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Bringing Stories to Life in 1001 Nights: A Co-creative Text Adventure Game Using a Story Generation Model


Abstract and Figures

How can the stories we tell be turned from abstractions in our own minds into concrete elements in a digital environment that we can interact with? To immerse everyday storytelling into digital interactions, we created a game that turns entities in a story into digital assets that have functional roles. Taking the classic folklore as inspiration, we created 1001 Nights, a co-creative, mixed-initiative storytelling game using an existing AI creative writing system. In this game, Shahrzad (driven by the player) tells stories through a dialogue interface, while the King (driven by the AI model) continues the player’s story in turn. Text from the story is used in the game mechanics, so that if the player enters keywords such as ‘sword’ and ‘shield’, they are turned into equipment that can be used in battles. Players who are more engaged with the game, measured by the length of their inputs, are rewarded with better achievements. The game aims to facilitate player engagement and creativity through natural language interactions in an empowering setting. This paper presents the game design, a breakdown of the development process and an analysis of user data, including instrumented gameplay data from 2055 players and comments from 422 players. The player feedback indicates that they enjoyed the creative interactions, the game mechanics and the narratives they constructed.KeywordsGame AIIntelligent narrativeConversational agentGame designNCP-player interactionsInteractive storytelling
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Bringing Stories to Life in 1001 Nights: a
Co-creative Text Adventure Game Using a Story
Generation Model
Yuqian Sun1˙
ID , Xuran Ni2˙
ID , Haozhen Feng3˙
ID , Ray LC4˙
ID , Chang Hee Lee5
ID , and Ali Asadipour1˙
1Computer Science Research Centre, Royal College of Art, London, UK
2Unaffiliated, London, UK
3New Drama
4School of Creative Media, City University of Hong Kong, Hong Kong
5Affective System and Cognition Lab, KAIST, Daejon, South Korea
Abstract. How can the stories we tell be turned from abstractions in
our own minds into concrete elements in a digital environment that we
can interact with? To immerse everyday storytelling into digital interac-
tions, we created a game that turns entities in a story into digital assets
that have functional roles. Taking the classic folklore as inspiration, we
created 1001 Nights, a co-creative, mixed-initiative storytelling game us-
ing an existing AI creative writing system. In this game, Shahrzad (driven
by the player) tells stories through a dialogue interface, while the King
(driven by the AI model) continues the player’s story in turn. Text from
the story is used in the game mechanics, so that if the player enters key-
words such as ‘sword’ and ‘shield’, they are turned into equipment that
can be used in battles. Players who are more engaged with the game,
measured by the length of their inputs, are rewarded with better achieve-
ments. The game aims to facilitate player engagement and creativity
through natural language interactions in an empowering setting. This
paper presents the game design, a breakdown of the development pro-
cess and an analysis of user data, including instrumented gameplay data
from 2055 players and comments from 422 players. The player feedback
indicates that they enjoyed the creative interactions, the game mechanics
and the narratives they constructed.
Keywords: Game AI ·Intelligent Narrative ·Conversational Agent·
game design ·NCP-Player interactions ·Interactive storytelling
1 Introduction
Humans are fundamentally storytellers. From advancing in our careers to making
pepperoni pizzas, stories infuse every part of our lives. The ability of machines to
2 Y. Sun et al.
Fig. 1. (1) Shahrzad, the player character, who has a magical ability to turn language
into reality. (2) The storytelling phase, where the player writes stories with the King,
an AI character. Weapon words like ‘sword’ can be turned into real in-game weapons.
(3) The turn-based combat phase, where the player can fight with the king in battles.
(4) The printer prints the story when a weapon word is triggered. This matches with
the core concept of the game: bringing storytelling into real life.
generate coherent text has allowed stories to be told in new ways by mechanising
the writing process, through collaborative writing tools [1,2,3] or even directly
talking to fictional characters [11,12]. Can text-based dialogue between human
and machine be used as part of the game mechanics? As a metaphor for the
storytelling process, we turned to the story of Shahrzad, who determined her
fate by telling stories in real life. We created a game that uses the conceit of
storytelling akin to The Thousand and One Nights to motivate real-life writing
of stories.
Inspired by the classic folklore, we created the game 1001 Nights 6(illustrative
screenshots shown in Fig. 1), a co-creative, mixed-initiative storytelling game
driven by an existing AI creative writing system. The core concept is ‘bringing
storytelling to real life’ in game form: entities in storytelling are not just words
and descriptions but can be turned into real assets to change the reality of a
video game setting.
