Examining the Impact of Social Relationships on the Semantic Tones of Large Language Model-Generated Comments
Large language models can generate comments that are similar in semantic tone to human-generated comments, even without being explicitly provided with social relationship information. However, explicitly including social context in the prompt does not consistently improve the similarity between human and generated comments.
청각장애 학습자를 위한 감정 기술 포용: 동영상 기반 학습에서의 요구 해결
청각장애 학습자들은 자신의 감정을 표현하고 동료 학습자의 감정을 이해하는 데 어려움을 겪으며, 이를 지원할 수 있는 기술이 필요하다.
Leveraging Activity Theory to Explore Interdisciplinary Collaboration Between Speech-Language Pathologists and Natural Language Processing Researchers in Clinical Projects
Interdisciplinary collaboration between speech-language pathologists and natural language processing researchers faces significant challenges due to knowledge boundaries and terminological barriers, which can be addressed through the use of boundary objects and knowledge brokers.
Factors Influencing First-Time Online Instructors' Expectations of Success During the COVID-19 Pandemic
Instructors' self-efficacy in online teaching significantly impacts their expectations of success when transitioning to online instruction, while class size also plays a role.
Analyzing Student Interactions with ChatGPT to Enhance Data Visualization Learning
The integration of ChatGPT into data visualization education can revolutionize student learning experiences, but understanding students' interaction patterns with ChatGPT is crucial for instructors to harness its full potential.
AI Suggestions Diminish Cultural Nuances and Homogenize Writing Toward Western Styles
AI writing suggestions homogenize the writing styles of non-Western users toward Western norms, diminishing nuances that differentiate cultural expression.
CataractBot: An Expert-Verified, Multilingual Chatbot to Address Cataract Patients' Information Needs
CataractBot, an LLM-powered chatbot, provides reliable, expert-verified information to cataract patients and their attendants in real-time, addressing their diverse information needs while minimizing the burden on healthcare providers.
Analyzing Learner Behavior Patterns in Immersive Learning Environments: A Systematic Literature Review and Research Agenda
Immersive technologies have increased research interest in analyzing the specific behavioral patterns of learners in immersive learning environments. This study develops a conceptual framework to identify learning benefits and potential hurdles, and conducts a systematic review to consolidate factors of behavioral analysis in immersive contexts.
Enhancing Transparency in Conversational Information-Seeking Systems through Explanations of Source, Confidence, and Limitations
Providing explanations about the source, system confidence, and potential limitations of conversational responses can enhance the transparency and usefulness of the information provided to users.
Enhancing Transparency in Automated Speech Recognition: ConFides, a Visual Analytics Solution for Transcription Analysis and Exploration
ConFides, a visual analytics system, aims to enhance understanding of speech-to-text results by visually representing the confidence associated with the transcription, enabling exploration and post-AI-transcription editing.
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