# Storypark: Leveraging Large Language Models to Enhance Children Story
Learning Through Child-AI collaboration Storytelling

Lyumanshan Ye<EMAIL_ADDRESS>Shanghai Jiaotong
UniversityShanghaiChina ,  Jiandong Jiang<EMAIL_ADDRESS>Shanghai
Jiaotong UniversityShanghaiChina ,  Danni Chang<EMAIL_ADDRESS>Shanghai
Jiaotong UniversityShanghaiChina and  Pengfei Liu<EMAIL_ADDRESS>Shanghai Jiaotong UniversityShanghaiChina

###### Abstract.

Interactive storytelling has been widely adopted by educators in teaching
activities of young children. Such a teaching method combines storytelling
with active child participation, benefiting their expressive abilities,
creative thinking, and understanding of stories. Interactive storytelling
requires facilitators to unidirectionally narrate the story content and
encourage children’s participation in story plot creation and interpretation
of central themes through multi-sensory interactive methods such as
questioning and drawing. However, providing tailored guidance based on diverse
feedback from children during interactive storytelling poses challenges for
most facilitators. These challenges include expanding story plot development
based on children’s ideas, using drawings to visualize children’s thoughts,
and interpreting the story’s central themes based on children’s thinking. This
necessitates facilitators to possess strong imaginative, associative, domain
knowledge, and drawing skills. Large language models have demonstrated their
potential in facilitating responsive and participatory dialogues, offering new
design possibilities to address the challenges faced by facilitators in
interactive storytelling.

In this study, our goal is to leverage large language models to design an
interactive storytelling system that provides children with plot frameworks
and interpretations of central themes during the interactive storytelling
process. Through user experiments involving 20 child participants, we evaluate
this interactive system’s usability, learning effectiveness, and user
experience. The user study shows that Storypark improves learning outcomes in
understanding story key ideas, generalization, and transfer. And high
engagement and willingness to use of participants demonstrate that StoryPark
provides children with a positive learning experience.

Human-centered computing Interactive systems and tools; Storytelling,
Children, Multimodal interfaces; Large language models; Child–AI
collaboration, Co-creativity

††ccs: Human-centered computing††ccs: Human computer interaction (HCI)††ccs:
Interactive systems and tools††ccs: User interface programming

##  1\. Introduction

To enrich children’s learning experiences before daily conversations and
scenarios, researchers emphasize the importance of incorporating storytelling
materials into teaching activities. Story materials can help children to have
early exposure to domain knowledge and some universal principles of human
society in diverse and dramatic scenarios. Interactive storytelling is a
flexible and effective educational method that integrates storytelling with
children’s participation, and it has been widely applied in teaching
activities for preschool and elementary school children. This educational
approach requires facilitators to center their interaction around story
materials, engaging with children through various forms. Among these, the most
common form of interaction is interactive dialogue. In interactive dialogue,
facilitators pose questions to children about the story or pictures in the
book and provide scaffolded feedback based on children’s responses. Feedback
strategies include implicit correction, repetition, expansion, and modeling of
answers in various forms. Within dialogic reading, facilitators encourage
children to participate in the creation of story plots by designing
questioning methods and articulating their understanding of the core ideas
contained within these storylines. Compared to one-way storytelling,
interactive storytelling does not cultivate passive listening; instead, this
process enhances children’s understanding of story meaning, language
expression abilities, and creative thinking. Previous research has found that
increasing interactivity and semantic guidance during storytelling can
effectively enhance the positive impact of storytelling on children’s language
development. Beyond language, facilitators also encourage children to
visualize their ideas through drawing.

Therefore, this story-based learning method requires children to comprehend
and practice the educational content of stories under the guidance of more
knowledgeable individuals such as parents, teachers, or older peers. However,
providing real-time guidance to facilitators based on children’s feedback
during interactive storytelling poses numerous challenges. These challenges
include expanding new storylines based on children’s ideas and integrating
them with the story background, visualizing children’s ideas through drawing,
and further explaining the core ideas of the story based on children’s
understanding. This necessitates facilitators to possess strong language
expression abilities, imagination, associative thinking, broad domain
knowledge, and preferably, some level of drawing skills. Additionally,
interactive storytelling necessitates a one-on-one environment to ensure
children’s active participation. However, given the limited resources faced by
most educational institutions, one-on-one storytelling educational resources
are not feasible in reality.

To address this challenge, researchers have long been exploring the potential
of artificial intelligence (AI) to simulate human storytelling interaction.
Prior to the emergence of large language models, AI primarily focused on
optimizing structured interactions with children, such as questioning and
providing evaluative feedback. With advancements in AI technology, large
language models have demonstrated their potential in facilitating responsive
and participatory dialogue, generating contextually relevant and coherent text
based on the user input received so far. This capability can simulate ”real
dialogue,” offering new design possibilities for addressing the challenges
faced by facilitators in interactive storytelling. However, the feasibility
and effectiveness of involving children in storytelling through large language
models as partners remain unclear, especially in the context of supporting
contextual understanding learning. Furthermore, it is not yet clear what
specific workflows are needed to customize the capabilities of large language
models and design interactive systems for storytelling collaboration between
children and AI, tailored to the needs of child users in interactive
storytelling scenarios.

In summary, this paper makes the following contributions:

  * •

Design and develop Storypark, a multi-modal child-story creation system that
enhance story learning including core value and domain knowledge through the
use of a question-scaffolding feedback framework. Provide multi-modal
creativity support for children by conversational generation assistant and
sketch generation assistant.

  * •

Formulat the prompt engineering of children-story cocreation for educational
purposes base on large language model. We evaluate the performance of
artificial intelligence compared to human users in generating expanded story
content and questions for the task of storytelling to children.

  * •

Results from a user study, summarized the advantages and limitation of
Storypark from the aspects of learning effectiveness and learning experience,
as well as the qualitative views of children, parents and education experts on
Storypark.

##  2\. Related works

In this section, we review research on: (1) Teaching values and knowledge
through interactive storytelling, (2) Multimodal collaboration system, and (3)
Using LLMs for child-facing storytelling system.

###  2.1. Understanding values and knowledge through interactive storytelling

Interactive storytelling has been demonstrate as an effective educational
method(Gallagher, 2011; Collins, 1999; Bonds, 2016; Leonard, 1990). And it has
been proven that it can improve children’s language and narrative
skills(Anggryadi, 2014; Zuhriyah, 2017). In the human-computer interaction
community, there have also been some works in natural language processing
focusing on issues related to interactive storytelling(Zhang et al., 2022; Yao
et al., 2022; Marzuki et al., 2016; Xu et al., 2022). However, these studies
focus on how to improve the performance of interactive storytelling system,
but do not pay attention to the learning process of children’s interaction
with the story system. With the emergence of large language models, some
studies have incorporated large language models into children’s interactive
storytelling activities. Nisha use automated planning in Natural Language text
generation to create stories that are coherent and believable(Simon and Muise,
2022; Simon, 2024), jiajun introduce the FairytaleCQA dataset for children’s
story narritive. but these study have not take into account how the system
interacts with children. yuling sun’s qualitative research proves that parents
have a need for interactive story systems, but her investigation focuses more
on tangible interfaces and has yet to come up with a solution(Sun et al.,
2024). In addition, studies have explored the use of llm to promote children’s
cooperative activities (jiawen liu, 2023a; Zha et al., 2024), as well as
parent-child interaction (Lee et al., 2022), child socializing (Seo et al.,
2023). However, these studies focus on small areas of application, and
interactive storytelling is a basic and general educational tool that can have
a wider range of applications.

