Abstract
With the digital revolution of artificial intelligence (AI) in language education, the way how people write and create stories has been transformed in recent years. Although recent studies have started to examine the roles of AI in literacy, there is a lack of systematic review to inform how it has been applied and what has been achieved in story-writing. This paper reviews the literature on the use of AI in story-writing during the last 5 years. The discussion explores the year of publication, countries of implementation, educational levels, participants and research methodology. In terms of research context, most studies were carried out in universities in the United States, and children and adult learners were the two most common participants. Most studies involved the collection and analysis of quantitative data. After that, the mechanisms of using AI for story-writing are investigated in terms of the types, approaches, and roles of AI. The pedagogies used in the learning context of AI-supported story-writing are discussed. Finally, the benefits of using AI in story-writing are pointed out. The findings show that the literature has paid most attention to learners’ creativity, writing skills, presentation skills, motivation, and satisfaction. The review also suggested that human-AI collaboration could effectively improve story creation. Some studies had trained high-level AI to help students write better stories. As findings from the current body of research are not conclusive, more work is needed in exploring challenges of using AI in story-writing. Lastly, a set of limitations and recommendations for future research are summarized in this study.
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Appendix. Reviewed studies and information
Appendix. Reviewed studies and information
SN | Author(s) and year of publication | Type of publication | Country | Educational level | Participant/Research object | Methodology | Type of AI technologies | Approach of AI technologies | Role of AI technologies | Pedagogy | Benefit |
|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Biermann et al. (2022) | Empirical study | Canada | Adult education | 20 adults (7 hobbyists and 13 professional writers) | Qualitative | AI-writers | Modern large-scale language models | Story collaborator | Human-AI collaborative writing | Help writers have good productivity and complete challenging writing tasks |
2 | Cambre et al. (2020) | Empirical study | USA | Adult education | 149 adults | Mixed methods | Voice assistant | No mention | No mention | No mention | No mention |
3 | Cheong et al. (2018) | Descriptive study | Korea | Not applicable | Not applicable | Not applicable | AI planner | The unity game engine | Story animator | Not applicable | Generate the story with humans and then visualize it as a 3D animation Encourage the user to interact with the story by manipulating props or characters |
4 | Chung et al. (2022) | Empirical study | USA | Adult education | 14 adults (7 female and 7 male) | Qualitative | TaleBrush | GPT-Neo | Story generator | Human-AI collaborative writing | Collaborate with writers to generate stories based on their intentions about characters’ fortune Maintain the novelty of generated sentences Inspire writers’ ideas when the AI application generates stories |
5 | Clark et al. (2018) | Empirical study | USA | Adult education | 36 adults | Mixed methods | Machine in the loop systems | A neural language model | Story co-creator | Human-AI collaborative writing | Generate suggestions based on writers’ story context |
6 | Coenen et al. (2021) | Descriptive study | USA | Not applicable | Not applicable | Not applicable | Wordcraft (an AI-assisted editor) | Neural language generation | Story collaborator | Not applicable | Collaborate with writers to complete a story |
7 | Gala et al. (2021) | Empirical study | India | Adult education | No mention | Quantitative | An encoder-decoder architecture | Recurrent neural networks | Story generator | No mention | Generate grammatically correct and sensible stories automatically based on images |
8 | Goldfarb-Tarrant et al. (2019) | Empirical study | USA | Adult education | 30 workers | Quantitative | A neural narrative generation system | A conditional language models implemented with LSTMs | Story co-creator | Human-AI collaborative writing | Interact with humans to generate stories |
9 | Hsu et al. (2019) | Empirical study | USA | Adult education | 197 workers | Quantitative | State-of-the-art visual storytelling models | Human-centered computer (supported storytelling system) | Story collaborator | Human-AI collaborative writing | Assist writers in generating machine-generated visual story based on the visual storytelling model using the VIST dataset (with photos) Collaborate with users to generate good quality and understandable stories by reducing word redundancy and increasing lexical diversity |
10 | Issa and Jusoh (2019) | Descriptive study | Jordan | Not applicable | Not applicable | Not applicable | Markov chain model (a model that hires statistics in determining a sequence of elements according to certain rules or history) | Natural language generation | Story generator | Not applicable | Generate educational stories automatically based on users setting characters |
11 | Karlimah et al. (2021) | Empirical study | Indonesia | Primary education | 25 primary students | Quantitative | Motion comic storyboard | Drawing applications, animation applications, and project export applications | Storyteller | Technology-mediated story creation | Improve primary students’ math skills by developing a motion comic prototype about fractions |
12 | Lee et al. (2022) | Empirical study | USA | Adult education | 63 writers | Quantitative | CoAuthor | GPT-3 | Story collaborator | Human-AI collaborative writing | Collaborate with writers to generate stories Enhance writers’ story language, ideation, and collaboration capabilities |
