Promptiverse: Scalable Generation of Scaffolding Prompts Through Human-AI Hybrid Knowledge Graph Annotation

要旨

Online learners are hugely diverse with varying prior knowledge, but most instructional videos online are created to be one-size-fits-all. Thus, learners may struggle to understand the content by only watching the videos. Providing scaffolding prompts can help learners overcome these struggles through questions and hints that relate different concepts in the videos and elicit meaningful learning. However, serving diverse learners would require a spectrum of scaffolding prompts, which incurs high authoring effort. In this work, we introduce Promptiverse, an approach for generating diverse, multi-turn scaffolding prompts at scale, powered by numerous traversal paths over knowledge graphs. To facilitate the construction of the knowledge graphs, we propose a hybrid human-AI annotation tool, Grannotate. In our study (N=24), participants produced 40 times more on-par quality prompts with higher diversity, through Promptiverse and Grannotate, compared to hand-designed prompts. Promptiverse presents a model for creating diverse and adaptive learning experiences online.

著者
Yoonjoo Lee
KAIST, Daejeon, Korea, Republic of
John Joon Young. Chung
University of Michigan, Ann Arbor, Michigan, United States
Tae Soo Kim
KAIST, Daejeon, Korea, Republic of
Jean Y. Song
DGIST, Daegu, Korea, Republic of
Juho Kim
KAIST, Daejeon, Korea, Republic of
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502087

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Interacting with Data

291
5 件の発表
2022-05-03 18:00:00
2022-05-03 19:15:00