In this game, Shahrzad (controlled by the player) uses a dialogue interface
to tell part of a story, and then the King (driven by the AI model) continues the
player’s story in turn. When the King’s continuation contains weapon keywords
like ‘sword’, ‘knife’ or ‘shield’, Shahrzad can use her special ability to turn words
into real weapons and use them to fight with the king, creating game mechanics
out of the player’s own writing. This leads to an alternative ending of the original
6The game is available for download at:
Bringing Stories to Life in 1001 Nights 3
story: the female storyteller and heroine, Shahrzad, defeats the tyrannical King
and puts an end to his heinous crimes.
With this game, we expanded existing creative writing tools to create a
playable storytelling experience in a familiar narrative setting. We believe that
combining natural language interactions with a classic story can help players to
explore and engage more in the game by expressing themselves. The efforts they
put into imagination and creativity are rewarded with positive and adaptive
content generation by the AI model.
We showcased a Chinese version of this game in several art exhibitions and
received 12030 records of story inputs from 2055 players. This paper aims to
investigate if the AI system can encourage players to contribute more collabora-
tively through engagement. The results demonstrate that those players who are
more engaged (measured by the number of their inputs) in the game are rewarded
with better achievements, as intended. Comments collected from winning players
(n=422) show positive feedback towards various aspects of the game, including
the game mechanics and the stories created. Some of their feedback also shows
a cultural connection through creative work: some players expressed their own
interpretations of characters in the folklore, and were able to include characters
and plots from their own cultural backgrounds.
2 Related Work
2.1 Natural Language Processing
Previous studies have investigated the use of natural language processing (NLP)
for many different applications, including creative tools [1,2,3]. Some projects
have developed collaborative AI writers focused on specific genres, for example
Shelly [4], a crowd-sourced horror writer.
Similar approaches have been used in academic research for content gen-
eration. Murder mystery generation [5] generated murder mysteries for adven-
ture games, using structured information about real-world people mined from
Wikipedia articles. Designing for Narrative Influence [6] trained a language
model to generate micro-fiction that promotes sustainable public health guide-
lines. Martin et al. [7] presented a series of experiments that connected ancient
procedural techniques to modern technologies like language generation models.
Other studies have applied NLP to dialogue systems. Scheherazade’s Tav-
ern [8] and Prom Week [9] tried to develop deeper NPC interactions for a natural
social simulation experience. Talk to Ghost [10] adapted Shakespeare’s work to
improve high school students’ interest in reading by turning stories into interac-
tive conversations with virtual characters.
In this work, we describe a hybrid experience that sits between creative writ-
ing and a game. Some of our main influences are CharacterChat [11] and Ban-
terBot [12], dialogue systems that allow writers to talk with characters they
have created. This extends writing assistance to an intelligent agent, turning the
context of the interactions into a more familiar social setting. We designed the
4 Y. Sun et al.
dialogue interface in 1001 Nights as a special scenario: two people telling each
other stories. Through this, we hope players can easily understand the narra-
tive context, and overcome a barrier to creativity documented by Kreminski and
Wardrip-Fruin [13]: the fear of the blank canvas.
2.2 Game Interactions Using NLP
Through the use of NLP, emerging text adventure games give players more con-
trol over games compared with traditional games, which give players a limited
number of fixed choices. For instance, Interview With The Whisperer [14] and
Mystery Of Three Bots [15] let players explore mysterious stories with natural
language text input through Semantic ML, a tool for semantic analysis developed
by Google [16]. Fraser et al. [17] developed open-domain social conversational
AI using emotion detection. In recent years, experimental games like AI Dun-
geon [18] even allow players to fully generate their text adventure with natural
language input. The main goal of such games is to enhance the game playing
experience by providing an immersive and engaging experience, similar to Sali’s
work [19], which has shown that natural language interfaces, while difficult to
use, can reward players with high levels of engagement and enjoyment.
By creating conversational interactions through free-input dialogue systems,
NLP has also been used in parts of commercial games such as KuileiXi [20],
Event [0] [21] and Bot Colony [22]. These games use a natural dialogue system
as the primary means to push the plot forward.
