in ballet dancers 运动不稳定和身体形象因素：芭蕾演员失控的不显眼的实时检测

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-15 DOI: 10.1016/j.ijhcs.2025.103686

Concepción Valdez , Ana Tajadura-Jimenez , Monica Tentori

Understanding the relationship between movement variability and body image is essential in disciplines where motor control and body perception are closely linked, such as dance. The present study with 24 ballet dancers presents a computational approach for detecting moments of loss of control in elbow movement fluidity. This serves as an objective proxy for body image and body awareness, as subtle disruptions in movement, especially in joints that are not the primary focus of technical training, can reflect increased self-monitoring or discomfort, factors commonly associated with negative body image in dancers.

Through movement analysis during two common rehearsal contexts (i.e., marking choreography and full execution with music) we examined how movement variability differs based on task demands. Our findings show that loss of control moments are more frequent during marking, underscoring the influence of rehearsal context on both motor performance and body-related self-perception. Furthermore, we demonstrate that movement instability in the elbow joint can be associated with self-reported measures of body image and awareness, reinforcing the connection between motor variability and psychological well-being.

By identifying the elbow as a sensitive and reliable indicator of instability during ballet practice, our approach offers a lightweight alternative to full-body kinematic analysis, supporting practical applications beyond laboratory settings. From a Human-Computer Interaction (HCI) and Ubiquitous Computing (Ubicomp) perspective, this work contributes design insights for systems that integrate body image and movement behavior metrics. These findings open new possibilities for interactive technologies aimed at enhancing body image and improving movement precision in dancers and other movement practitioners.

在运动控制和身体感知密切相关的学科中，如舞蹈，理解运动变异性和身体形象之间的关系是必不可少的。目前的研究与24芭蕾舞者提出了一种计算方法来检测失去控制的时刻在肘部运动的流动性。这是身体形象和身体意识的客观代表，因为运动中的细微中断，特别是在不是技术训练的主要重点的关节，可以反映出自我监控或不适的增加，这些因素通常与舞者的负面身体形象有关。通过在两种常见的排练情境（即标记编舞和完全执行音乐）中的动作分析，我们研究了基于任务需求的动作可变性是如何不同的。我们的研究结果表明，在标记过程中失去控制的时刻更频繁，强调了排练环境对运动表现和身体相关自我感知的影响。此外，我们证明肘关节的运动不稳定可能与自我报告的身体形象和意识测量有关，从而加强了运动变异性和心理健康之间的联系。通过确定肘部在芭蕾练习中是一个敏感而可靠的不稳定性指标，我们的方法为全身运动学分析提供了一个轻量级的替代方案，支持实验室环境之外的实际应用。从人机交互（HCI）和普适计算（Ubicomp）的角度来看，这项工作为整合身体图像和运动行为指标的系统提供了设计见解。这些发现为旨在增强舞者和其他动作实践者的身体形象和提高动作精度的互动技术开辟了新的可能性。

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引用次数: 0

Transforming data clutter into cultural experiences: an evaluation framework for accessibility in cultural big data platforms 将数据杂乱转化为文化体验：文化大数据平台可及性评价框架

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-15 DOI: 10.1016/j.ijhcs.2025.103685

Qitong Zhou, Yinman Guo, Zixiang Fan, Tie Ji

The large-scale digitization of cultural heritage has enhanced public access to massive online resources; however, issues such as redundant data and inconsistent design standards across platforms often compromise user experience. This study investigates the accessibility factors of cultural big data platforms and aims to evaluate the digital user experience of platforms. Accessibility is explicitly defined here as the comprehensive ability of users to access, interpret, and utilize cultural data without barriers. A multi-stage mixed-method approach was adopted. First, a systematic literature review and bibliometric analysis were conducted to derive a preliminary indicator framework. Second, qualitative refinement of the framework was carried out using grounded theory based on interviews with three user groups (the public, researchers, and creative practitioners). Finally, the Delphi method and fuzzy AHP were employed for expert validation and weight calculation. Consequently, a weighted evaluation framework comprising four primary dimensions and twenty-two secondary indicators was established. The findings indicate that platform usability constitutes the foundation of accessibility, cultural data quality ensures reliability, cultural data presentation facilitates user engagement, and cultural data interaction deepens cultural understanding. Moreover, the study highlights the necessity of tailoring platforms to specific user needs and discusses strategies for enhancing the accessibility of digital cultural resources through interaction design. This research provides both theoretical insights and practical guidance for the design and optimization of cultural big data platforms.

大规模的文化遗产数字化为公众获取海量网络资源提供了便利；然而，跨平台的冗余数据和不一致的设计标准等问题往往会损害用户体验。本研究考察文化大数据平台的可达性因素，旨在评估平台的数字用户体验。可访问性在这里被明确地定义为用户无障碍地访问、解释和利用文化数据的综合能力。采用多阶段混合方法求解。首先，进行了系统的文献综述和文献计量分析，得出了初步的指标框架。其次，基于对三个用户群体（公众、研究人员和创意从业者）的访谈，使用扎根理论对框架进行定性改进。最后采用德尔菲法和模糊层次分析法进行专家验证和权重计算。据此，建立了包含4个主要维度和22个次要指标的加权评价框架。研究结果表明，平台可用性是可访问性的基础，文化数据质量保证了可靠性，文化数据呈现促进了用户参与，文化数据交互加深了文化理解。此外，该研究强调了定制平台以满足特定用户需求的必要性，并讨论了通过交互设计提高数字文化资源可及性的策略。本研究为文化大数据平台的设计与优化提供了理论洞见和实践指导。

