WPPG: Fifty Shades of Personalization - Workshop on Personalization in Serious and Persuasive Games and Gameful Interaction
UMAP’17, July 9-12, 2017, Bratislava, Slovakia
An Adaptive Learning with Gamification & Conversational UIs: The Rise of CiboPoliBot Ahmed Fadhil
Adolfo Villafiorita
Fondazione Bruno Kessler (FBK-irst) CRG - GPI Research Centre Trento, Italy
[email protected]
Fondazione Bruno Kessler (FBK-irst) Trento, Italy
[email protected]
ABSTRACT
knowledge and raising awareness about a topic. Gamification and the use of game elements are increasingly focused on attitude or behaviour change in a desirable direction, such as towards a more healthy lifestyle [4, 8]. Although with the increasing research studies on personalisation of serious and persuasive games and gameful interactions, the true ability of such application is yet to be explored. Recent advanced in AI and conversational interfaces have shown new evidence of the value of using chatbots to provide users with personality factors to tailor the activity to user behaviour, need, and demographics [4]. Chatbots offer the simplicity and personalisation factor that outperforms many mobile, web and other forms of application. This is due to its intuitive nature that is based on text or visual communication and have no UI complexity as in GUIs. Integrating gamification into such technology will introduce both the simplicity and personalisation aspect into the developed technology. This new approach focuses on more optimistic takes rather than solving problems. It uses simple conversation and game elements and mechanics as visualisation while communicating with the user. We propose an adaptive gamification approach in learning about healthy diet and food waste management for kids between 8-14 years old. This is important, since it assesses students to increase awareness about healthy diet and physical activity and how to manage the portion sise and mitigate the waste associated with food [5]. The game proposed ‘CiboPoli’, was initially created by ICT4G unit - FBK1 for school children to increase their diet and food waste knowledge. We describe the analysis and design aspect associated to enhance and increase the personalisation and engagement aspect of the game among school children. The unique contribution of this approach is integrating the game mechanics within the chatbot system. To our best of knowledge, the proposed system is the first in which gamification and conversational interfaces are integrated to provide a meaningful game interaction. We envisage this study to broaden the scope of gamification and conversational interfaces to combine and apply in other areas and help to provide a mindful interactions to users. The study will highlight the importance of healthy food education for kids, and the role of conversationalAI with gamification in user engagement and adherence towards healthy diet education. In addition, we will present the design and integration of game mechanics into the chatbot application that will mainly be based on Telegram Bot Platform2 . We will discuss the game dynamics and the user-bot interaction throughout the game play. We hope this paper to be a useful guideline for gamification and chatbot developers who intend to use such technologies for user engagement and lifestyle promotion.
Gamification in the era of chatbots is a novel way to engage users with the chatbot application. When developing a gamified chatbot system, there are factors related to user types (ages, gender and others) that we should consider to effectively integrate the game elements into the chatbot while targeting the right audience. In this study, we discuss the development of an educational chatbot game ‘CiboPoli’, that’s specialised in teaching children about healthy lifestyle through an interactive social game environment. The presented game is based on a paper prototype that we developed to teach primary school students about healthy diet and food waste management. The current approach will be more engaging and pose AI capabilities. This is still a work in progress and we plan to improve its design by incorporating additional components, such as dialog management module, user-specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, we tested the initial prototype with school students and found that it outperforms the paper version. Future work will focus on applying it to other domains and demographics.