###  2.2. Multimodal collaboration system

Previous studies have designed story idea generation tools for children.
Generation methods include vr avatars, voice interface creation stories(Barber
and Kudenko, 2010), through drawing creation, tangible interface(Liang et al.,
2021) and graphic interface(Shao et al., 2021; Murray et al., 2017). Such
tools underscore the significance of multi-modal creative support, enabling
children to express themselves through various mediums. However, few studies
have focused on addressing the core truth of children’s understanding of
stories through participatory creation. Benton present a storytelling system
for narrative co-creation through a game(Benton et al., 2014), Zarei came up a
enactment-scaffolded narrative authoring tools(et al., 2021). But these
studies do not integrate the educational goals and understanding processes of
stories. Some studies have noted that interactive systems improve children’s
math(Zhang et al., 2024; Ruan et al., 2020) , coding skill(Rocha et al., 2023;
jiawen liu, 2023b) and narrative skills(Liang et al., 2015). Some studies have
noted that interactive systems improve children’s math and narrative skills.
But most of the research has tried to improve a single skill. Few studies have
focused on designing an interactive system that helps children improve the
more general skills of understanding and transferring the core values of
stories through interactive storytelling.

###  2.3. Using LLMs for child-facing storytelling system

Pretraining large language models, such as GPT-3(Brown et al., 2020),
GPT-4(OpenAI, 2023), and LLAMA-2(Meta, 2023), have accelerated the field of
NLP in recent years. These models are trained on large amounts of text data
and can perform downstream tasks such as text generation(Wei et al., 2022)
etc. Since the large model output depends on the input, the prompt of the
large model greatly affects the output performance of the large model, and the
prompt allows individuals to provide specific text instructions and examples
to guide the model to perform the task(Zamfirescu-Pereira et al., 2023).
Although the nudged llm may not consistently outperform baseline models, it
provides a lightweight approach that achieves competitive performance on a
variety of tasks(Brown et al., 2020; Chowdhery et al., 2023).

LLMS have the potential to facilitate responsive and participatory
conversations. They are not limited by a predetermined dialogue paradigm, nor
do they rely on pre-written replies. LLMS are able to generate contextually
relevant and coherent responses based on the input they receive(Wei et al.,
2022), which allows them to achieve near-real conversations, collaborate
effectively with children, and create a cohesive response. Generate naturally
rich dialogue(Lee et al., 2023a). Therefore, llm is very suitable for the
field of education and interaction with children. Many researchers have made
efforts to incorporate llm into educational technology(Kasneci et al., 2023).
These adaptations witness the application of llm in creating educational
content(Dijkstra et al., 2022; Gabajiwala et al., 2022), enhancing student
engagement and interaction(Abdelghani et al., 2023; Bao, 2019; Tai and Chen,
2023) and personalized learning experiences(Sailer et al., 2023). For example,
LLMS have been used to generate children’s narratives(Alabdulkarim et al.,
2021; Guan et al., 2020). In different research trajectories, some scholars
have used LLMS to create intelligent learning partners that are able to
collaborate with humans(Ji et al., 2023a), provide feedback(Jia et al., 2021)
and encourage students(El Shazly, 2021; Tai and Chen, 2023). A common
application involves employing LLMS as dialogue partners in written or oral
form, such as in the context of task-oriented dialogues that provide
opportunities for language practice(El Shazly, 2021). Or use llm as a story
continuation partner to collaborate with children to create stories(Peng et
al., 2023). Overall, we believe that large language model applications, can
provide children with unique conversational experiences that complement their
interactions with others. Our findings have implications for designers and
developers of applications based on large language models designed to support
children’s learning. In addition, they highlight areas that require further
consideration to ensure that the technology is both supportive and effective
for different learning experiences.

##  3\. Storypark: System design

To generate the design principles of the participatory story interactive
system for children, we recruited five education experts and three parents to
conduct semi-structured interviews. Each participant had more than one year of
experience in educating or raising children. The interview focused on three
points: the educational purpose, the benefits of interactive storytelling for
children, and the problems that educators currently encounter in interactive
storytelling.

Participants indicate that telling stories to children generally has teaching
purposes. Teaching purposes are usually divided into two types, daily domain
knowledge and core ideas. Interactive storytelling aimed at children places
high demands on the guide’s own knowledge, imagination, and narrative skills.
Children, who have not yet undergone extensive learning processes, often
provide feedback that can be unexpectedly surprising to adults. The guide
needs to assist the children in weaving their imaginative responses into the
story framework, elaborating new plot lines based on the children’s feedback.
In the model evaluation mentioned below, we designed a questionnaire with a
given story outline and educational objectives. Participants were required to
devise interactive questions and write plot expansions and moral
interpretations based on presumed children’s responses. Of the 20
participants, 15 had professional experience in early childhood education,
while 5 had experience raising children aged 3-8. The participants commonly
expressed that ”the questionnaire was challenging to answer” and ”without
having been a kindergarten teacher, one would probably not manage to respond.”
The questionnaire consisted of 9 questions, with the median time spent filling
it out being 1638 seconds. This process also demonstrates the challenges that
guides face in co-creating stories with children.

We followed these 3 design guidelines:

  * •

DG1: The storytelling system should adopt an interactive model of ”Question-
Feedback-Story Generation”, presenting open-ended questions to elicit
narrative contributions from children. Based on children’s contributions and
the story framework, the system should expand details to support further story
development.

  * •

DG2: The storytelling system should help children understand the core morals
of the story and provide detailed explanations based on children’s responses
and story samples.

  * •

DG3: The storytelling system should augment the ability for children to create
stories through voice by incorporating drawing features for visual completion,
providing additional visual cues for children.

###  3.1. Feature design

Figure 1. Child interaction process

Story can be divided into four parts includes domain knowledge, plot
development, main idea, and story elements. Among them, knowledge and key
values are the most important assessment points for learning stories, and they
are also the teaching purposes that parents and teachers want to achieve
through stories. The co-creation process between AI and children focuses on
story development. We will describe the specific design details of the company
park below. StoryPark uses AI capabilities to help children expand the
contributions they want to add to the story, and effectively weaves the key
values and knowledge points of the story into the development of the
storyline. StoryPark has five interactive steps in this interactive
storytelling process: 1) generate question leads to elicit children’s
narrative contributions, and 2) expand storyline development based on
children’s feedback input. 3) Help children complete story elements after they
draw them. 4) Based on the children’s participation in the creation of the
story, generate the truth questions to evaluate the central idea of the story.
5) Agree with the child’s answer and further explain the core truth expressed
in the story. Figure1 shows the overview of the StoryPark process.

####  3.1.1. Question-Feedback-Story Generation model

A round of dialogue sets up the story and listens to the background of the
story. The AI asks the child questions and lets the child decide what happens
at this point in the story. Two questions are designed, and the number is
determined according to the suggestions of parents and preschool teachers.
Because children’s attention is not able to focus for long periods of time,
two questions can fit into the attention span of most children. At the end of
the story, ask the child questions and explain the reason again. Multiple
rounds of dialogue can take place here, and if the child gives an answer
contrary to the key values of the story, the AI will give advice and give the
right guidance. Two questions are designed to address the different core
truths a story can convey.

####  3.1.2. Visual-voice interface: Multi-modal assistant

In the process of education, it is important to give children specific visual
descriptions in time to attract children’s attention. It can help children
visualize their imagination. Previously, educators had also used the method of
drawing sticks on the blackboard. Therefore, we designed the function of AI to
complete children’s drawing. The child first uses the button to record voice,
and then uses the sketching board to directly draw the story they want to
create. In the migration scenario, StoryPark randomly provides story elements
created in previous stories, as well as elements from other stories. The voice
tells the child to use different story elements to convey the same core idea.
Test whether children can combine the truth they have just understood with the
domain knowledge they have previously understood to build new stories. The
child needs to transform different scenes and story elements and be able to
describe the development of the story using sounds and images respectively.

###  3.2. How to use this system

As shown in the figure2, Storypark is used on iPad. Users press the voice
button to communicate with Storypark. When the voice button lights up, it
means recording is in progress, and when the child presses the speak button
again, the recording ends. Storypark will continue the story development based
on what they have said, while still conveying the original core key value of
the story. After the child tells Storypark about their creation, they can
press the drawing button to bring up the drawing board, where children can
freely draw elements of their creation directly in the story scene. Storypark
has designed a feature to help children complete drawing elements because
concrete drawing operations and visual feedback are helpful for children to
increase their engagement with the story, deepen their understanding, and aid
in memory retention. After completing the entire story, Storypark will ask the
children two questions in succession to assess whether they understand the
knowledge and core key value of the story. Children can engage in multiple
rounds of voice conversations with Storypark. Even if children provide
incorrect, negative (”I don’t like being polite”), irrelevant, or
uninformative answers, Storypark will patiently explain the core knowledge of
the story to them. In the final transfer phase of the assessment, children are
asked to use Apple Pencil 3 to draw or erase on the canvas and switch colors
in the drawing tool area. The system also allows children to move, rotate, or
scale the story elements provided on the canvas through touch gestures such as
dragging, rotating, and pinching. Based on these elements on the canvas and
the key value of the story just co-created, they can create a new story that
still conveys the same key value.

