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arXiv 2023年

作者： Schmucker, Robin Xia, Meng Azaria, Amos Mitchell, Tom Machine Learning Department Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science Ariel University

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the time and cost required to author CTS content is a major obstacle to widespread adoption. In this paper, we introduce a novel type of CTS that leverages the recent advances in large language models (LLMs) in two ways: First, the system induces a tutoring script automatically from a lesson text. Second, the system automates the script orchestration via two LLM-based agents (Ruffle&Riley) with the roles of a student and a professor in a learning-by-teaching format. The system allows a free-form conversation that follows the ITS-typical inner and outer loop structure. In an initial between-subject online user study (N = 100) comparing Ruffle&Riley to simpler QA chatbots and reading activity, we found no significant differences in post-test scores. Nonetheless, in the learning experience survey, Ruffle&Riley users expressed higher ratings of understanding and remembering and further perceived the offered support as more helpful and the conversation as coherent. Our study provides insights for a new generation of scalable CTS technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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"it Might be Technically Impressive, but It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry 25

"it Might be Technically Impressive, but It's Practically Us...

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

作者： Xiao, Qing Fan, Xianzhe Simon, Felix Marvin Zhang, Bingbing Eslami, Motahhare Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Tsinghua University Beijing China Reuters Institute for the Study of Journalism University of Oxford Oxford United Kingdom School of Journalism and Mass Communication University of Iowa Iowa CityIA United States School of Computer Science Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. Although prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how internal cross-functional collaboration around AI unfolds between AI professionals and journalists within the news industry. Through interviews with 17 journalists, six AI technologists, and three AI workers with cross-functional experience from leading Chinese news organizations, we investigate the practices, challenges, and opportunities for internal cross-functional collaboration around AI in news industry. We first study how these journalists and AI professionals perceive existing internal cross-collaboration strategies. We explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry. © 2025 Copyright held by the owner/author(s).

关键词： Motivation

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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Opportunities for Serious Game Technologies to Engage Children with Autism in a Pakistani Sociocultural and Institutional Context: An investigation of the design space for serious game technologies to enhance engagement of children with autism and to facilitate external support provided 22

Opportunities for Serious Game Technologies to Engage Childr...

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Proceedings of the 34th Australian Conference on human-computer interaction

作者： Sara Muneeb Laurianne Sitbon Faizan Ahmad Research Center for Human Computer Interaction COMSATS University Islamabad Lahore Campus Pakistan and School of Computer Science Queensland University of Technology (QUT) Australia School of Computer Science Queensland University of Technology (QUT) Australia Department of Computer Science School of Technologies Cardiff Metropolitan University United Kingdom

ISBN: (纸本)9798400700248

Usability and design of serious game technologies have been widely investigated as academic interventions tools for children with autism. However, besides the technology, the sociocultural and institutional context also impacts the gameplay activities of children with autism as well as the external support provided by instructors. Therefore, this paper investigates i) the impact of sociocultural and institutional context over the engagement of children with autism with serious game technologies, ii) the types of external support children with autism are offered or require during gameplay session, and iii) the type of rewards and encouragers that can act as unbiased support to increase the engagement of the children with autism during *** paper investigates these factors with an inductive analysis of educational gameplay activities of 23 children with autism in Lahore, Pakistan. Our study found that sociocultural and institutional context has substantial impact over the gameplay activities of children with autism. Children required both physical and verbal support during the gameplay activities, and institutional context influences the behavior of instructors as well. Finally, dynamic animations, rewards and encouragers can keep the children engaged for longer in the gameplay.

关键词： serious game technologies children with autism Game-based learning support context and encouragers rewards

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Enhancing the De-identification of Personally Identifiable Information in Educational Data

arXiv

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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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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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Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study

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JMIR Formative Research 2024年 8卷 e59623页

作者： Cho, Minseo Park, Doeun Choo, Myounglee Kim, Jinwoo Han, Doug Hyun Human Computer Interaction Lab Department of Cognitive Science Yonsei University Seoul South Korea Human Computer Interaction Lab School of Business Yonsei University Seoul South Korea HAII Corp Seoul South Korea College of Medicine Chung-Ang University Seoul South Korea

Background: The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. Objective: This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. Methods: In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. Results: The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents’ lives, with surveys scheduled at convenient times and supportive messages tailored to their dai

关键词： adolescent mental health adolescents digital phenotyping ecological momentary assessment phenotype data phenotypes proof of concept qualitative research self-monitoring smartphone apps

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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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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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Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

arXiv

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arXiv 2025年

作者： Kondo, Yui Dunnell, Kevin Xiao, Qing Zhao, Jun Rocher, Luc Oxford Internet Institute University of Oxford United Kingdom MIT Media Lab Massachusetts Institute of Technology United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science University of Oxford United Kingdom

Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals’ behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces ‘hypothetical inference’—a novel approach that uses language models to simulate how algorithms might interpret users’ digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Video analysis

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