KEYWORDS Gamification, Conversational Interfaces, Gameful interactions, Persuasive Technology, Personalisation, Healthy Diet ACM Reference format: Ahmed Fadhil and Adolfo Villafiorita. 2017. An Adaptive Learning with Gamification & Conversational UIs: The Rise of CiboPoliBot. In Proceedings of Adjunct, Bratislava, Slovakia., July 09-12, 2017 (UMAP’17), 5 pages. https://doi.org/http://dx.doi.org/10.1145/3099023.3099112
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INTRODUCTION
With the emerging learning technologies and persuasive approaches focused on improving knowledge delivery and learning process, the traditional learning approaches are left obsolete in adhering learners and engaging them into a learning process. Serious games and persuasive approaches are increasingly applied in imparting Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
[email protected]. UMAP’17 Adjunct, July 09-12, 2017, Bratislava, Slovakia. © 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-5067-9/17/07. . . $15.00 http://dx.doi.org/10.1145/3099023.3099112
1 ict4g.org
2 https://telegram.org/
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RELATED WORKS
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tricky lexical and grammatical rules that hone their skills at stuff like subject-verb agreements, but often neglect the more practical skill of making meaningful conversations. More often than not, students who go through conventional language courses also tend to be overly conscious of whether they have conjugated a verb correctly, which only diverts their focus from contextual meanings as they listen or speak in the new language. Duolingo, on the other hand, adopts a more naturalistic approach. Their apps and chatbots which earned numerous awards from app stores, publishers, and online influencers do away with untenable memorisations, focusing instead on giving users an immersive experience that encourages them to learn new words and understand grammatical concepts through association and contextual clues. To keep students motivated, Duolingo also deploys the key elements of gamification that help personalise user learning and keep their linguistic development on track. Applications that promotes behaviour change should deeply consider the simplicity aspect during the design and development of the product. In a different work by Fadhil et al., [4] we focused on developing an e-coaching platform by heavily relying on introducing simplicity in the interaction. The findings revealed such approach as effective, but has to be carefully applied not to be too simple. Discussion: The literature revealed few studies focused on integrating gamification and conversational-AI for educational purposes or promoting healthy lifestyle. The majority of studies were either focused on rewarding users for certain activities using some game mechanics and design techniques or were providing text based information to users with a chatbot agent. None have considered combining these two approaches to provide simplicity and yet engaging and effective way to educate users about healthy food choice and food waste management. Educating kids about these issues is hard, and often the available mobile or web applications are either complex or not engaging. Integrating gamification framework with conversational-AI platform will enhance their health knowledge and in long-term will prepare and adhere them towards a healthier lifestyle, which will promote a healthy life with no risk of chronic conditions, such as diabetes or obesity. There is a need to combine both the visual and textual context in a chatbot to provide it with powerful intelligence. Therefore, better algorithms are required to help chatbots accept, evaluate, and correct poorly constructed phrases.
A successful long-term education to adhere users towards a certain behaviour has become a growing necessity of modern times. This includes a successful communication between users or user-system. Emotions play an important role in e-learning environments. Previous studies have investigated the prediction of learners’ emotions using various features, such as acoustic-prosodic features, mouse movements, facial features, and body postures. In addition to these features, linguistic features are also useful for identifying learners’ emotions especially in text-based e-learning systems. A work by Mazur et. al., [7] proposed a free talking dialogue system designed for tutoring English language. The paper emphasise gamification principles, a notion of integrating game mechanics into non-game systems, applications and services in order to encourage user interaction. Finally, it underlines the importance of emotion recognition by introducing a potential foundation for verifying the assumption that certain words containing emotional content can improve learning [7]. Linguistic features are analysed for different types of emotions by collecting a text corpus of emotion sentences. Therefore, chatbots are a great candidates to perform sentiment analysis and extract emotional context from a text corpus. A work by Lin et. al., [6] described an advanced platform for evaluating and annotating human-chatbot interactions. The paper highlights the persisting difficulties in creating data-driven-systems as they need large amount of data for development and training. In the context of health and to provide educational support to patients, patient self-management support focused on encouraging patients to be knowledgeable about their illness and to be able to sufficiently look after themselves. Some key elements to this achievement include knowledge, motivation, self-efficacy, goal-setting, action planning and problem-solving. A work by Buranarach et. al., [2] described a personalised patient education framework aimed to provide personalised learning resource recommendations for patients. Specifically, resources are recommended based on patients’ goals and barriers. The framework allows a patient to define their goals and barriers in their profile. Learning resources help to support patient education that can promote them to achieve specified goals. In this framework, an ontologybased approach was used for modelling patient’s goals and barriers as well as learning subjects. Ontology allows patient conditions and learning resource metadata to be linked. Effective language learning tools as well as translators ‘human or bot’ remain in high demand across many fields and industries. From personal tutors who teach nonnative speakers a second language, to powerful applications like Google Translate and Rosetta Stone. One application that combined learning with gamification and chatbot is Duolingo3 , a free-to-use language learning platform continues to attract users, investors, and accolades since their launch in 2011. Duolingo recently launched conversational bots to compete in the tongue twisting industry of language learning. Conversational bots take the embarrassment and scheduling problems out of language learning. Instead of meeting with a tutor, you can chat with the everpresent, ever-patient Duolingo bots. Conventional language teaching generally takes learners through a rulesbased curriculum that focuses on grammar and other technical aspects of a foreign language. This approach (mercilessly) forces learners to memorise
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THE CIBOPOLI BOT DEVELOPMENT
Several studies have indicated the need for personalised gamification system to provide personalisation and social engagement among users. However, mapping user personality into a design elements is hard [10]. There is a need to find a balance between the game mechanics and chatbot AI intelligence. In all this new technology we must not forget the most important actor in this enterprise; that’s the human being who is expected to interact with the bot. There are several issues regarding the design and integration of gamification elements into the medium of chatbot and building great experiences and many conversation related issues with chatbot systems.