Figure 2. User Interface of StoryPark

Here is a complete interactive process example:

  1. (1)

StoryPark[prologue]: Once upon a time, there was a little turtle…

  2. (2)

StoryPark[first interactive question]: Children, what can bear do with its
shell?

  3. (3)

Children[pressing the voice button]: I know bears like honey and can make a
bowl out of shells.

  4. (4)

Children[Press the voice button again to indicate the end of this audio
recording]

  5. (5)

Children[press sketch button], StoryPark[evoke sketching board],
Children[start sketching]

  6. (6)

StoryPark[SketchRNN helps children complete drawing details based on classes
choosen by children]

  7. (7)

Children[Press the sketching button again to represent the end of sketching],
StoryPark[Collect elements drawn in the sketching board]

  8. (8)

StoryPark: Wow, kid, you’re so smart! Like you said, little Bear uses the
turtle’s shell as a bowl…

  9. (9)

StoryPark[second interactive question]:… Omit the second round of interactive
questions and the second round of story creation

  10. (10)

StoryPark[end of story]: You’re awesome, kid! The bird uses the turtle’s shell
as… This process changes the way the turtle thinks about its shell…

  11. (11)

StoryPark[first key value question]: Kids, why does the bird thank the turtle?

  12. (12)

Children[presses voice button]:” Shell is good”

  13. (13)

Children[Press the voice button again to indicate the end of this audio
recording]

  14. (14)

StoryPark[first key value guide]: The bird thanks the little turtle because…
What this tells us is that sometimes even seemingly ordinary traits or things…

  15. (15)

StoryPark[end of story, random display of the elements the child has just
created, as well as other story elements]: Kids, you can use these elements to
tell a new story, still express…

##  4\. Implementation

In the concrete implementation, StoryPark consists of two parts, namely prompt
engineering and system building. Since LLMs is used as the core component of
the multimodal interaction process, the prompts used to utilize LLMs execution
are analyzed and designed in detail to ensure stable LLMs performance in all
aspects. For the part of system building, we first evaluated the current LLMs
and human performance on continue writing and feedback, verified the
feasibility of using LLMs. Secondly, we explained the technical implementation
of the system in detail, and showed the technical implementation details of
StoryPark.

###  4.1. Prompt Engineering

####  4.1.1. Question Generation

In order to achieve interactive storytelling and meet the requirement of
asking open-ended questions in the design principles described, the design of
questions in the story should avoid simple Y/N questions, allow children to
make diverse answers, and the direction of the answer should not exceed the
original frame of the story. Therefore, we will send a preset prompt to the
model before question generation, such as ”Tell a story to a 3-7 year old
child, and let them understand the positive value of the story, given a single
open-ended question at the end of the unfinished story, which answer can
expand story” In general, depending on the story frame, we directed the model
questioning about item usage or language response. At the end of the story,
StoryPark will raise questions about the key value contained in the story. We
set prompt let model to generate questions with positive educational effect
and in line with the story outline, and give positive feedback after children
answer. For possible deviations from educational purpose or negative answers,
the model will give guidance in the correct educational sense and give
encouragement.

####  4.1.2. Story Continuation

Role setting for large language models: In order to better enable LLM’s to
continue writing interactive stories, and ensure the establishment of the
overall story interaction framework, we first prompting the model to assume
the role of a ”interactive story telling robot”. At the beginning of each
interaction process, we set the character prompt ”Your character is a
children’s story interactive robot, and needs to interact and continue the
story based on children’s feedback, and can convey positive values. Your
audience is 5-12 years old.”

Story opening template Settings: The next part of the prompt focuses on the
story context setting. We provide the model with a narrative scenario: ”The
main outline of the story and the plot development is [story setting], and
through this story teaching the child ’[key value]’. Please expand and
continue this story, in the story, design interactive questions 1 and 2 ”. The
square brackets [story setting] and [key value] represent the outline of the
story development and the educational key value of the story, respectively,
where the elements will be dynamically filled according to the story
selection. We also set some high-level rules to ensure that the generated
story content is ”simple and appropriate for young children” and does not
contain anything unrelated to the story.

Story continuation template Settings: In order for the model to have coherence
in story continuation before and after interactive questions, we need to
indicate the model continuation requirements. In the continuation phase, the
task of the model is to encourage children’s answers and give positive
feedback, and then continue the content according to the story outline
combined with children’s feedback. This is done with the following detailed
prompts: ”Tell the beginning of the story, then continue the story to the
position of question 1 and stop to generate interactive question 1, where you
need to stop and wait for the child’s feedback”, ”The child’s feedback is
[feedback], generate an expansion and continue the story until the child stops
at question 2 and waits for the child’s feedback”, ”The child’s feedback is
[feedback], generate the ending plot”, and the square brackets [feedback]
represent the child’s feedback content.

Context maintenance in story continuations: We observed the LLM’s occasionally
forgot the storyline which has already expanded as interactions continued and
story outline of the setting. This results in LLM’s expands a completely
unrelated new story based on the last answer. Since the OpenAI API interface
is stateless, we maintain a dialogue context during the interaction, which
saves the LLM’s dialogue with the child and the system prompt setting in the
current round of interaction. This ensures the consistency of Storypark in
story development.

####  4.1.3. Scaffolding

In the process of interactive story creation, children may give feedback that
they cannot expand the story. We classified the situations where children
require scaffolding into two main categories :(1) cases where they completely
cannot continue the story, expressed as uncertainty (e.g., saying ”I don’t
know”); (2) cases where they gave only short and vague answers, and the image
completion assistant models did not include children’s answer object. To deal
with this situation, we give different scaffolding, and to construct the first
situation, our first step is using model to determine that the child is
showing signs of uncertainty. After the uncertain answer is given, we utilize
model to provide hints related to the question, and adhering ”paraphrasing”
strategy. When solving the second case, GPT-4 model was used to
comprehensively consider and feedback according to the children’s previous
short answers and the list of image completion model types of image completion
assistant. This method involves first acknowledging what the child said,
continuing the story considering both related object in the image completion
models and child’s feedback.

####  4.1.4. Ethical Considerations

The safe use of AI by children has been a topic of public discussion. As we
transition to an era dominated by generative AI, the debate becomes more
urgent. In this new context, children may directly encounter content produced
by AI without the protective filters traditionally provided by human
supervision.

On the privacy side, we utilize two cloud-based services to process children’s
utterances: iFlytek’s speech-to-text for transcription, and OpenAI’s GPT-4 for
story continuation and feedback generation. During the invocation, the child’s
utterance is not stored. Additionally, OpenAI promises not to use data passed
through the API to train their models. But when cloud-based AI products enter
the market, the adequacy of children’s data privacy protection is still
uncertain due to the lack of specific regulations for AI products. On the
security side, to prevent inappropriate AI-generated content, we added a
moderation layer that exploits OpenAI APIs to ensure the output is does not
contain sexual content, hate speech, harassment, violence, or self-harm. We
also use prompt Settings to limit the amount of content involved in story
continuation and problem feedback.

###  4.2. Multi-modal Story Interaction System Build

StoryPark is implemented as a web-accessible software to run on an iPad with a
iPencil, microphone, and speaker. The application is written in Python,
JavaScript and Html to interact with stories through voice input, iPencil
drawing, and tap operations.