2.3 Natural Language Generation in Stories
Among these NLP-driven projects, only a few have used a natural language gen-
eration model, for example personaChat [25] in CharacterChat [11] or OpenAI’s
GPT-3 in AI Dungeon [18]; most other projects have implemented NLP for a
specific task like parsing player utterances into logical statements [8,26] or find-
ing the closest response from a database [14,15]. The main reason for this is that
the use of a natural language generation model risks producing content that is
out of topic, and these projects need to find a balance between player freedom
and content quality. Accordingly, even when players can use natural language
input, these games set very fixed storylines and backgrounds that cannot be
intentionally changed by player inputs. Off-topic input will either lead to con-
fusing responses, which are frequently discussed in the player community of AI
Dungeon [27,28,29], or get limited by the customised module, like in Facade’s
Global Context Pool [30], which tried to maintain players’ suspension of disbe-
lief (their belief in the fictional story for the sake of enjoyment) even when their
input was out of bounds. Another example is that, in Scheherazade’s Tavern [8],
when a player mentions a topic that is not in the knowledge module, the AI
agent will repeat a word and then change the topic. These control mechanisms
are methods to ensure output quality.
This led us to think about the possibility of letting players decide the game’s
plot and generating mechanics corresponding to the narrative framework of their
Bringing Stories to Life in 1001 Nights 5
story. To avoid quality pitfalls faced by other games, we set a clear goal the
player must lead the AI king to mention weapons to restrict the output and
keep it on topic. We assume the natural language model’s creative ability has
space to improve under the game environment: when it can generate adaptive
content according to player’s input, and let players’ choices define components
of the story (such as equipment and scenes), the full gameplay will become more
dynamic, and bring co-authored creative artifact in the same time.
2.4 Dynamic Feedback Beyond Text
In games based on NLP, it is difficult to give dynamic feedback beyond the text.
The text can adapt to the player’s natural language input, but the rest of the
game cannot. It is very time-consuming for creators to prepare assets, such as
character animations and background scenesery, that are synchronous with text
output. For instance, in the development of Facade [30], two authors spent two
years preparing the character reactions and assets for a 20-minute game with a
single scene [30]. Some projects have started to use other AI generation models
to provide adaptive content, like the GAN-generated images in AI Dungeon [18].
Our focus is on the text modality, but our contribution is to map text to another
part of the game world: equipment. We were inspired by word-typing [31,32]
games, where players must quickly type specific words to release character skills.
However, these games do not have semantic relationships between the words and
the skills or world environment, while keywords in 1001 Nights will always be
part of stories and created through human-AI interactions.
We designed game mechanics to create a rich AI system: understanding the
player’s actions and responding intelligently, through which a player can attempt
many different strategies in the game and find that they are equally supported
by the system [33]. Accordingly, with weapon words as the main target, we
can map infinite creations from players to limited instances, and then we can
provide dynamic interactions with prepared assets, including 3D models and
visual effects when a keyword is triggered.
3 Game Design
This section presents the design and development of the game.
The game is made up of two parts: storytelling and battles. Fig. 2 illustrates
the game mechanics: the player needs to keep telling stories to lead the King
to produce story continuations that mention the important items for battle. In
the first phase (Fig. 2 bottom left), Shahrzad (driven by the player) and the
King (driven by the AI model) take turns continuing the story. In this game,
Shahrzad has a special ability to turn words into reality: when another person
utters keywords like ‘sword’, ‘knife’ or ‘shield’, those items materialise and drop
to the ground. The player’s goal in this phase is to lead the King to tell more
stories that contain keywords and collect weapons.
6 Y. Sun et al.
Fig. 2. (Up) Storyboard of the gameplay. (Bottom) Game process
After collecting enough weapons and pieces of armour, the player can enter
the turn-based battle phase (Fig. 2 bottom right) to fight with the King. In this
phase, the player can use the weapons collected during the last phase to fight
the King. The Player’s goal in this phase is to beat the King and free Shahrzad.
This is a different ending from the original folklore.
We attempt to combine all components of the game into a coherent expe-
rience. The story background links to the mechanics: Shahrzad needs to create
stories to survive. The AI system allows the player to be creative and explore
different parts of the story.
3.1 Game Art
To encourage players to focus on the gameplay, we use a pixel art visual style.
Most pixel art games, like Terraria [34] and Red String Club [35], use 2D hand-
drawn images for all game assets. However, to save time while keeping the flu-
ent aesthetic, we used 3D-to-2D techniques as in A Short Hike [36]. With this
method, there is no need to manually draw character animations; instead, we
used existing action animations from open source libraries like Mixamo [37]. The
Bringing Stories to Life in 1001 Nights 7
resulting game art is still in the traditional 2D pixel art style but with a reduced
development time. The low-resolution art that blurs character faces also matches
the feel of the ancient story and leaves room for the players’ imagination.
