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8th International Workshop on Chatbots and human-Centered AI, CONVERSATIONS 2024

作者： Chilufya, Emma Mainza Arvola, Mattias Severinsson, Susanne Bylund, Anna Martín Stenliden, Linnéa Mortazavi, Arezou Ziemke, Tom Human-Centered Systems Department of Computer and Information Science Linköping University Linköping Sweden Education Teaching and Learning Department of Behavioural Sciences and Learning Linköping University Linköping Sweden Learning Aesthetics Natural Science Department of Behavioural Sciences and Learning Linköping University Linköping Sweden

ISBN: (纸本)9783031880445

The motivation to read among children in Nordic countries has seen a noticeable decline in recent years. This study explores the design of social robots for stimulating interest in reading among fourth-grade students (age 10–11). We used a combination of conceptual design methods to engage teachers and students from four classes in two Swedish schools in co-creation workshops. Ideas on functions, qualities, and robot designs were generated, and based on this a set of ten distinct design concepts were created: the Facilitator, the Librarian, the Coach, the Buddy, the Assistant, the Narrator, the Creator, the Apprentice, the Portable, and the Gamer. The strengths and weaknesses of different designs were evaluated, resulting in a final design named ‘The BookBot,’ aiming to inspire and engage students in book reading through discussions of book content, character portrayal, and personalised book recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Social robots

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Ontologies in Design: How Imagining a Tree Reveals Possibilities and Assumptions in Large Language Models 25

Ontologies in Design: How Imagining a Tree Reveals Possibili...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Haghighi, Nava Yu, Sunny Landay, James A. Rosner, Daniela Computer Science Stanford University StanfordCA United States Symbolic Systems Program Stanford University StanfordCA United States Computer Science Department Stanford University StanfordCA United States Human Centered Design & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713941

Amid the recent uptake of Generative AI, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-based analyses are crucial, we argue that ontologies - concerning what we allow ourselves to think or talk about - is a vital but under-recognized dimension in analyzing these systems. Proposing a need for a practice-based engagement with ontologies, we offer four orientations for considering ontologies in design: pluralism, groundedness, liveliness, and enactment. We share examples of potentialities that are opened up through these orientations across the entire LLM development pipeline by conducting two ontological analyses: examining the responses of four LLM-based chatbots in a prompting exercise, and analyzing the architecture of an LLM-based agent simulation. We conclude by sharing opportunities and limitations of working with ontologies in the design and development of sociotechnical systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ontology

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Perceiving and Countering Hate: The Role of Identity in Online Responses

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Proceedings of the ACM on human-computer interaction 2025年第2期9卷 1-28页

作者： Ping, Kaike Hawdon, James Rho, Eugenia H Department of Computer Science Virginia Tech Blacksburg United States College of Liberal Arts and Human Sciences Virginia Tech Blacksburg United States

This study investigates how online counterspeech, defined as direct responses to harmful online content with the intention of dissuading the perpetrator from further engaging in such behavior, is influenced by the match between a target of the hate speech and a counterspeech writer's identity. Using a sample of 458 English-speaking adults who responded to online hate speech posts covering race, gender, religion, sexual orientation, and disability status, our research reveals that the match between a hate post's topic and a counter-speaker's identity (topic-identity match, or TIM) shapes perceptions of hatefulness and experiences with counterspeech writing. Specifically, TIM significantly increases the perceived hatefulness of posts related to race and sexual orientation. TIM generally boosts counter-speakers' satisfaction and perceived effectiveness of their responses, and reduces the difficulty of crafting them, with an exception of gender-focused hate speech. In addition, counterspeech that displayed more empathy, was longer, had a more positive tone, and was associated with higher ratings of effectiveness and perceptions of hatefulness. Prior experience with, and openness to AI writing assistance tools like ChatGPT, correlate negatively with perceived difficulty in writing online counterspeech. Overall, this study contributes insights into linguistic and identity-related factors shaping counterspeech on social media. The findings inform the development of supportive technologies and moderation strategies for promoting effective responses to online hate. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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What does ChatGPT know about natural science and engineering?

arXiv

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arXiv 2023年

作者： Balhorn, Lukas Schulze Weber, Jana M. Buijsman, Stefan Hildebrandt, Julian R. Ziefle, Martina Schweidtmann, Artur M. Delft University of Technology Delft Netherlands Human-Computer Interaction Center Chair of Communication Science RWTH Aachen University Aachen Germany

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT’s expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions from 198 faculty members across 5 faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are on average perceived as "mostly correct". Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the complexity level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude. Copyright © 2023, The Authors. All rights reserved.

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Shared Autonomy for Proximal Teaching

arXiv

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arXiv 2025年

作者： Srivastava, Megha Iranmanesh, Reihaneh Cui, Yuchen Gopinath, Deepak Sumner, Emily Silva, Andrew Dees, Laporsha Rosman, Guy Sadigh, Dorsa Computer Science Department Stanford University Stanford United States Computer Science Department Amherst College Amherst United States Computer Science Department University of California Los Angeles Los Angeles United States Human Interactive Driving Toyota Research Institute Cambridge United States

Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have leveraged AI-assistance to improve instruction of tasks ranging from rehabilitation to surgical robot tele-operation. However, these works often make simplifying assumptions on the student learning process, and fail to model how a teacher’s assistance interacts with different individuals’ abilities when determining optimal teaching strategies. Inspired by the idea of scaffolding from educational psychology, we leverage shared autonomy, a framework for combining user inputs with robot autonomy, to aid with curriculum design. Our key insight is that the way a student’s behavior improves in the presence of assistance from an autonomous agent can highlight which sub-skills might be most "learnable" for the student, or within their Zone of Proximal Development. We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills. In a user study (n=50), where we teach high performance racing in a simulated environment of the Thunderhill Raceway Park with the CARLA Autonomous Driving simulator, we show that Z-COACH helps identify which skills each student should first practice, leading to an overall improvement in driving time, behavior, and smoothness. Our work shows that increasingly available semi-autonomous capabilities (e.g. in vehicles, robots) can not only assist human users, but also help teach them. © 2025, CC BY.