3 https://it.duolingo.com/
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3.1
The Gamification Layer
UMAP’17, July 9-12, 2017, Bratislava, Slovakia
associated with the system dynamics (exclude Disruptors). Hence, from Table 1, we can conclude the Socialisers and Achievers as the most suitable user types for our study.
The goal of this study is to empower children knowledge about healthy diet and lifestyle promotion, and increase their awareness about food waste management and reduction. The problem is educating kids about these issues, which is often hard. Moreover, available technologies pose limitations in terms of user engagement and personalisation. The proposed approach educates kids and enhance their health knowledge and, in long-term, it will prepare and adhere them towards a healthier lifestyle. With this work, we will adapt the Hexad model of gamification [10] to choose the game element suitable for our user types. This model helps with identifying user types and map them with suitable game elements. The framework enables accurate measures of user preferences in gamification. It proposes six user types that differ in the motivation degree by either intrinsic (e.g., self-realisation) or extrinsic (e.g., rewards) motivational factors. The user types are personifications of people’s intrinsic and extrinsic motivations [9]. Accordingly, the four intrinsically motivated types in the Hexad model are derived from the three types of intrinsic motivation from SDT4 , namely relatedness, competence, autonomy, and purpose [3] (see Figure 1).
3.2
The Conversational Layer
This is mostly related to the intelligence of the chatbot and the way it interacts with the user in a natural way, using text and visual means of communication. For this part, we will use the Telegram Bot Platform to create the CiboPoli chatbot channel, since this platform provides many design functionalities to use while working on chatbot development (e.g., custom keyboard). In order to add the bot intelligence related to NLP and natural conversation we will use the Microsoft bot framework. To make the bot interactive and more friendly when engaging with kids, we will rely on API.ai platform to add necessary bot intelligence (e.g., wisdom and funny). This makes a simple, intuitive to use, and engaging bot, since it provides competitive gamification leaderboard for the players and communicates with the user during the game play. When users signup to the chatbot, at first it presents the game and the rules to play. It answers questions about the service, tells jokes, and posts updates about new features.
3.3
The CiboPoli Architecture
The high-level architecture consists of the gamification and chatbot layer that interact to achieve a common sense goal. The system development will incorporate a leaderboard and points as the main game mechanics to engage users in the game play. The points are provided by how the user responds to the game quizzes about diet and food in general. At the end, the user who have the right combination of fruit, grain, vegetable, diary and protein as recommended by MyPlate [1] (see Figure 2) will acquires more points. The scoring mechanism will be harnessed along the game journey to motivate users and facilitate, evaluation. The architecture in Figure 3 resem-
Figure 1: Gamification User Types Hexad By Andrsej Marcsewski ©. The user types according to the Hexad model are: Philanthropists, Socialisers, Free Spirits, Achievers, Players, and Disruptors. In Table 1 [10], we list them and the game design elements suggested. This will help to address the motivations of each type and decide the type that fits our user demographics. Since the user demographic are kids and the purpose of the game is to motivate them to interact with the game and learn about healthy diet and food waste management, the game we envision is a social game where kids interact with each other and the chatbot whenever relevant. Moreover, the user should be intrinsically motivated and with no external motivations involved (exclude Players), the system rewards users by providing a way to compete and achieve a task (exclude Philanthropists), the system follows restricted rules during game play (exclude Free Spirits), finally, there will be no negative or positive rewarding
Figure 2: Choose MyPlate ©. bles the integration of gamification and chatbot system represented within CiboPoli. The student, while conversing with the chatbot, is
4 http://selfdeterminationtheory.org/
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asked to toss a diss and based on the outcome he decides the moves. While interacting with the bot, the student encounters quizzes about the amount of each food type that he has to collect to reduce waste and promote healthy diet.
Figure 3: The CiboPoli High-level Architecture.
3.4
Figure 4: The CiboPoli Conversational UI.
