Colin: A Multimodal Human-AI Co-Creation Storytelling System To Support Children's Multi-Level Narrative Skills
May 10, 2024 Β· Declared Dead Β· π CHI Extended Abstracts
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lyumanshan Ye, Jiandong Jiang, Yuhan Liu, Yihan Ran, Yufan Zhou, Zhao Wang, Yipeng Yu, Pengfei Liu, Danni Chang, Yucheng Jin
arXiv ID
2405.06495
Category
cs.HC: Human-Computer Interaction
Citations
10
Venue
CHI Extended Abstracts
Last Checked
4 months ago
Abstract
Children develop narrative skills by understanding and actively building connections between elements, image text matching, and consequences. However, it is challenging for children to clearly grasp these multi level links only through explanations of text or the facilitator's speech. To address this, we developed Colin, an interactive storytelling tool that supports children's multi level narrative skills through both voice and visual modalities. In the generation stage, Colin supports the facilitator to define and review the generated text and image content freely. In the understanding stage, a question feedback model helps children understand multi level connections while co creating stories with Colin. In the building phase, Colin actively encourages children to create connections between elements through drawing and speaking. A user study with 20 participants evaluated Colin by measuring children's engagement, understanding of cause and effect relationships, and the quality of their new story creations. Our results demonstrate that Colin significantly enhances the development of children's narrative skills across multiple levels.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted