# StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

arXiv: https://arxiv.org/abs/2202.06205 · HTML: https://mlsanity.com/paper/2202.06205/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 93 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems](https://mlsanity.com/paper/2010.06028/) (2020)
- [Question Answering and Question Generation as Dual Tasks](https://mlsanity.com/paper/1706.02027/) (2017)
- [Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations](https://mlsanity.com/paper/1909.00031/) (2019)
- [A Joint Model for Question Answering and Question Generation](https://mlsanity.com/paper/1706.01450/) (2017)
- [Conversational User Interfaces As Assistive interlocutors For Young Children's Bilingual Language Acquisition](https://mlsanity.com/paper/2103.09228/) (2021)

## Built on by (most cited first, 30 of 37)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing](https://mlsanity.com/paper/2402.10294/) (2024)
- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making](https://mlsanity.com/paper/2401.07058/) (2024)
- [Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank](https://mlsanity.com/paper/2306.08847/) (2023)
- [Storyfier: Exploring Vocabulary Learning Support with Text Generation Models](https://mlsanity.com/paper/2308.03864/) (2023)
- [Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review](https://mlsanity.com/paper/2312.12751/) (2023)
- [ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12](https://mlsanity.com/paper/2402.04975/) (2024)
- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Scratch Copilot Evaluation: Assessing AI-Assisted Creative Coding for Families](https://mlsanity.com/paper/2305.10417/) (2023)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples](https://mlsanity.com/paper/2404.15974/) (2024)
- ["I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support](https://mlsanity.com/paper/2405.13803/) (2024)
- [StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning](https://mlsanity.com/paper/2311.09756/) (2023)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales](https://mlsanity.com/paper/2305.16641/) (2023)
- ["When He Feels Cold, He Goes to the Seahorse"-Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy](https://mlsanity.com/paper/2402.06472/) (2024)
- [Engaging with Children's Artwork in Mixed Visual-Ability Families](https://mlsanity.com/paper/2407.18874/) (2024)
- [Challenges and Opportunities for the Design of Smart Speakers](https://mlsanity.com/paper/2306.05741/) (2023)
- [Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs](https://mlsanity.com/paper/2309.11478/) (2023)
- [PlayWrite: A Multimodal System for AI Supported Narrative Co-Authoring Through Play in XR](https://mlsanity.com/paper/2603.02366/) (2026)
- [More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice](https://mlsanity.com/paper/2603.22152/) (2026)
- [Designing a realistic peer-like embodied conversational agent for supporting children's storytelling](https://mlsanity.com/paper/2304.09399/) (2023)
- [Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home](https://mlsanity.com/paper/2306.00482/) (2023)
- [Learning Nonverbal Cues in Multiparty Social Interactions for Robotic Facilitators](https://mlsanity.com/paper/2501.10857/) (2025)
- [Diffusion-Based Imitation Learning for Social Pose Generation](https://mlsanity.com/paper/2501.10869/) (2025)

## Abstract (arXiv)

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy's design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress. A user study validated StoryBuddy's usability and suggested design insights for future parent-AI collaboration systems.