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arXiv 2025年

作者： Shen, Yuntian Ji, Zilyu Lin, Jionghao Koedinger, Kenneth R. Carnegie Mellon University PittsburghPA15217 United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Faculty of Information Technology Monash University ClaytonVIC3800 Australia Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University PittsburghPA15217 United States

Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4omini’s performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data’s utility for research and pedagogical analysis. Our code is available on GitHub: https://***/AnonJD/PrivacyAI © 2025, CC BY.

关键词： Differential privacy

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The BookBot Project: Conceptual Design of a Social Robot Facilitating Reading Motivation 8th

The BookBot Project: Conceptual Design of a Social Robot F...

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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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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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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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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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Emotion Detection by Image Analysis for Painter 26

Emotion Detection by Image Analysis for Painter

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26th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2022

作者： Wu, Yung-Gi Yang, Meng-Hua Chen, Wen-Jing Lin, Zi-Yu Wu, Ke-Shang Department of Computer Science & Information Engineering Chang Jung Christian University Tainan City711 Taiwan Department of Interaction Design Chang Jung Christian University Tainan City711 Taiwan

ISBN: (纸本)9781950492640

Painting is an expression of visual psychological intentions, which presents inner thoughts and emotions to the outside. This expression and the displayed picture works have an initial diagnostic function and can be used as one of the emotional indicators of the painter. Because traditional interviews are often limited to the use of speech, or limited by cognitive functions;in contrast, painting does not depend on language, nor is it limited by age and education level, language loss, intelligence inadequate or children can do it, and they can express their thoughts through creation. Understanding psychological emotions are the beginning of mental therapy. The goal of this research is to use artificial intelligence and deep learning technologies to develop a system that can detect the psychological state of the painter. This paper collects pictures of those with weak communication skills and through image analysis methods captures color, shape, composition, pattern, and other information, with the help of psychologists and art professional professors for deep learning and recognition. In our system, people can detect emotions through the pictures drawn. If they have been in negative emotions frequently, they can seek medical assistance. We hope to help more people to be aware of their emotional status in an easy way. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Image analysis

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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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