关键词： Students

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The IoT Codex: A Book of Programmable Stickers for Authoring and Composing Embedded Computing Applications

The IoT Codex: A Book of Programmable Stickers for Authoring...

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IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Kristin Williams Jessica Hammer Scott Hudson Computer Science Emory University Atlanta USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh USA Entertainment Technology Center Carnegie Mellon University Pittsburgh USA

The Internet-of-Things (IoT) promises to enhance everyday objects with computing, but rarely enables directly authoring or composing that behavior. Lightweight IoT approaches attach identifiers (e.g., RFID tags) to objects to enable networked services. Typically these tags are passive, and so, depend on activity recognition and predefined context. This limits interaction to invoking predetermined behavior. Instead, this work presents The IoT Codex: a lightweight approach to customizing everyday objects with IoT by enabling interactive attachable IDs (aIDs) to compose software-supported behavior in situ. This work contributes 1) paper engineering techniques to construct aIDs that embody state, and 2) a tangible, end user programming (EUP) language for customizing IoT within symbolic and idiosyncratic contexts. Here, we provide preliminary validation of our approach with an empirically informed design space, sample applications, and a small co-design workshop. In doing so, we offer preliminary evidence for tangible, end user programming to enable meaningful control over IoT services.

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Automating Gaze Target Annotation in human-Robot interaction

Automating Gaze Target Annotation in Human-Robot Interaction

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Linlin Cheng Koen V. Hindriks Artem V. Belopolsky Faculty of Science Computer Science Vrije Universiteit Amsterdam Department of Human Movement Sciences Vrije Universiteit Amsterdam

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Identifying gaze targets in videos of human-robot interaction is useful for measuring engagement. In practice, this requires manually annotating for a fixed set of objects that a participant is looking at in a video, which is very time-consuming. To address this issue, we propose an annotation pipeline for automating this effort. In this work, we focus on videos in which the objects looked at do not move. As input for the proposed pipeline, we therefore only need to annotate object bounding boxes for the first frame of each video. The benefit, moreover, of manually annotating these frames is that we can also draw bounding boxes for objects outside of it, which enables estimating gaze targets in videos where not all objects are visible. A second issue that we address is that the models used for automating the pipeline annotate individual video frames. In practice, however, manual annotation is done at the event level for video segments instead of single frames. Therefore, we also introduce and investigate several variants of algorithms for aggregating frame-level to event-level annotations, which are used in the last step in our annotation *** compare two versions of our pipeline: one that uses a state-of-the-art gaze estimation model (GEM) and a second one using a state-of-the-art target detection model (TDM). Our results show that both versions successfully automate the annotation, but the GEM pipeline performs slightly (≈10%) better for videos where not all objects are visible. Analysis of our aggregation algorithm, moreover, shows that there is no need for manual video segmentation because a fixed time interval for segmentation yields very similar results. We conclude that the proposed pipeline can be used to automate almost all of the annotation effort.

关键词： Annotations Soft sensors Pipelines human-robot interaction Manuals Object detection Time division multiplexing Particle measurements Robots Videos

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Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions

arXiv

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arXiv 2024年

作者： Moore, Steven Schmucker, Robin Mitchell, Tom Stamper, John Human-Computer Interaction Carnegie Mellon University PittsburghPA United States Machine Learning Department Carnegie Mellon University PittsburghPA United States

Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowledge. To streamline this process for higher-education courses, we employed GPT-4 to generate KCs for multiple-choice questions (MCQs) in Chemistry and E-Learning. We analyzed discrepancies between the KCs generated by the Large Language Model (LLM) and those made by humans through evaluation from three domain experts in each subject area. This evaluation aimed to determine whether, in instances of non-matching KCs, evaluators showed a preference for the LLM-generated KCs over their human-created counterparts. We also developed an ontology induction algorithm to cluster questions that assess similar KCs based on their content. Our most effective LLM strategy accurately matched KCs for 56% of Chemistry and 35% of E-Learning MCQs, with even higher success when considering the top five KC suggestions. human evaluators favored LLM-generated KCs, choosing them over human-assigned ones approximately two-thirds of the time, a preference that was statistically significant across both domains. Our clustering algorithm successfully grouped questions by their underlying KCs without needing explicit labels or contextual information. This research advances the automation of KC generation and classification for assessment items, alleviating the need for student data or predefined KC labels. © 2024, CC BY.

关键词： E-learning

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Hu, Yongquan Gowen' Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Gfloyd' Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia CSIRO's Data61 Sydney Australia University of New South Wales Sydney Australia

ISBN: (纸本)9798400713941

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： human computer interaction

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Correction: Prompt4LJP: prompt learning for legal judgment prediction

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The Journal of Supercomputing 2025年第5期81卷 1-2页

作者： Huang, Qiongyan Xia, Yuhan Long, Yunfei Fang, Hui Liang, Ruiwei Guan, Yin Xu, Ge College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computer and Big Data Minjiang University Fuzhou China Fujian Mental Health Human-Computer Interaction Technology Research Center Minjiang University Fuzhou China Fuzhou Institute of Oceanography Fuzhou China

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