The front end of StoryPark is built using the React framework, which is an
open source JavaScript library maintained by FaceBook which uses components to
quickly develop single-page applications. The sketch drawing components and
image related operations the interface are implemented using the p5.js
library. When sketching, the child can perform actions such as selecting brush
colors, clearing the sketch board, completing the image, etc. The back-end
server is developed using Flask framework. In the process of interaction, the
data types of the front end and back end interaction include audio, text and
image, so the StoryPark back end realizes the transceiver interface of
multiple data types. We chose to use GPT-4 as our LLM model of choice, which
is currently the most powerful LLM available from OpenAI, We use the prompt
designed in 4.1 to send and receive the generated text content using OpenAI’s
GPT-4 interface.

Figure 3. Multimodal interaction process technical details

Figure3 shows the multi-modal interaction process technical details. In multi-
modal interaction, StoryPark implements the sketch module to provide the
ability of sketch completion. Considering the differences in drawing ability
of children of different ages, StoryPark will supplement the sketch after
children have finished drawing. sketch-RNN is a recurrent neural network (RNN)
capable of building common object graphs based on strokes using the dataset
Quick Draw! trained on, the Quick Draw dataset is a collection of 50 million
drawings across 345 categories. In the interaction process, firstly, the
sketch module extracts the keywords in the child’s voice response and matching
with 114 RNN model categories that realize the completion ability. The
similarity matching is realized through LLM’s ability. After obtaining the
keywords, the corresponding type of RNN model is loaded for sketch completion.

Through the voice module, StoryPark enables converse capabilities with
children. In the voice narration process, after obtaining the generated text
through prompt engineering, we use the text-to-speech interface provided by
iFlytek to create corresponding voice audio, which is played to the children
via the front end. In voice recognition, after the child presses the dialogue
button, the iPad activates its built-in microphone to start recording. After
the child clicks the dialogue button again, an audio file is created and sent
to the backend voice module. We use iFlytek’s speech-to-text interface to
recognize the content of the speech, and the recognized text content is
inserted into the prompt to continue story generation.

###  4.3. Model Performance Evaluation

To assess the performance of GPT-4 in generating stories expansions and
questions, we design a set of identical story outlines and educational points,
which are distributed to both human participants and ChatGPT. By comparing the
content generated by humans and ChatGPT, we evaluate the GPT-4’s performance
in co-creating story plots, formulating open-ended core idea questions,
expanding storylines, and providing detailed explanations of the story’s
central concepts.

Figure 4. The Questionnaire of Human-generated story materials

We gathered 20 participants and generated 80 stories. Of the participants, 15
had professional learning experience in early childhood education and 5 had
parenting experience with children aged 3-12. Participants were asked to write
interactive questions. The author observed children’s reaction in these story
nodes through a brief pre-experiment, and randomly selected some children’s
feedback into the questionnaire. The questionnaire listed the designed
questions and randomly selected three different children’s feedback, and
participants need to expand the plot and explain the reason according to the
questions and the three kinds of children’s feedback. As shown in Figure 4,
for the four questionnaires issued, I added story pictures to facilitate
participants’ understanding.

The questionnaire question design for human participants was the same as the
prompt word task for ChatGPT4, and the same questions and guidance were used
in storypark’s story core co-creation process. As Figure 4, the human
participant and ChatGPT4 are required to complete the same task.After
obtaining 80 stories from participants with early childhood education
experience, we also generated 80 stories using GPT-4.

####  4.3.1. Evaluation index design

As shown in Figure 5, we designed a complete evaluation metric for the
requirements of story co-creation. It is divided into two modules: text
generation and question generation. The text generation module is divided into
plot extension text and truth extension text, and the question generation
module is divided into plot question and truth question. There are similar
indexes and different indexes of the two groups of modules. The metrics are
based on a widely accepted approach to evaluating AI-generated conversations
presented in the work of Junyi Li and Ziwei Ji(Shi et al., 2023; Lovato and
Piper, 2019). In this method, evaluators were shown pairs of stories, one
generated by GPT-4 and one generated by a human. They were asked to choose
from each pair based on the evaluation dimension. Therefore, we ask three
evaluators to choose from each pair of generated text based on the different
dimensions given in the index system. All assessments were conducted by three
university student research assistants with more than one year of experience
in the education of children.

Figure 5. Evaluation index design

####  4.3.2. Evaluation of Content generation

In the text generation evaluation module, the four evaluation dimensions of
story generation are understandability, preference, logical rationality and
attractiveness. For guidance generation, the four dimensions of evaluation are
understandability, preference, clarity of interpretation, and relevance. Based
on these definitions, participants chose which of two pairs of generated story
content or guided content performed better.

Dimensions: Here provides a detailed explanation of these metrics:

  1. (1)

Preference: Whose story/principle do you like better?

  2. (2)

Understandability(Zhang et al., 2024): Which story would you think is in easy
to understand, and using correct grammar and vocabulary.

  3. (3)

Articulately explain: Who explains the key values more clearly and concretely
for children?

  4. (4)

Attraction: Whose story sounds more creative, contains more twists and turns
and more vivid details?

  5. (5)

Story Relevancy: Which explanations are more in line with the story’s core
educational goals?

Figure 6. Story content evaluation results

Result: As the figure6 shows, GPT-4 is very close to human-generated content
in terms of intelligibility. The content generated by GPT-4 is superior to
that of humans in terms of logical rationality, attractiveness, and
preference. ChatGPT-4’s attractiveness and explanatory clarity after linking
stories to new plot creation may be due to the fact that the task takes
advantage of the large language model’s ability to create logical connections
of language elements based on specific instructions, and that the logical
links made by the large language model are more syntactically tight. Human-
generated story content is close to ChatGPT4 in terms of comprehensibility and
relevance. Subsequent discussions with the evaluators indicated that the lack
of readability of AI-generated stories mainly stems from the fact that the
text generated by GPT-4, while consistent in language structure, sometimes
lacks internal logical consistency.This observation is consistent with
existing research that highlights inherent limitations(Shi et al., 2023;
Lovato and Piper, 2019).

####  4.3.3. Evaluation of Questions generation

In the problem generation evaluation module, the plot problem and the reason
problem have three similar evaluation dimensions. For plot problems, the three
dimensions are: understandability, story relevance, and inspiration. The
question of reason has the same first two dimensions, and the third different
dimension is inclusiveness. Based on these definitions, participants were
asked to choose which of two pairs of plot questions or reason questions
performed better.

Dimensions: Here provides a detailed explanation of these metrics.

  1. (1)

Understandability: Questions are generated in an easy-to-understand format,
using correct grammar and vocabulary.

  2. (2)

Story Relevancy: The questions generated relate to the story plot.

  3. (3)

Inspiration: The generated question stimulates children’s thinking and
provokes them to articulate their thoughts.

  4. (4)

Inclusiveness: The questions generated are open-ended, allowing children to
answer freely.

Result: As the figure7 shows, The evaluations between the human-generated
questions and GPT-4 generated questions were close, with no significant
differences. This shows that the GPT-4 model’s ability to design problems in
story co-creation is acceptable and close to human level. Questions generated
by humans score relatively low on story relevance compared to other
dimensions. In order to maintain the continuity of the story, the relevance of
the problem to the story needs to be considered. This may be because for human
participants with early childhood education experience, the task of designing
easy questions tends to associate them with various situations in life,
resulting in the question design being too divergent and not sufficiently
relevant to the story. The questions generated by GPT-4 were also more
enlightening and inclusive than those generated by humans, possibly because
human participants sometimes created questions that were related to
educational goals but unrelated to plot development. The questions generated
by GPT-4 can more meaningfully collect children’s feedback on plot development
and further promote the development of the story. When it comes to reasoning,
human participants sometimes create questions that are related to everyday
issues, but unrelated to the core content of the story itself, and can be
answered with a clear ”yes” or ”no”, which also limits the inclusiveness of
the question and reduces the space for the child to answer.

Figure 7. Story questions generate evaluation results

Therefore, in reference to the opinions of the evaluators, the API interface
of GPT-4 is used in this study in the formal system construction, because the
story text and question text generated by GPT-4 can achieve the effect of real
human guides in all dimensions of quality, and has good comprehensibility.

