Promptiverse: Scalable Generation of Scaffolding Prompts Through Human-AI Hybrid Knowledge Graph Annotation
Yoonjoo Lee, John Joon Young Chung, Tae Soo Kim, Jean Y. Song, Juho Kim
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext fb1cd4d8-e83a-48c1-a483-4d97238d7dfbCited by top-tier papers10
- Enabling Conversational Interaction with Mobile UI using Large Language ModelsBryan Wang, Gang Li, Yang LiCHI 2023 · 149 citations
- Cells, Generators, and Lenses: Design Framework for Object-Oriented Interaction with Large Language ModelsTae Soo Kim, Yoonjoo Lee, Minsuk Chang, Juho KimUIST 2023 · 55 citations
- KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationYoufu Yan, Yu Hou, Yongkang Xiao, Rui Zhang et al.IEEE VIS 2024 · 33 citations
- Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLMZhe Liu, Chunyang Chen, Junjie Wang, Mengzhuo Chen et al.CHI 2024 · 29 citations
- VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture VideosSeulgi Choi, Hyewon Lee, Yoonjoo Lee, Juho KimCHI 2024 · 16 citations
Builds on8
- AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated PromptsTaylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace et al.EMNLP 2020 · 1,162 citations
- Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational AgentRainer Winkler, Sebastian Hobert, Antti Salovaara, Matthias Söllner et al.CHI 2020 · 176 citations
- The Power of Scale for Parameter-Efficient Prompt TuningBrian Lester, Rami Al-Rfou, Noah ConstantEMNLP 2021 · 94 citations
- texSketch: Active Diagramming through Pen-and-Ink AnnotationsHariharan Subramonyam, Colleen M. Seifert, Priti Shah, Eytan AdarCHI 2020 · 43 citations
- From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource DomainsJan-Christoph Klie, Richard Eckart de Castilho, Iryna GurevychACL 2020 · 42 citations
Related papers
- PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content CreationMohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. PardosCHI 2025 · 18 citations
- All in One: Multi-Task Prompting for Graph Neural NetworksXiangguo Sun, Hong Cheng, Jia Li, Bo Liu et al.KDD 2023 · 149 citations
- NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video UnderstandingRunning Zhao, Zhihan Jiang, Xinchen Zhang, Chirui Chang et al.UIST 2025 · 6 citations
- Large-Scale Pre-Training for Grounded Video Caption GenerationEvangelos Kazakos, Cordelia Schmid, Josef SivicICCV 2025
- Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language ModelsLiangchen Liu, Nannan Wang, Xi Yang, Xinbo Gao et al.ICML 2025
