BunCho: AI Supported Story Co-Creation via Unsupervised Multitask Learning to Increase Writers’ Creativity in Japanese

Osone, Hiroyuki; Lu, Junli; Ochiai, Yoichi

doi:10.1145/3411763.3450391

Cited by 41 publications

(10 citation statements)

References 24 publications

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“…First, we recognized the importance of providing a structured narrative framework to help children clarify the foundational elements of their projects [25]. Second, it is crucial to provide stimuli that can ignite creative thinking in children, aiding them in the detailing and refinement of their stories; such stimuli can help children overcome artist's block [44,71]. By providing this support, we provide them with a clear reference point, thereby enabling them to systematically build upon their initial ideas and ensuring a cohesive development of their projects.…”

Section: 21mentioning

confidence: 99%

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

Chen,

Xiao,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children. CCS CONCEPTS• Social and professional topics → Computational thinking; K-12 education; • Human-centered computing → Interactive systems and tools.

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Section: 21mentioning

confidence: 99%

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

Chen,

Xiao,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…In the creative context, the focus lies on imagination, artistic expression, and original storytelling. An intelligent writing assistant within this context can provide support for diverse creative endeavors, including writing lyrics [257], crafting metaphors [84], and offering inspiration and assistance for storytelling [187,220]. The journalistic context centers around factual reporting, news coverage, and effective communication of information to the public.…”

Section: Planningmentioning

confidence: 99%

A Design Space for Intelligent and Interactive Writing Assistants

Lee,

Gero,

Chung

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a large community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants.

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“…The specific guidance that a human can provide is varied and depends on their ML expertise. Model developers can adjust model parameters such as neural attention Passali et al, 2021); Semantic prompts (e.g., keywords, topic tags, and written prompts): (Pouliquen et al, 2011;Ghazvininejad et al, 2017;Clark et al, 2018;Fan et al, 2018;Zarinbal et al, 2019;He et al, 2020;Osone et al, 2021;Chang et al, 2021;Strobelt et al, 2021); Style specification (e.g., sentiment slider, length of text): (Ghazvininejad et al, 2017;Freiknecht and Effelsberg, 2020); Modification on input (e.g., select/deselect chunks of input text): (Pouliquen et al, 2011;Zhang et al, 2011;Gehrmann et al, 2019) Selecting or Rating Model Output Selecting (or rating)…”

Section: Findings: Five Human-ai Interactions In Text Generationmentioning

confidence: 99%

Mapping the Design Space of Human-AI Interaction in Text Summarization

Cheng¹,

Smith-Renner²,

Zhang³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans' roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks. We first conducted a systematic literature review of 70 papers, developing a taxonomy of five interactions in AI-assisted text generation and relevant design dimensions. We designed text summarization prototypes for each interaction. We then interviewed 16 users, aided by the prototypes, to understand their expectations, experience, and needs regarding efficiency, control, and trust with AI in text summarization and propose design considerations accordingly.

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BunCho: AI Supported Story Co-Creation via Unsupervised Multitask Learning to Increase Writers’ Creativity in Japanese

Cited by 41 publications

References 24 publications

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

A Design Space for Intelligent and Interactive Writing Assistants

Mapping the Design Space of Human-AI Interaction in Text Summarization

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