##  5\. Storypark Evaluation

The experimental process includes usability testing and formal experiments.
Usability testing aims to validate the usability of the interface design and
ensure that participants in the formal experiment can learn to use the
interface. The formal experiment focuses on evaluating whether the process of
co-creating stories and answering questions about the core ideas and knowledge
of the story system can help children understand the key ideas and knowledge
of the story. There is no overlap between the participants in usability
testing and formal experiments. The participants in usability testing have a
younger average age than those in formal experiments. This is because children
older than 7 years old have more experience using software on mobile phones
and tablets, making it necessary to test the usability of the story system for
younger participants.

Therefore, during the evaluation phase, our research questions are the
following three:

  * •

RQ1: How would Storypark affect story knowledge and point learning outcome?

  * •

RQ2: How would Storypark affect the learning experience?

  * •

RQ3: What are educators and parents perceptions towards Storypark?

###  5.1. Usability test

To probe the user experience and perceived usefulness of the three
storytelling phases supported by Storypark, we conduct a within-subjects
usability test in a children educational institution in Minhang District,
Shanghai, China.To make sure that even kindergarteners can also learn to use
our interface in formal experiments, we recruited four preschool children
between the age of 6 and 7 years(2 females, 2 males, mean age: 6.75 (SD =
0.5)). The purpose of usability testing in this stage is not to evaluate the
effectiveness of Storypark in the story creation process, but to improve the
interface design of the story system through the feedback of the children.
Children can provide us with valuable feedback because they learn from stories
on a daily basis in training facilities.

####  5.1.1. Procedure

After obtaining parental consent and child assent, participants were
introduced to a research assistant and began the four-part user study
procedure which consisted of introduction, understanding, interaction and
evaluation. The devices be used in this experiment were a iPad and a Mac. The
two research assistants involved in the experiment both have experience
teaching preschoolers and both have design backgrounds.

During the introductory phase, participants will be required to view the three
main screens of StoryPark on a Mac computer (i.e., co-creating the plot,
understanding, and migrating the creation). The research assistant first
introduced the participants to the use of the buttons in the interface and
informed the children about the correct order to use the dialogue buttons and
the sketch buttons. The research assistant then explains the meaning of the
four icons that imply the flow of the story, and which stage of story creation
each corresponds to each of the four nodes.

In the comprehension phase after the introduction phase, the research
assistant points to the buttons and icons in the iPad interface and asks
questions to the participants. This is to see if the child can correctly say
the usefulness of the two buttons in the interface, the interaction method,
the order of use, and the meaning of the icon change. The research assistant
assessed the participant’s understanding of the interface using the yes/no
Guttman scale. When a participant’s explanation is incorrect, or the
participant is unable to provide an informative answer (e.g., silence, answer
I don’t know, etc.), the research assistant will again provide the correct
answer about how to use the interface to help the participant move on to the
next stage.

Participants are required to complete the tasks of the three stages using the
provided iPad in the interactive phase. All participants in the usability
testing phase will not participate in subsequent formal experiments, so the
story materials used in this testing will also be used in the actual
experiments. Participants were required to complete two interactive tasks
concluded two storylines co-creation through voice, two storylines co-creation
through canvas, two questions on the core value of the story, and one transfer
creation using new story elements. The research assistant is also present, and
when the child does not respond for a long time, the research assistant will
provide a small amount of help promptly, but will not directly tell the child
what to do next.

Participants rated the story creation system by using a 5-point sus scale
(Cronbach’s Alpha was good for the total scale (=0.78)). In the scale used,
all problem descriptions were changed to colloquial descriptions that were
easy for children to understand. At the same time, in terms of visual
presentation, the 5-point Richter scale has also been improved to be easier
for children to understand. That is, 5 arranged from small to large, drawing
circles from crying faces to smiling faces. The full scale can be found in
Appendix B. Considering the limited literacy skills of preschoolers, the
research assistant will dictate the questions one by one, show the
participants a modified Richter scale on a computer screen, explain that the
crying faces to the smiling faces in the small to large circles represent
”unhappy” and ”very happy”, and ask the children which circle to decide to
choose for this question.

Finally, we asked the children for their opinions on StoryPark and how they
felt during the use of the Storypark. They spent about 20 minutes in the study
and were given children’s toys worth 20 RMB.

####  5.1.2. Result

Our usability testing score is 73.33 (¿65), indicating that our interface
design has good system usability among children. One participant could not
recall the meaning of the left-hand process hint button; therefore, in the
formal experiment, we added symbols of ”light bulb” and ”question mark” to the
left-hand process buttons. Additionally, we enlarged the recording and drawing
buttons on the right-hand side to facilitate children’s operation.

###  5.2. Participants

We sought 20 child participants (11 female, 9 male (M=7.97 S=2.81) between the
ages of 5 and 13 years in child training institutions and kindergartens
through offline recruitment. Gender and participant imbalances we will discuss
in the ”Limits” section. None of the child participants had extensive
experience with AI tools. The participants were compensated with 20 yuan worth
of money for the 20-minute session. In the formal experiment, we designed
story materials tailored to participants of different age groups, with varying
emphasis on key ideas and knowledge. Drawing from insights provided by five
educators, for participants under the age of 6, the key ideas and knowledge in
the stories primarily encompass specific behaviors and everyday language usage
(e.g., expressing gratitude promptly after receiving help, avoiding getting
wet in the rain). For participants aged 7 and above, the key ideas and
knowledge include insights that require participants to contemplate and
analyze the logic of character behaviors (e.g., adopting a different
perspective to tackle challenges, appreciating one’s unique qualities).

###  5.3. Metrics

In user study, the evaluation metrics focus was on children’s learning of
learning of story value and knowledge, the quality of transferring value to
the new stories they created, as well as their engagement and enjoyment
throughout the process. In all instances where children’s feedback was
required, an experimenter orally presented the questions and anticipated
verbal responses from the children. The experimenter recorded the children’s
verbal feedback, and the complete experiment was recorded on video. Thus,
children did not need reading or writing skills to answer questions.

####  5.3.1. Learning of Story Education Goal

In previous studies, few studies focused on assessing the degree to which
children understood the key idea of the story. In order to assess whether
participants understood the key value of the story through co-creation with
the story system, and whether the co-creation with AI had any influence on the
understanding of the truth, we refer to the ”Core continent Standard”(eng,
2024) and their assessment method of exercising math ability through stories.
A six-question, two-stage scale was designed. The pre-test and post-test
questions were the same, and both were scored on a true/false binary scale. In
order to prevent children from directly repeating the answers in the pre-test
in the post-test stage, the words used in the post-test questions are slightly
adjusted compared with the pre-test questions. The four dimensions of the
scale refer to the definition of English Language Arts in the Common Core
State Standards in the United States. Children’s understanding of the story
principle was assessed through knowledge, key idea, generalization and
transfer.

The knowledge and core idea dimensions are scored on a binary scale: 0 for
wrong and 1 for right. Here are the definitions and sample questions for these
two dimensions:

  * •

Knowledge: Whether the child understands the specific knowledge mentioned in
the story

  * •

Key idea: Whether the child understands the key idea expressed by the story

For the knowledge dimension, ask the children questions about the knowledge
mentioned in the story. Ask the children to explain the meaning of some common
sense knowledge mentioned in the storyline, such as ”What is the
characteristic of the turtle’s shell”. Each story material contains 3-5
knowledge points that can be evaluated.

In the core thought dimension, children are asked to answer questions about
the truth of the story. Children need to analyze why the characters in the
story did what they did and how they would move the plot if they were the
characters in the story. In order to distinguish between pre-test and post-
test, the description of the problem was fine-tuned in the post-test (for
example, the pre-test asked, ”The little turtle does not like its shell, how
do you comfort it”, and the post-test changed to ”the little turtle thinks its
shell is heavy, how do you comfort it”).

The generalization and migration project is open-ended, allowing children to
answer freely through it. The scores match as follows: 2 means you can
transfer the key idea with a new story, 1 means you can retell the original
story in your own language, and 0 means you can’t complete it. Here are the
definitions and sample questions for these two dimensions:

  * •

Generalization: Children summarize and abstract general vocabulary (such as
definition of features) after understanding the truth from specific story
plots.

  * •

Transfer dimension: Whether children can transfer the truth of the story to a
new scene and explain the truth from their own perspective.