3.2 Battle System
The aims of the battle system (illustrated in Fig. 3) are (1) to make the game
interesting and challenging and encourage players to write more stories; and
(2) to balance the difficulty a player should not feel that it is too easy or too
hard to win. For these purposes, we decided to require players to write at least
two stories that trigger valid responses to win the game, that is, a player needs
two attack weapons to win.
However, if two attacks are enough to defeat the King, a player will only click
twice to win the battle. Hence, the time spent in this phase will be too short.
The battle phase needs to be exciting for players, and they may meet failures
before the victory. Thus, we implemented the following rules:
1. Each weapon collected in phase one can only be used once. Once used, this
weapon will be replaced by a fist (punch) icon. The fist can also be used for
attacking, but it deals much less damage. Shahrzad can only attack with a
punch when she has no weapon in her hands, as in real life.
2. Some equipment is for defence rather than attack, such as ‘shield’ and ‘ar-
mour’. They cannot hurt the king but enrich the game experience by pre-
venting king from dealing damage during his turn. This effect is also realistic.
3. The maximum inventory space is four. Equipment exceeding this limit is
not saved for battle but is recorded in the backend to confirm the player’s
achievement of obtaining weapons. If the inventory is not full, the empty
spaces are replaced by the first in the battle phase.
By these rules, once players collect two attacking weapons (like ‘sword’ and
‘dagger’), they can use them and then punch with their fist twice. These combi-
nations will lead to victory. If players only have defending equipment, they can
not defeat the King, since the only method of attack is a punch.
Fig. 3. Battle in game
8 Y. Sun et al.
3.3 AI Development
Demo 1 and Keywords We designed four offensive weapons the sword, knife,
blade and dagger and two defensive weapons the shield and armour. These
are keywords that yielded over 50 matching paragraphs when we searched for
them in in the Guttenberg Short Story collection [38], a dataset extracted from
the portal of Project Gutenberg, a library of over 60,000 eBooks. Meanwhile,
these are also weapon words that match with the ancient narrative setting of
1001 Nights. Within the original 94 MB Gutenberg short story dataset [38], we
located each paragraph with a matching keyword and extracted the previous
five paragraphs, yielding a smaller 16.24 MB dataset. In the first prototype, the
King’s AI model was trained on the extracted dataset using the GPT-2 medium
model [39] with 6000 steps, so that it would be familiar with common tropes in
various adventure stories. The text generation temperature was set to 0.8. Here
are several 200-word examples of the results:
Example 1. Player: The princess tried to find her magic sword in the forest
AI: . . .and at last through sword at once hid it in her breast; whereupon
the beast a common creature pretended to understand all she said, and
then proceeded to strangle her for a good while. The princess fought with it, till
the beast relaxed, took its sword, and set off home. ‘They are the daughters of
Snaxen,’ said the young man, passing by to his friends.
Player: ‘Do you know them?’ The princess asked.
AI: ‘I knew them,’ answered Javelin. ‘I was called about twenty years ago,
and the King of Thessaly picked me up for the defense of the south against the
armies of the Chilkatrachels. He gave me orders, and I carried them, and soon
learned to toss my sword, and to wait for the return of the Sun, so that I might
meet him face to face in the courtyard of my honor. . .
Stories generated in this manner are full of leaps and bounds, and develop
logical holes as the story goes on. Although the training model might be improved
using a larger dataset, this process would be time-consuming. Even if the model
were improved, the story-continuation ability may not be flexible enough for
modern players. Since the original story collection includes many old books, the
model may struggle to understand players when they write in a more modern
style. This may result in low-quality and confusing stories. To fix this problem,
we sought alternative solutions.
English and Chinese Demo 2 We used [2] to reinforce our game
design. is a creative writing platform using a modified transformer
(a self-attention multi-layer neural network) model trained with high-quality fic-
tion. Both its English and Chinese datasets consist of open access fan fiction and
ebooks on the web, and both datasets are about 100 GB in size. This platform
has over one million users of the Chinese version and 200 thousand users of the
English version. Although the model is not suitable for all tasks (e.g. writing of-
ficial documents or code), it is well suited to story generation. To use the model,
Bringing Stories to Life in 1001 Nights 9
it is only necessary to call the application programming pnterface and send the
title and prompt to’s server[45].
