lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Students in online education struggle to actively ask questions and explore knowledge from video-based learning.Data Annotation, Model Debugging, and Data Work / Contextual Example Selection and Rare Pattern Coverage
Students in online education struggle to actively ask questions and explore knowledge from video-based learning.
Similar questions
lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Children lack suitable AI learning tools to effectively master AI fundamentals.lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Users often overgeneralize complex data due to confirmation bias, leading to suboptimal decisions.lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Data slicing and model optimization depend on metadata, making operations complex and costly.lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
WiFi activity recognition struggles to adapt across environments and individuals and requires large amounts of labeled data.lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Users frequently decline proactive learning tasks from smart speakers due to poor timing.lightbulbPractical problemContextual Example Selection and Rare Pattern Coverage
Users struggle to cover rare patterns when selecting in-context examples, leading to unstable model performance.IUI '23ScatterShot: Interactive In-context Example Curation for Text Transformation
Related papers
CHI 2025
Technologies for Children’s AI Learning: Design Features and Future Opportunities
Kaiyue Jia, Junnan Yu
CHI 2025
From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant
Anna Bodonhelyi, Enkeleda Thaqi, Süleyman Özdel
CHI 2025
Confirmation Bias: The Double-Edged Sword of Data Facts in Visual Data Communication
Shiyao Li, Thomas James Davidson, Cindy Xiong Bearfield
IUI 2024
Slicing, Chatting, and Refining: A Concept-Based Approach for Machine Learning Model Validation with ConceptSlicer
Xiaoyu Zhang, Jorge H Piazentin Ono, Wenbin He
UbiComp 2024
MetaFormer: Domain-Adaptive WiFi Sensing with Only One Labelled Target Sample
Biyun Sheng, Rui Han, Fu Xiao
CHI 2024
Interrupting for Microlearning: Understanding Perceptions and Interruptibility of Proactive Conversational Microlearning Services
Minyeong Kim, Jiwook Lee, Youngji Koh
UbiComp 2023
AMIR: Active Multimodal Interaction Recognition from Video and Network Traffic in Connected Environments
Shinan Liu, Tarun Mangla, Ted Shaowang
IUI 2022
A Dialogue-Based Interface for Active Learning of Activities of Daily Living
Ronnie Smith, Mauro Dragone