In the generalization dimension, children are asked to understand and abstract
words mentioned in stories that generalize. Such as ”What is the definition of
characteristics?” . For migration problems, children need to use randomly
provided new story elements to tell a new story, but still convey the core
truth of the story. Each story was designed with three core principles, which
were randomly selected in the experiment, and the children were asked to use
new elements to create a story that could express the core principles.

Based on this scoring system, we calculate the total score by adding the
scores for all the items. The Cronbach’s alpha for these problems is 0.65,
which means that there is a good consistency between the four dimensions,
which is acceptable.

####  5.3.2. Engagement

We measured children’s engagement in two ways, behavioral observations and
quantitative analysis. Behavioral observation was used because child subjects
sometimes have difficulty expressing their thoughts as clearly as adult
subjects. Therefore, behavioral experiments can also give real feedback on
children’s participation in the story park. In order to understand the
participation of children in the interaction with the story park, we assessed
the quality of children’s answers to questions in the quantitative indicators,
and the evaluation criteria were the quality of answers and the length of
answers. There are three types of response quality: meta-comments that reflect
the child’s thought process (e.g., ”I think so”), statements that are
irrelevant or uncertain to the question (e.g., ”I don’t know” and ”I don’t
want to hear it”), and substantive responses that include meaningful details
that move the story forward.

In behavioral observation, the experimenter will directly observe the
following four behaviors of the participants:

  * •

Whether the expressions and movements are happy during the entire process of
participating in the experiment;

  * •

Whether the tone of voice is positive when interacting with the Storypark;

  * •

Whether the experiment requires repeated use of certain functions of the
Storypark;

  * •

After the experiment, are you willing to discuss the story park with the
experimenter?

####  5.3.3. Enjoyment

To measure children’s enjoyment in interacting with artificial intelligence or
humans, we adapted a questionnaire consisting of 4 items based on the prior
work of Waytz(Waytz et al., 2014). These questions are as follows:

  * •

Are you happy when you tell a story with Storypark?

  * •

Do you feel comfortable telling a story with Storypark?

  * •

Do you want a reading companion like Storypark?

  * •

Do you want to tell another story with Storypark?

The experimenters verbally presented questions to the participants and guided
children to indicate their level of agreement with each item using a graphical
scale to express their responses. The Likert scale of five points was
visualized as five circles depicting facial expressions: ”Really happy,”
”Happy,” ”Somewhat happy,” ”A little happy,” and ”Not happy at all.” Each
option was associated with a circle of varying size, with circles ranging from
small to large and featuring faces from crying to laughing. Researchers
pointed to each circle when describing the corresponding option. The size of
the circle corresponded to the degree of happiness, with the largest circle
representing the greatest happiness and the smallest circle indicating the
least happiness. The Cronbach’s alpha internal consistency was 0.77, which is
acceptable.

###  5.4. Procedure and Task

After obtaining the consent of parents and children, the participants were
introduced to the researchers, and the experiment was conducted in four
phases: information collection, introduction, pre-test, story co-creation,
narrative transfer, post-test, assessment, and interview. The devices used are
ipads and Macs.

  1. (1)

Collect information In the information-gathering phase, the children were
asked some basic questions about story learning. The questions were as
follows: 1\. Children’s age and learning stage. 2. How often children usually
listen to stories. (once a week) 3, whether the child can retell the story. 4.
Does the child like to add his own ideas to the story, for example?

  2. (2)

Introduction stage In the introduction phase, the study assistant first
introduced the participants to the use of buttons in the interface, and taught
the children the correct order to use the voice buttons and the canvas
buttons. The research assistant then explains the meaning of the four ICONS
that indicate the flow of the story, and which stage of story creation each of
the four nodes corresponds to. After the research assistant asked simple
questions to ensure that all children present understood the correct use of
the interface, the pre-test session was conducted.

  3. (3)

Pre-test The pre-test stage aims to test children’s understanding of the story
knowledge and story truth before they interact with Storypark. Do the test by
answering questions. Take the turtle story as an example. Table 5-5 lists the
questions.

  4. (4)

Story co-creation In the story creation phase, the researchers chose a story
for the children according to their age group. The children need to complete
four rounds of dialogue and finally create a story in cooperation with
Storypark.

  5. (5)

Narrative transfer In the narrative transfer stage, tortoise, firefly, cat,
fish and other elements are given, and the task of children is to use the
elements in the picture to tell a story and express the core principles of the
story learned in the ”story co-creation” stage. For example, in the story co-
creation stage, children create based on the story material ”the story of the
turtle”, while in the narrative transfer stage, the tasks can be randomly
divided into the following three: tell a story with the elements in the
picture to express ”cherish your own characteristics”; Using the elements in
the picture, tell a story to express ”don’t discriminate against others”;
Using the elements in the picture, tell a story about ”finding the good in
your friends”.

  6. (6)

Post-test The purpose of the post-test stage is to test whether there is any
change in the understanding and mastery of the story knowledge and the story
truth after the children Storypark completes the co-creation of the story.
Take the turtle story as an example. Table 1 lists the questions.

Table 1. List of pre and post test questions

Evaluation dimension | Problem list(Correctly marked as 1 point, incorrectly marked as 0 points)
---|---
Knowledge | 1\. Is the turtle’s shell light or heavy?
| 2\. From the following words, please choose the characteristics of the
turtle shell: light/soft/heavy.
Key idea | 3\. Why did the little turtle dislike his shell at first and then become happy again?
| 4\. If the baby turtle is sad because its shell is heavy, how do you comfort
it?
Generalization | 5\. What does it mean to cherish?
| 6\. What is the definition of characteristics?
Evaluation dimension | Problem list(This dimension is scored 0 points in the pre-test, and 0, 1, and 2 points in the post-test respectively)
Transfer | 7\. Use the elements in the picture (turtle, firefly, cat, fish, bird’s nest) to tell a story and express ”cherish your own characteristics”.
| 8\. Using the elements in the picture (turtle, firefly, cat, fish, bird’s
nest), tell a story about ”finding the good in your friends”.

##  6\. Result

###  6.1. RQ1: Impact on learning outcome

In order to calculate the score of this scale, we recruited 3 college students
with children’s education experience to evaluate the performance of children
in the experiment according to the definition of the above indicators. The
data in this experiment did not meet the normal distribution, so the
comparison of pre and post test data was conducted using paired sample
Wilcoxon signed rank test.

Figure 8. Pre-Post Test Among four Dimensions

As can be seen from the boxplot8, the median of all indicators has increased,
and the pre-test and post-test scores of core ideas and generalization have
increased. The score of the four indicators of learning effect is calculated
in the total score, and the median of the total score is improved, which
proves that Storypark is helpful to story learning.

Table 2. Wilcoxon signed rank test for paired samples of different dimensions Pre-test and post-test Pair Comparison | P value | Cohen’s d value
---|---|---
Question1 of Knowledge | 0.025** | 0.796
Question2 of Knowledge | 0.157 | 0.459
Total score of Knowledge | 0.180 | 0.392
Question3 of Key idea | 0.001*** | 1.577
Question4 of Key idea | 0.000*** | 1.671
Total score of Key idea | 0.000*** | 2.048
Question5 of Generalization | 0.001*** | 1.524
Question6 of Generalization | 0.003*** | 1.247
Total score of Generalization | 0.000*** | 1.949
Transfer | 0.001*** | 1.798

As shown in the table2, among the four dimensions, ”key idea”,
”generalization” and ”transfer” all have significant differences. The
”knowledge” dimension of problem 1 in the pre-test and post-test p values.
0.05, there was a significant difference, but question 2 of knowledge
dimension and the total score of knowledge dimension were P-values in the pre-
test and post-test. 0.05, no significant difference. Because the story
elements prepared this time are common elements of animals, food and plants in
life, and all participants have background knowledge of these elements in
life, Storypark story system has little impact on this dimension.

”Core Ideas” all questions and total P-values are ¡0.05, which means that
there is a significant difference between all of them, which proves that the
learning process of co-creating stories with Storypark can significantly help
children understand the core truth conveyed in the stories.