This generation model with a large dataset was able to produce results similar
to Demo 1, except for the keywords part. To implement our game mechanics, we
designed the structure of requests for the model as in Fig. 4. With this design, produces flexible stories that closely correspond to player input. The
past five inputs are added to the prompt to ensure fluency. Records are refreshed
when the player starts a new game or moves to the battle phase. When King’s
response does not include a keyword, Shahrzad sends a notification message to
provide a hint to the player that they should tell a more relevant story. This
helps to relieve confusion and the Tale-Spin effect [40], in which a system makes
people feel it is less intelligent than it actually is due to insufficient explanation
of the underlying processes.
Fig. 4. Requests for the AI model
The player is allowed to mention keywords (like ‘sword’) in the input phase.
This may increase the chance of obtaining stories that mention the corresponding
weapons in some way, but it will not guarantee it. In contrast, an input that
creates a suitable context without a weapon word can still lead to a valid response
that contains keywords. Fig. 5 shows an example of this. However, as illustrated
in Fig. 6, due to the randomness of the AI system and the limited word list,
a player may mention weapon words repeatedly, but may still fail to trigger a
weapon. Some players may ask questions in conversations with the king, as shown
in Fig. 7, but the king will still give adaptive responses. This can sometimes make
the stories appear out of context.
10 Y. Sun et al.
Fig. 5. Player record with a triggered weapon
Fig. 6. Player record with no triggered weapon
Bringing Stories to Life in 1001 Nights 11
Fig. 7. Player record with out-of-context inputs
All these examples, translated from Chinese, are taken from play test records
during exhibitions, which are discussed in the next section.
4 Evaluation Study
4.1 Experimental Setup
We were invited to showcase our work at three exhibition sites in China, to re-
search a range of players and collect feedback. All three were in different cities,
but they all shared shared the same digital and analogue setup, including a vin-
tage monitor (to match the ancient setting of the story), a printer, and a work-
station using the Windows 10 operating system. A tutorial leaflet (a screenshot
of the help page in the game) was on the table for players to read.
A mini-printer was used during a two-month offline exhibition in Beijing to
emphasise the concept of ‘invading language’ and to improve public engagement.
Each time a keyword was triggered, the current piece of the story was printed
out. In this game, the keywords are the materialised language that becomes part
of the ‘reality’, and to players, the printed text is tangible output from the game
to the real world. This feature encouraged people to spend more time at the
exhibition since they could keep a hard copy of their stories.
12 Y. Sun et al.
Fig. 8. Left: Exhibition setup Right: Tutorial leaflet for players
4.2 Opening and Tutorial
All players were informed about data collection for research use before they
entered the tutorial. In the tutorial, players were informed about the game dy-
namics, for instance, the click and collect mechanism using displayed keywords
‘sword’ and ‘shield’. Not all valid keywords were revealed to players. One reason
was to let them focus on a more specific instruction: to write about the sword
and shield. Another reason was to encourage exploration finding the valid
keywords is also part of the gameplay.
Fig. 9. Left: Opening screen Right: Screenshot of tutorial
5 Results and Findings
Since the core system in the game is the story generation model, this inevitably
adds randomness to the results, which cannot be fully limited by rules. When
analysing the player data, we aimed to confirm if the AI system can encourage
players to engage and contribute more collaboratively in play.
Bringing Stories to Life in 1001 Nights 13
Following the aim of this game collaborative storytelling with clear goals
‘engagement’ can be regarded as how much time and energy players want
to spend, and ‘contribution’ means the quality of their inputs: whether the
sentences make sense or not and whether they are directed towards obtaining
weapons. Accordingly, the AI system should give positive feedback to players
in the form of responses containing weapon keywords, which become the items
that lead to success. If the AI system works effectively, then when a player
engages and contributes more in collaborative storytelling, they should receive
more weapons, making them more likely to win the game.
When evaluating player contribution, we met some difficulties. We had large
amounts of player data (2065 players with 12030 inputs), so it was not feasible
to evaluate the quality of all story content. Additionally, since the data were
collected during exhibitions, the playing time might have been influenced by
unpredictable factors, like the queue length or the number of visitors on that day.
Thus, we decided to use average inputs per play to evaluate player engagement,
and compare this with their achievements in the game. To be specific, ‘play’ is
defined as reaching the end: success in defeating the king, failure to defeat the
king, restarting or ending the game. Further evaluation like thematic analysis of
stories and play tests without time limitations are left for future works.