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引用次数: 0

Evaluating the effect of co-speech gesture prediction on Human–Robot Interaction 评价协同语音手势预测在人机交互中的效果

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-13 DOI: 10.1016/j.ijhcs.2025.103674

Enrique Fernández-Rodicio, Juan José Gamboa-Montero, Marcos Maroto-Gómez, Álvaro Castro-González, Miguel A. Salichs

Robots are starting to be used in tasks involving human–robot interactions. For them to be efficient in these tasks, they must be seen as suitable interaction partners. One method to achieve this is to enable them to use proper verbal and non-verbal communication. Selecting non-verbal behaviours that appropriately complement the robot’s verbal messages is complex and requires roboticists to understand how each dimension of communication, as well as their combination, affects how the user perceives the message. In this work, we evaluated the effect of selecting appropriate non-verbal expressions given the robot’s speech on how users perceive the robot and its expressiveness. To do this, we conducted a within-subjects experiment where participants played cards with two robots — one that used a co-speech gesture prediction module for selecting its non-verbal expressions and another that used random expressions. The results showed that using the gestures predicted by our system improves the experience of participants during interactions. Specifically, participants perceived the robot using the co-speech gesture prediction module as having a higher level of agency, and as having a more coherent expressiveness.

机器人开始被用于涉及人机交互的任务。为了让他们有效地完成这些任务，他们必须被视为合适的互动伙伴。实现这一目标的一个方法是使他们能够使用适当的语言和非语言交流。选择适当补充机器人语言信息的非语言行为是复杂的，需要机器人专家了解交流的每个维度以及它们的组合如何影响用户对信息的感知。在这项工作中，我们评估了在给定机器人语音的情况下选择适当的非语言表达对用户如何感知机器人及其表达能力的影响。为了做到这一点，我们进行了一项受试者内部实验，参与者与两个机器人打牌——一个使用共同语音手势预测模块来选择非语言表达，另一个使用随机表达。结果表明，使用我们的系统预测的手势可以改善参与者在互动过程中的体验。具体来说，参与者认为使用协同语音手势预测模块的机器人具有更高水平的代理能力，并且具有更连贯的表达能力。

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引用次数: 0

Easy to handle: Exploring users’ interactions with an augmented reality human-machine interface for virtual stops in automated on-demand mobility scenarios 易于操作：探索用户与增强现实人机界面的交互，用于自动按需移动场景中的虚拟站点

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies

Pub Date : 2025-11-08 DOI: 10.1016/j.ijhcs.2025.103671

Fabian Hub , Marc Wilbrink , Michael Oehl

For the success of future shared automated mobility on-demand (SAMOD) a high quality of service needs to be assured. One key challenge lies in the passenger journey, particularly in finding the designated virtual stop (vStop) for pick-up and identifying the approaching shuttle. Therefore, the new vStop human-machine interface (vStop HMI) concept supports passengers with means of augmented reality (AR). This explorative user study with young adults (N = 44) used a virtual reality (VR) setup to simulate the AR-based vStop HMI and answers two research questions across two consecutive scenarios: (1) When using an AR-based navigation aid like the vStop HMI, users tend to focus heavily on their device while walking, which may increase the risk of negligent roadside behavior. While restricting AR information access during walking might reduce this risk, how does it affect overall user experience? To examine this, we compared a vStop HMI with restricted AR information access against a baseline condition. Results showed positive effects on user experience and acceptance for both HMI versions, although workload was partly negatively affected. (2) When identifying the approaching shuttle, does using the vStop HMI effectively assist users in doing so and lead to positive user experience? Usability testing indicated positive rates of workload, user experience, and acceptance during the shuttle identification task. Overall, the findings suggest that selectively restricting AR information during navigation tasks can help mitigate undesired user behavior in roadside environments while maintaining high levels of pragmatic quality and acceptance. Moreover, the vStop HMI enables seamless shuttle identification, even under complex conditions. By improving these critical stages of the passenger journey, the vStop HMI concept has the potential to enhance the overall quality of SAMOD services and increase public acceptance of future shared, automated, demand-responsive transportation systems.

未来共享自动按需出行（SAMOD）的成功需要保证高质量的服务。一个关键的挑战在于乘客的旅程，特别是找到指定的虚拟站点（vStop）接送和识别接近的班车。因此，新的vStop人机界面（vStop HMI）概念为乘客提供增强现实（AR）的手段。这项探索性的用户研究（N = 44）使用虚拟现实（VR）设置来模拟基于ar的vStop HMI，并在两个连续的场景中回答了两个研究问题：(1)当使用基于ar的导航辅助工具（如vStop HMI）时，用户倾向于在行走时过多地关注他们的设备，这可能会增加疏忽路边行为的风险。虽然在行走过程中限制AR信息访问可能会降低这种风险，但它如何影响整体用户体验？为了检验这一点，我们将限制AR信息访问的vStop HMI与基线条件进行了比较。结果显示，两个HMI版本对用户体验和接受度都有积极影响，尽管工作量部分受到负面影响。(2)在识别接近的班车时，使用vStop人机界面是否有效地帮助用户识别并带来积极的用户体验？可用性测试表明，在航天飞机识别任务期间，工作量、用户体验和接受度都是积极的。总的来说，研究结果表明，在导航任务中有选择地限制AR信息可以帮助减轻路边环境中不受欢迎的用户行为，同时保持高水平的实用质量和接受度。此外，即使在复杂的条件下，vStop HMI也能实现无缝穿梭识别。通过改善乘客旅程的这些关键阶段，vStop HMI概念有可能提高SAMOD服务的整体质量，并提高公众对未来共享、自动化、需求响应的交通系统的接受度。

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