”Generalization” all questions and total scores show significant differences
in the comparison before and after the test, which proves that Storypark can
not only promote children’s understanding of the core truth conveyed in the
story, but also help children abstract and summarize the truth based on
specific plots in the story into the definition of some general words (such
as: From the turtle shell to the generalization of the story to explain the
definition of treasure, characteristics, with their own language to explain
”everyone should cherish their own characteristics of the truth”.

In the ”Transfer” dimension, 8 subjects were able to successfully complete the
task of ”creating a new story that still expresses the same core idea as the
original story” immediately after listening to the story. Although the
closeness of the connection between the story and the core idea created by
them was different, it meant that after co-creation with the AI story system,
There are many children who can quickly transfer the theory to their own
construction of a new scenario, and make reasonable use of their other
background knowledge. Nine subjects were able to immediately retell the
original story to convey the newly learned truth, while only three 5- and
6-year-old subjects were unable to construct their own story.

The ”Knowledge” dimension had no significant difference between the pre-test
and the post-test, because the story elements prepared this time were common
elements of animals, food and plants, and all participants already had
background knowledge of these elements in their lives, so the AI story system
had little impact on this dimension.

###  6.2. RQ2: Impact on learning experience

####  6.2.1. Engagement

Behavioral observation: It has been observed from children’s behavior that
children generally show a positive attitude toward co-creation with story
parks(Figure9)

Figure 9. Results of Children’s Engagement in the System

Among the 20 participants, 16 exhibited happy expressions, actions, and
positive tones. However, 4 participants displayed confusion and perplexity,
which seemed to correlate with the frequency of storytelling and story
creation at home. These 4 participants reported listening to stories at home
less than three times a week. Following the experiment, 8 participants
actively engaged in discussions with the experimenters regarding Storypark’s
functionalities. Some child participants even volunteered suggestions for
Storypark, which will be addressed in RQ3. The specific experimental data are
shown in the figure9. During the whole process of participating in the
experiment, whether the expression and movement are happy or not (16:4);
Whether participants interact with the story park in a positive tone (16:4);
Whether participants were asked to use certain features of the story park
repeatedly (12:8); At the end of the experiment, the participants were asked
whether they would like to continue discussing the story park with the
experimenter (8:12).

Additionally, 12 participants expressed a desire to use Storypark repeatedly.
The functionalities most frequently requested for repeated use, ranked in
descending order of frequency, were the drawing feature during plot creation
(10 times), regenerating expanded text during plot creation (8 times), and
drawing feature in the transfer section (5 times). There was some overlap in
the number of participants making requests, with some children suggesting the
repeated use of more than one feature. The “drawing-completion feature was the
most frequently requested for repeated use, likely because the act of drawing
with a stylus is more concrete and greatly enhances children’s sense of
participation and creativity, with lower difficulty compared to the final
”Transfer creation” feature.

Quantitative analysis: Among the quantitative indicators, all subjects were
able to give answers. Among all the recorded answers, 30.6% were meta-comments
reflecting the thinking process, and 29.22% contained substantive statements
with explanatory significance. The remaining answers that were either
irrelevant to the question, or uncertain, or refused to complete the
interactive task accounted for 9.8%, and the short answers of less than five
words accounted for 39.2%, the highest proportion, possibly because many
children answered the plot questions directly with a single word. Therefore,
in terms of the combination of quantitative indicators and behavioral
observation, most of the subjects actively participated in the process of co-
creating stories and interacting with Storypark.

In this user experiment, the age span and study period of the subjects are
large. The school section includes middle class, large class, lower grade and
upper grade of primary school. Such a range of subjects can explore the
adaptation degree and effect of children of different ages to the AI-assisted
story co-creation system. In the experiment, it is observed that different
ages have different adaptability to the story system and different willingness
to use it. Based on the participation index, combined with the learning effect
index and qualitative interview, it is found that the children aged 6-8 years
old, in the upper class and the lower grade of primary school have the
strongest adaptability to the story system. Younger children can only give
short answers and rely more on the story system to help them develop, while
children aged 6-8 can give rich answers and perform well in the ”migration”
task, and have a strong willingness to continue working with the story system
to complete the story. This means that the difficulty of the current design of
the story system may be more suitable for children with certain language
ability and knowledge level, and they can obtain a sense of accomplishment in
the process of co-creating stories.

####  6.2.2. Enjoyment

Figure 10. Results of Children’s Enjoyment in the System

The figure10 shows the children’s responses to four questions (Q1 to Q4) about
the pleasantness of the system. Each bar represents the percentage of
responses ranging from ”strongly disagree” to ”strongly agree.” For Q1, a
majority (84%) strongly agreed. Feedback from Q2 was more balanced, with 42%
agreeing. Q3 shows that a majority of 21% neither agree nor disagree (gray)
and 42% agree. 57% of Q4 respondents agreed, with the rest of the responses
spread across other categories. Red, orange, gray, light blue, and dark blue
represent different levels of disagreement and agreement, respectively.

It can be seen that children have an overall positive perception of learning
story content through collaborative storytelling. There is a general high
level of engagement and satisfaction: children’s willingness and satisfaction
in using the AI story assistant were assessed using a four-dimensional scale
ranging from 1 to 5. Among the 20 participants, 16 gave a rating of 5 for all
four dimensions on the scale. Children expressed high satisfaction with the
combination of voice and visual modalities. Two participants, aged 5-6,
accurately articulated the value of the AI story assistant without guidance
from experimenters: ”It can save my parents’ energy because it is a machine
and can tell me new stories unlimited times” and ”My dad is very busy with
work, he only tells me once, but AI can tell me many times”.

###  6.3. RQ3: Educator perceptions towards Storypark

We recruited 9 adult users, including the parents of 3 children who
participated in the experiment, 3 institutional teachers who participated in
the field user experiment, and 3 preschool teachers interviewed through online
conference. They all tried out my AI story prototype. We conducted semi-
structured interviews with parents, educators, and children to observe their
attitudes towards the Storypark.

####  6.3.1. Storypark is an effective educational tool

Participants agreed that participating in story creation was conducive to
children’s understanding of the core ideas of the story. One parent of the
subjects mentioned that ”children guide the development of the story at the
same time, it is a process of constantly understanding the story background
and thinking about the direction of the story plot”, and he believed that this
deep participation can help children understand the story more deeply. Another
parent mentioned.

  * ”I think it’s good to do it in the same way that when we play RPGS (Role-Playing games), we have some plot options. When the user chooses this plot, the story goes in this direction. It makes the player feel very involved and immersed.”

One parent of the subject expressed understanding and recognition of the
design of the two problem points,

  * ”When listening to the story background, he may still listen to the recording as before.” When he encounters the first problem, he will think back to what happened before. I think from his first question to his second question, he will listen or watch more carefully than he did before. Because he knew there would be a second question, and he wanted to shape the story. For example, he continues the story entirely on his own imagination, or he wants to control the development of the story according to his own preferences, so he will listen more carefully.”

####  6.3.2. Participating in a story brings ownership

Children become more self-aware and care more about stories that they can
participate in deeply, thinking that this is ”my story.” This process allows
the child to understand the truth more deeply. Children like repeated
feedback, changing, rather than one-way output, the AI assistant will each
time fine-tune its own extension to the child’s current feedback. The addition
of visual creation can effectively attract children. At present, most picture
books and story recordings are one-way or have different focuses, and few
combine the two modes of speech and vision into a co-creation story system. In
the current way parents tell stories and listen to recorded stories, children
first hear the full plot of a story. AI Story assistants help children create
storylines that add their own ideas from the start. This process of
participating in the creation can help children focus their attention, have
the cognition of ”this is my story”, and then have a deeper impression on the
knowledge points in the story.

A parent of a 9-year-old child expressed recognition of the assessment
significance of this link,

  * ”I think this link is quite meaningful. Children can organize stories in their own language, exercise their expression skills, use their imagination, and test Her ability to weave stories around core ideas is very important.”