To evaluate the level of achievements, we categorised players into three
groups, as shown in Table 1: non-winner (G1, n=299), journeyman (G2, n=1106),
and winner (G3, n=650). These groups are independent of each other, but the
level of progress raised from G1 to G3: non-winners (G1) did not collect any
weapons or win the game, journeymen (G2) acquired at least one weapon but
did not win, and winners (G3) defeated the king in one or more plays. All players
had a chance to familiarise themselves with the game with printed screenshots
and integrated tutorials prior to the game, with an identical experimental setup
in all three locations.
Table 1. Grouping players by their achievements
Definition G1 G2 G3
Collected at least one weapon No Yes Yes
Defeated the King at least once No No Yes
5.1 Analysis
This study aims to investigate the impact of engagement in storytelling (average
inputs per play) on overall achievement level (from G1 to G3) made by players
and to understand any potential trends between the groups. Hence, a Levene
test is used to check the homogeneity of variances among engagement of each
group, F (2, 2055) = 32.02, p <.05. We believe that players that make little
progress on average are more likely to experience frustration, meaning that the
distribution of inputs per play in G1 (M = 2.59, SD = 2.05) is more influenced
14 Y. Sun et al.
by the players’ propensity to lose patience. Meanwhile, the distribution of inputs
per play in G2 (M = 4.51, SD = 3.61) and G3 (M = 6.30, SD = 3.85) may also
be influenced by the players’ luck in finding the right inputs. This distinction
may explain the difference in variances. Fig. 10 shows the distributions in each
A non-parametric Kruskal-Wallis test, as an alternative to one-way ANOVA,
is used to evaluate the correlation between player engagement and overall achieve-
ments. Players’ achievements in this game were reported to be affected by en-
gagement, H(2) = 328.295, p <.05. The results show that players are more
likely to achieve a better outcome by making more contributions to the story-
line. Also, a positive trend (shown in Fig. 11) is observed and reported by the
Jonckheere-Terpstra test. Since the shape and variability assumption is violated,
the obtained Welch’s adjusted F ratio was used F(2, 1012.54) = 191.85, p <.001.
Hence, we can conclude that at least two of the three groups differed significantly
in their overall achievements in this game.
In general, the randomness of story generation sometimes influences the
gameplay: high engagement (more inputs per play) does not guarantee victory,
and fewer inputs may also lead to enough valid keywords for the player to win.
However, as shown by the previous analysis, this randomness does not impact the
overall performance of game design. In conclusion, the game encourages players
to engage in storytelling: the more they engaged, the better achievements they
would reach in the game.
Fig. 10. Average input per play distributions per group
5.2 Comments From Winners
Since this game was only exhibited in China, although it received some feedback
in English, the following section will only focus on feedback in Chinese. Only
players in G3 (winners) were allowed to leave feedback after victory for the
following reasons:
1. Players played this game during an exhibition, so not all of them had enough
interest to reach the end. Sometimes there was a queue to play this game.
Bringing Stories to Life in 1001 Nights 15
Fig. 11. Positive trend in overall achievements by average contributions made
2. We wanted to encourage players who were defeated to try again until they
achieved victory, so that they went through the full gameplay. If we showed
the ending page (Fig. 12 left) to all players, including ones who were defeated
by the King, they may have regarded it as an ending and left.
3. We assume that players who were patient enough to win gained a deeper
experience in this game, which is helpful for us to identify its weaknesses.
We received winners’ records (n=650) and removed those who did not leave
comments (n=226). We also removed two records from players who met technical
difficulties during the experience (the printer was not working).
Finally, with thematic analysis, one of us developed a set of initial codes.
After discussion with colleagues, the rest of the remaining feedback (n=422) was
identified and classified into nine categories (shown in Fig. 12 right). In future
work, we hope to include multiple coders and inter-rater reliability calculations.
Fig. 12. Feedback page appears when a player achieves victory (left), categorised
feedback (right)
16 Y. Sun et al.
Fig. 13. Comments categories
General Positive feedback like ‘Good game (P10)’ or ‘Interesting (P60)’ are
in the praise category. This type makes up the largest share of results (29.1%,
n=123). Feedback in other categories shows various focuses. 10.7% (n=45) of
players made suggestions. Together with 6.6%(n=28) of players who left in-
quiries, 8 of them expressed willingness for further development and publishing
on a commercial game platform like Steam. These comments made us notice
several perspectives that we ignored before. About half of the players (n=25)
who left suggestions, and some from the inquiry category (n=7) asked for the
inclusion of more weapons. Although some of them (n=5) mentioned weapons
like guns that do not fit into the narrative setting, we do recognise the benefits
of improving weapon choices and better player guides. Several players (n=13,
3.1%) were unsatisfied with or disliked this game, which is expected for a game
at an early stage of development.
