Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912805449367552 |
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| author | Keelawat, Panayu Barron, David Narasimhan, Kaushik Manesh, Daniel Tang, Xiaohang Chen, Xi Lee, Sang Won Chen, Yan |
| author_facet | Keelawat, Panayu Barron, David Narasimhan, Kaushik Manesh, Daniel Tang, Xiaohang Chen, Xi Lee, Sang Won Chen, Yan |
| contents | Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlight frequently discussed themes and surface underrepresented viewpoints, making accurate representations of insight occurrence essential. Yet authoring presentations in real time is cognitively overwhelming due to the volume of data and tight time constraints. We present Dynamite, an AI-assisted system that enables semantic updates to instructor-authored slides during live classroom discussions. These updates are powered by semantic data binding, which links slide content to evolving discussion data, and semantic suggestions, which offer revision options aligned with pedagogical goals. In a within-subject in-lab study with 12 participants, Dynamite outperformed a text-based AI baseline in content accuracy and quality. Participants used voice and sketch input to quickly organize semantic blocks, then applied suggestions to accelerate refinement as data stabilized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction Keelawat, Panayu Barron, David Narasimhan, Kaushik Manesh, Daniel Tang, Xiaohang Chen, Xi Lee, Sang Won Chen, Yan Human-Computer Interaction Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlight frequently discussed themes and surface underrepresented viewpoints, making accurate representations of insight occurrence essential. Yet authoring presentations in real time is cognitively overwhelming due to the volume of data and tight time constraints. We present Dynamite, an AI-assisted system that enables semantic updates to instructor-authored slides during live classroom discussions. These updates are powered by semantic data binding, which links slide content to evolving discussion data, and semantic suggestions, which offer revision options aligned with pedagogical goals. In a within-subject in-lab study with 12 participants, Dynamite outperformed a text-based AI baseline in content accuracy and quality. Participants used voice and sketch input to quickly organize semantic blocks, then applied suggestions to accelerate refinement as data stabilized. |
| title | Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.20137 |