####  6.3.3. Repeatedly explore the myriad possibilities in a story

Parents and education experts believe that Storypark has this significant
advantage: artificial intelligence can unlimitedly embed children’s
imagination into story development, and accommodate and expand children’s
imagination. One parent mentioned that ”when I was a child, I probably read a
lot of picture books, but the interaction was just talking and asking one or
two questions.” Another parent said that he believed that his own thinking
would limit his children’s imagination. In this regard, Big Language Models
will perform better than humans:

  * ”Later, when she grew up, my daughter would read stories by herself and rarely read stories with us, because I felt that the child would think that we might hinder some of her understanding or some of her ideas. Because now When she grows up, she has her own thoughts. Just like when she encounters a fork in the story, she can develop from direction A or direction B. Her ideas may conflict with ours, so AI. You can accompany her to explore on her own, and the AI can completely extend the story according to her ideas.”

Children create directly without knowing the complete plot of the story, which
helps them concentrate on thinking about the development of the plot, and is
conducive to cultivating children’s divergent thinking and imagination. The
advantage of the story system compared to human guides is that infinitely
embedding children’s imagination into stories and continuously supplementing
subsequent plots requires a high level of background knowledge on the part of
the educator. In the classroom, teachers cannot help every child in the class
generate their own stories, so they can only have a fixed story template. AI
is a good creative assistant.

Both preschool teachers mentioned that adults’ thinking has accepted the
teachings of social conventions and has been solidified to a certain extent,
making it easy to tell stories to children with strong guidance. Because
parents are so good at summarizing the truth in advance, sometimes their
interpretations will constrain their children’s thinking. Some parents believe
that there is only one answer to story questions and storyline interpretation,
which will also affect the development of children’s divergent thinking and
imagination. For parents, the process of telling the same story over and over
again and replacing story elements can be tedious. Storypark can replace the
process of exhausting story possibilities for children. A preschool teacher
mentioned:

  * ”When the mother tells a story, she may only guide one route, and the children will only know this one route after listening to the story. AI can help children think about the story from many different angles.”

##  7\. Discussion and future work

###  7.1. Experiments limitations

The participants in our study participated in only one session of AI
collaborative storytelling. Such short-term visits may not fully reflect the
changes that occur when children interact with the story system over a long
period of time. Over time, children will become more and more accustomed to
co-creating with the story system. It may take a more adaptable and
interesting storytelling experience to keep a child’s attention during
storytelling. Future research should explore the changes that occur after
children interact with the story system over a period of time. Perhaps by
giving children more opportunities to interact with the AI story system, and
observing whether they will have more interaction with the story system, as
well as changing some of their learning habits and thinking habits.

In addition, children’s performance in achieving educational goals after
interacting with the story system may be related to some of the children’s own
qualities, including background knowledge already mastered, expressive skills,
introversion or extroversion, parents’ occupation, and story listening habits
at home. In this experiment, for example, two six-year-old girls outperformed
many older children. Specifically, they react faster in the ”migration” stage;
The story is more relevant to the core values expressed in the original story;
The story is created using new material rather than retelling the original
story. The parents of both children are university professors, articulate and
extroverted. Therefore, future story systems may be designed to provide more
detailed scaffolding for introverted, slow children, especially during the
final migration phase.

###  7.2. Challenges in the Utilization of LLMs

During the design StoryPark, we encountered three challenges while utilizing
LLMs for generating questions in story continuation.

####  7.2.1. Impact of LLM Unpredictability Output

During the development process of interactive storytelling, we discovered that
LLMs exhibit a high degree of unpredictability in their output. Slight
variations in wording or syntax within the story outlines set in prompts can
significantly affect the content generated by the LLMs, with each iteration of
story generation producing variations. Modifications in prompt engineering
cannot consistently guarantee the desired outcomes, as the interpretation of
our story outlines and prompt details by LLMs remains a ”black-box” process of
input and output. LLMs exhibit “dispersion” in their potential predictions for
a fixed input (i.e., prompt) has also been noted by Lin et al. in their
research(Lin et al., 2023). To ensure that prompt settings yield relatively
stable and consistent outputs, extensive testing of story generation was
conducted during development to assure the stability of the story
continuations.

####  7.2.2. Fine-grained Control of LLMs’ Performance

LLMs demonstrate competence in understanding and executing simple directives
conveyed through basic descriptive language. However, it remains a challenge
when tasked with precise instructions that incorporate numerical details. Our
development experience revealed that LLMs can effectively and consistently
execute instructions lacking precise descriptions, such as constraints related
to plot continuation or requirements tailored to specific story audience
demographics. But when faced with numerically precise instructions, such as
”The story’s opening should exceed 100 words but not surpass 200 words,” LLMs
frequently disregard these word count constraints, producing content shorter
than 100 words. This indicates that current LLMs still have limited
capabilities in understanding numerical values and reasoning, necessitating
the integration of validation layers(He-Yueya et al., 2023) to ensure model
outputs align with exact requirements. Feedback loops (Lee et al., 2023b) also
emerges as a viable strategy.

Additionally, we encountered issues with LLMs deviating from the intended
story progression, wherein the model would overlook the initial story outline
and plot development constraints. To mitigate this, we employed conversational
contexts to maintain a history record, and removed dialogue records from the
keyword extraction phase to prevent interference with story generation, Shi
also found this phenomenon(Shi et al., 2023). For longer stories, injection
prompting at the end of each input has been suggested to enhance LLM
performance(Liu et al., 2024).

####  7.2.3. Impact of LLM Hallucinations

Hallucinations in LLMs refer to instances where the generated responses
possess internally flawed logic or absurd content, albeit appearing
plausible(Ji et al., 2023b; Li et al., 2023). At times, LLMs may not
comprehend the real-world logic of object interactions within story
continuations or whether children’s feedback aligns with the question
requirements, merely producing content that seems correct based on the
constraints of our prompt engineering. We observed hallucinatory phenomena in
our story continuations, for example, in a story about a turtle, children
suggested using the turtle shell to make herbal jelly, and the LLM described
the process of making herbal jelly from the turtle shell, despite the fact
that turtle shells cannot be used to make herbal jelly and such a storyline
cannot proceed further.

In a value-based educational system like StoryPark, such hallucinatory
phenomena do not severely impact educational objectives. However, in contexts
requiring scientific accuracy or logical rigor, this could lead to
misunderstandings. Since the domain of mitigating hallucinations in LLMs is
still in its nascent stages, future research could benefit from exploring
post-processing techniques that review content generated by AI for potential
hallucinatory information(Dziri et al., 2021), or employing external knowledge
frameworks to guide and calibrate information generation within LLM-based
conversational systems(Ren et al., 2023).

###  7.3. Generality for different age of Storypark

Education experts mentioned in interviews that 3-6 years old, 6-8 years old
and over 8 years old are three very different stages. There are great
differences in the development of narrative ability and logic ability of
children in these three ages. Future research should design different story
interaction modes with different difficulty levels according to children of
different ages and abilities. (For example, for children with particularly
strong narrative skills, co-creation questions can increase to 3-5). Improving
children’s ability to understand the core values of stories through
interactive stories takes time to develop habits. Future story systems should
record historical data about each child’s interaction, matching varying levels
of difficulty to the data the child retains in the system.

In addition, the usual learning goals are different for ages 3-6, 6-8, and 8+.
Ages 3-6 usually learn specific daily habits (e.g., saying thank you
regularly). Ages 6-8 begin to understand the logic and motivations of the
characters’ actions in a story, and to learn general ethics and values (e.g.,
being helpful) from the story. Children over the age of 8 focus on learning
more and more complex background knowledge from the story and analyzing more
complex and multidimensional character behavior logic. The future story system
should refine the generated content according to the learning content suitable
for children of different ages, and generate different questions according to
different learning focuses.

##  8\. Conclusion

In this paper, we introduce Storypark, a child-story creation system that
emphasizes core story values through the use of a question-scaffolding
feedback framework. The storytelling system should adopt an interactive model
of ”Question-Feedback-Story Generation”, presenting open-ended questions to
elicit narrative contributions from children. Based on children’s
contributions and the story framework, the system should expand details to
support further story development. The storytelling system should augment the
ability for children to create stories through voice by incorporating drawing
features for visual completion, providing additional visual cues for children.
The system evaluation revealed that Storypark supports creative abilities for
story understanding, and is perceived as a pleasant partner accompanying the
children.

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