Immersion in the Game and Story 10.4%(n=44)players shared personal
feelings towards gameplay, like ‘we should always believe in love and magic
(P326)’, ‘It’s interesting and immersive. Players are invited to save a charac-
ter and feel strength (P511)’ and ‘we choose free rather than love (P261)’. Some
also expressed thoughts about AI: ‘Humans reach consensus with AI (P586)’ and
‘Humans are those who think beyond AI (P561)’. These suggest our game can
provoke reflections about freedom, strength and the agency of AI. P624 gave a
good summary that matches our motivation: ‘It’s interesting. The game mechan-
ics of hidden triggers also brought ‘freedom’ to players, not only to Shahrzad.’
The more interesting fact is that nearly one-third of this group (n=14) shared
their impressions about the king, like ‘This king sounds like a gastronome (P564)’
and ‘The king can become a good writer in his next life (P542)’. A player even
said ‘There is not only betrayal and injury but also warmth and protection, in
the hope that the defeated King in prison can understand what he has, treasure
what he has, do not ask the past (P491)’. Even though we did not add any per-
Bringing Stories to Life in 1001 Nights 17
sonal lines to the king, some players showed empathy toward this character. To
some degree, this feedback shows the potential attraction of intelligent charac-
ters driven by NLP technology. A character can give reasonable responses even
without detailed design work, and the player’s interpretations can fill the gap in
the story. The players’ feedback was more varied than expected. We expected to
receive general praise and suggestions the most surprising result is that 9.5%
(n=40) of players talked specifically about the stories they created. Most of them
(n=29) mentioned the characters they included in their stories and described the
plot in detail, like ‘Summon the beasts’ success! ! The black cat is turned to the
witch, and it turns into a magic hat (P395)’ and ‘Princess Li finally defeated the
evil emperor with high ideal (P230)’. This provides evidence that many players
are highly engaged in the stories they created.
While some players were immersed in the stories they created, some players
receive more pleasure from their victory. 5.4% (n=23) of players gave highly pos-
itive feedback describing their feelings of victory, like ‘AI cannot defeat human
Shahrzad! (P30)’ or ‘I am very smart! I’m the smartest princess(P138)’. In gen-
eral, this feedback suggests our game can bring both an entertaining experience
and creative collaboration between humans and AI.
Cultural Connection Among players who commented on the story they cre-
ated, many of them were inspired by personal interests that matched with the
mysterious background, like ‘I want to lit the fire of renaissance in the darkness
(P234)’, ‘No matter what, Sword Soul, Shield Sprite and Gun Sprite will always
be good friends! (P637)’ and ‘Mountain Boots Puss and Iliad, Hit, the three live
together forever and inherit the throne of Snow Mountain. (P540)’
Since the testing was performed in exhibitions in China, some of the players
put aspects of their cultural background into their stories, which became cre-
ative artifacts through human-AI collaboration that show possibilities in cultural
blends. For example, ‘Awesome! How to play the sequel? I want to chat more
with the old ancestor Ye who fought with the shovel in Luoyang and the witcher
who fought with the lich...What happened to the Prince? (P350)’ ‘Shovel in Lu-
oyang’ here is one of the most important tools in Chinese archaeology, and is
usually mentioned in grave robbing stories. Another player (P148) put a charac-
ter from pop culture in the story: ‘A Liang, the youth who left the factory, can
beat the king.’ This character ‘A Liang’ comes from the pop song ‘About Life’ by
the ‘Wutiaoren’ [41], a popular band in China who are famous for their attention
to the current situation of Chinese rural youth and for the strong humanistic
feelings in their music.
Reflection on Reality It was observed that a few players (n=7, 1.7%) even
connected this game to their experiences and feelings in real life, like someone
who felt encouragement from Shahrzad: ‘I am a student, I also want to be free,
be as brave as the heroine in-game once (P451)’ and ‘I love this world, I also
want to create valuable works. (P46)’
18 Y. Sun et al.
We also received very detailed feedback about previous personal experiences:
‘Thank you for reminding me of my favourite game I played with my friends in
class during my reading time. In those days we used to write a story on a large
piece of scratch paper, one at a time. No one knows what will happen next, and
we tend to avoid stories that fall into a rut, creating more and more mysterious
adventures for it. Good memories. That’s a good game. (P11)’ This feedback
suggests this game may have potential ability as an educational game for story
Fig. 14. Exhibition photos. Left: A 11-year old boy kept playing for half an hour
Right: Official photo from exhibition
6 Discussion
This study investigates how the AI system can reward players to motivate col-
laboration in writing stories. The results show a significant difference between
at least two groups’ overall achievements based on the level of engagement in
storytelling. Players with higher engagement were more likely to reach improved
achievements in the game. This is aligned with the hypothesis that our game
design did encourage players to explore and collaboratively interact in the game,
and the AI system could reward them with creative feedback.