13 | Min et al. (2021) | Descriptive study | Korea | Not applicable | Stories from two datasets | Not applicable | Visual story writer model | Recurrent neural network structure and encoder-decoder model | Story generator | Not applicable | Generate several captions describing story contents based on the input images. These captions are then used to create a sequence of sentences to make a short story as the output |
14 | Nichols et al. (2021) | Empirical study | Canada | Higher education | 122 adults (workers and university students) | Mixed methods | AI agent | A large-scale neural language model | Story collaborator | Human-AI collaborative writing | Assist writers in generating sufficiently human-like utterances and propose a sample-and-rank approach to improve utterance quality |
15 | Noceti et al. (2020) | Empirical study | Italy | Preschool and primary education | Over 1000 participants (parents and children aged 5–8) | Qualitative | Triangle coding | Triangle language | Story generator | Human-AI collaborative writing | Help an interpretation of the shapes sequence and the generation of a fantasy sentence or a small story |
16 | Osone et al. (2021) | Empirical study | Japan | Adult education | 48 adults (16 writers and 32 readers) | Quantitative | BunCho (an AI supported story co-creation system) | GPT-2 | Story co-creator | Human-AI collaborative writing | Assist Japanese novelists in creating high-level and creative writing, enhance affective-enjoyed writing synopses (creativity, interestingness, comprehensibility, grammatical correctness, consistency of sentences), improve common metrics-creativity, and broadened their stories |
17 | Park and Park (2018) | Descriptive study | Korea | Not applicable | Not applicable | Not applicable | Intelligent narrative story creation systems | Unity game engine | Story co-creator | Not applicable | Enhance the satisfaction of users through VR in storytelling activities |
18 | Peng et al. (2018) | Empirical study | USA | No mention | 98,162 stories | Quantitative | An analyze-to-generate story framework | A conditional language model | Story generator | No mention | Generate stories based on control factors extracted from existing stories to reflect a user’s intent Provide a good interaction environment for users |
19 | Roemmele et al. (2018) | Empirical study | USA | Adult education | 139 adults | Quantitative | Creative Help | Recurrent neural network language model (RNN LM) | Story collaborator | Human-AI collaborative writing | Assist writers in generating more grammatical and coherent sentences, writing the story easier, influenced its content more, and were more helpful overall. The authors made significantly fewer changes to the sentence suggestions |
20 | Shakeri et al. (2021) | Empirical study | Canada | Adult education | 2 adults | Qualitative | SAGA (an asynchronous collaborative storytelling system) | GPT-3 | Story collaborator | Human-AI collaborative writing | Collaborate with users to generate stories based on their prompt in terms of the basis of the story, and can include things like the setting, genre of the story, and even descriptions of the characters |
21 | Suh and An (2022) | Descriptive study | Canada | Not applicable | Not applicable | Not applicable | CodeToon (generative conversational AI) | GPT-3 | Story co-creator | Not applicable | Assist students’ learning, creative, and sensemaking process in a visual programming environment where users can create comics from code Encourage out-of-the-box ideas and motivate users to participate actively in this co-creative process |
22 | Wicke and Veale (2021) | Descriptive study | Ireland | Adult education | 2 robot and 1 human | Not applicable | A multi-modal storytelling system | Scéalability storytelling framework | Story generator | Not applicable | Collaborate with robots to develop stories based on users’ emotions and gestures |
23 | Xu et al. (2020) | Empirical study | China | No mention | 98,161 stories | Mixed methods | MEGATRON-CNTRL (a novel framework) | Large-scale language models | Story generator | No mention | Assist writers in generating more fluent, consistent, and coherent stories with less repetition and higher diversity |
24 | Yuan et al. (2022) | Empirical study | USA | Adult education | 25 hobbyist writers | Mixed methods | Wordcraft (an AI-assisted editor) | A generative language model | Story co-creator | Human-AI collaborative writing | Respond to writers’ custom requests via open-ended conversation and express them in natural language Generate suggestions for writers in the creative process |
25 | Zhang (2020) | Descriptive study | China | Not applicable | Not applicable | Not applicable | An application of AI Interactive storytelling in Animation | UnrealTM game engine | Story animator | Not applicable | Use 3D animation system to present storyline setting by characteristics and scenario development |
26 | Zhang et al. (2021) | Empirical study | China | Preschool and primary education | 10 children aged 5–10 | Mixed methods | StoryDrawer | A co-creative agent | Story co-creator | Human-AI collaborative writing | Assist children’s oral and drawing skills |
27 | Zhang et al. (2022) | Empirical study | China | Preschool and primary education | 24 participants (12 parents and 12 Children aged 6–10) | Qualitative | StoryDrawer | A context-based voice agent and two AI-driven collaborative strategies | Story collaborator | Human-AI collaborative writing | Inspire participants’ creative, elaborate ideas, and contribute to their creative outcomes during an engaging visual storytelling experience |
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Fang, X., Ng, D.T.K., Leung, J.K.L. et al. A systematic review of artificial intelligence technologies used for story writing. Educ Inf Technol 28, 14361–14397 (2023). https://doi.org/10.1007/s10639-023-11741-5
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DOI: https://doi.org/10.1007/s10639-023-11741-5