Even when we did not ask players to rate their experience, in comments from
players (n=422) who won the game, players showed a high level of enjoyment
and interest throughout the game, where they contributed their own stories to
be part of it. Supported by the AI system, the same character and interface may
bring different stories and experiences based on the players’ personal choices,
and they can immerse themselves in the game, exploring their own interests.
Similar to previous studies [42], the unexpected but logical text generated by AI
may make the story more exciting than the player’s intention.
Players expressed their own interpretations of characters in the folklore and
were able to include characters and plots from their own cultural backgrounds.
For them, the king could be a coward, a peace lover or a gastronome, and these
Bringing Stories to Life in 1001 Nights 19
are reflections through the creation, rather than the line the creators set. They
could introduce a character from a pop song in their story, or link the game to
current social news. This suggests a potential chance to alleviate the creator’s
burden to develop games. Players’ autonomy and imagination may fill in the gaps
that developers leave blank. This is similar to the finding of Aljammaz et al. [8]
that a player may view the repeated responses as the NPC’s own personality.
7 Limitations
The creators faced common barriers in developing 1001 Nights. Like similar
studies on dialogue interfaces, we found that open dialogue systems are a double-
edged sword. They contribute to a sense of freedom but face the risk of going off
track, and to limit that requires a large amount of authoring and design work.
Current weapon keywords are specific and limited. In future work, we plan
to use semantic similarity detection to extend the range of valid keywords. For
instance, in WebVectors [44], the sword is similar to the scimitar, rapier and
broadsword. Less similar but closely related words, like hilt and scabbard, may
become fragments that can be used to form actual weapons later.
Furthermore, due to the large playtest data, the current AI system was not
able to evaluate the quality of player input. Consequently, we could only evaluate
player performance through engagement (number of inputs per play). In future
we hope to analyse the quality of inputs and responses received from the King.
To see the impact of the game environment, we also hope to analyse how the
results differ for players who directly use the story generation model and those
who play the game in a version without keywords. In future work, participants
will be able to download and play the game in their preferred environment,
without the time pressure of physical exhibitions.
Meanwhile, when the player input includes some components that do not fit
well in the setting of The Thousand and One Nights (like ‘computer’ or ‘rifle
gun’), the king can still continue the story, which may reduce player immersion,
since an ancient king should not know about modern technologies. This could be
improved by future enhancements, like keyword detection or neural classifiers.
Overall, as a game in its early stage, we received encouraging results. We
started with the concept of ‘bringing storytelling to life’, and it was surprising
to see that many players could naturally blend their own life into the game. We
also suggest future investigation on using NLP models like OpenAI’s GPT-3 [43]
in more storytelling games.
8 Conclusion and Future Work
To extend storytelling to real-life contexts beyond the language, we created the
game 1001 Nights, a co-creative storytelling game that leverages story writ-
ing into actual game mechanics, based on an existing story generation model.
We have shown that 1001 Nights facilitates player engagement and creativity
through natural language interactions in a well-known folklore setting. Our data
20 Y. Sun et al.
suggest that higher player engagement generally leads to better achievements in
the game, which demands further investigations.
Potential extensions of this research include multiple coder thematic analyses
of players’ stories and comments, and asking them to evaluate their engagement
and interest on a Likert scale. This is key to evaluating the quality of inputs via a
hybrid approach using NLP algorithms like text perplexity and domain experts.
The game design could be improved to enhance replay value. For example, a
weapon index could be added that documents the stories behind the triggered
items. Furthermore, with emerging text-to-image technologies like DALL-E[46],
it may even be possible to change the appearance of a weapon or character based
on the corresponding sentences or change the background scenery over the course
of the story. Beyond 1001 Nights, supported by text generation models, similar
mechanics that set clear goals for players could be extended to more games, like
suggesting a non-player character to spill a secret location that hides treasure.
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