The CiboPoli Design
The game consists of steps designed in a decision tree-like structure. The player begins by tossing a dice and choosing a direction. The player can either collect an item (vegetable, fruit, protein, grains, or dairy), add product to basket to buy, trow a product away, or buy a product of type grain. At a certain steps, the player can celebrate his birthday, where other players have to provide him with an item as a gift. Moreover, to keep the food items balanced, the player can donate a food item to a friend. Finally, the player can eat at the pizzeria and collect dessert. At each step and based on players choice, the system will reward them with points. At the end, the one who have the right combination of food choice and wastes the least will acquired more points, and hence will be located first on the leaderboard (see Figure 4 for the CiboPoli Conversational Interface). During the game play players will encounter quizzes where they have to add to a plate the right combination of the food items, and hence get the right daily caloric value. The more the player repeats the less point they get. The chatbot converses with the player throughout their journey and provides them with hints and instructions on how the quizzes should be answered. The gamification will make the app engaging enough to make users want to complete all modules and stick with their learning plan even without the chatbot. However, the chatbots make the experience doubly rewarding. Moreover, unlike human tutors, chatbots won’t get tired or be judgmental as users practice their learning all they want, without fear of ridicule or embarrassment if they make a mistake. The chatbots we’ve encountered so far have unique, friendly and charming personalities. There’s Roberto the chef, Gabriela the cab driver and Susana the zoo keeper. By interacting with these characters, you learn a few terms that are relevant to their occupations.
3.5
"Students who start learning about humans and healthy lifestyle in the biology class will be provided by the CiboPoli Bot to use it and engage in the game, as well as learn about healthy diet and food waste reduction. Students begin by accessing the bot and getting information on how to play the game. They begin by tossing the dice and going into the steps. Students will collect an item (vegetable, fruit, protein, grains, or dairy), add product to basket, or trow it away. At a certain step a student will celebrate his birthday, where each other student have to provide him with an item as a gift. To keep food balance, the student can donate some to his friends. At each step the system will reward the player with certain points based on their performance. At the end, the one who gathers the right combination of food choice and waste the least will acquire more points, and will be positioned at the leader board, accordingly."
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PRELIMINARY RESULTS
People, including school children, spend more time on their chatting applications, such as WhatsApp, Telegram and Facebook than any other application. We conducted a design and questioner phase to validate our initial prototype with 44 school kids with smartphone to validate the prototype by performing in-field testing with the targeted users. Students were provided with the mockups to visualize and see how the application is structured. We then asked them about the type of application they use the most (e.g., Social Networking (e.g., Telegram, WhatsApp), Medical (e.g., Diabetes app), Entertainment (e.g., YouTube), Health and Fitness (e.g., Google Fit) and Games (Any games), to validate the distribution of chatting application among our targeted group. The findings proved the widespread of chatting applications and students use them on daily bases (see Table 2). The majority responded they use mostly social networking applications in their smartphone.
Use Case Scenario
To better illustrate the use case of our chatbot by children we present the following case, where primary school students use CiboPoli to learn about healthy diet and food waste management:
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UMAP’17, July 9-12, 2017, Bratislava, Slovakia
Table 1: The Six User Types & Their Characteristics By The Hexad Model. User Types
Motivation Type
Definition
Game Elements
Philanthropists
Purpose
They are altruistic and willing to give without expecting a reward.
collection and trading, gifting, knowledge sharing, and administrative roles.
Socialisers
Relatedness
They want to interact with others and create social connections.
guilds or teams, social networks, social comparison, social competition, and social discovery.
Free Spirits
Autonomy
They like to create and explore within a system.
exploratory tasks, nonlinear gameplay, Easter eggs, unlocked content, creativity tools, and customisation.
Achievers
Competence
They seek to progress within a system by completing tasks, or prove themselves by tackling difficult challenges.
challenges, certificates, learning new skills, quests, levels or progression, and epic challenges.
Players
Extrinsic rewards
They will do whatever to earn a reward within a system, independently of the type of the activity.
points, rewards or prises, leaderboards, badges or achievements, virtual economy, and lotteries or games of chance.
Disruptors
Triggering of change
They tend to disrupt the system either directly or through others to force negative or positive changes.
innovation, platforms, voting, mechanisms, development, tools, anonymity, anarchic gameplay.
Table 2: Mostly Used Application Category By School Children.
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Application Type
Responds
Rate
Social Networking (e.g., Telegram, WhatsApp)
43
97.7%
Medical (e.g., Diabetes app)
6
13.6%
Entertainment (e.g., YouTube)
31
70.5%
Health and Fitness (e.g., Google Fit)
16
36.4%
Games (Any games)
12
27.3%
about healthy lifestyle through an interactive social game. We plan to improve its design by incorporating additional components, such as dialog management module, user specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, the final version of the game will validate if it works, then we will apply it to other domains and targeted demographics.
ACKNOWLEDGMENTS
DISCUSSION & FUTURE WORK
We acknowledge that the graphics in Figures3, 4 were designed using piktochart.com online graphic design tool and all the images are their property ©.
The bot messages should be styled differently and clearly labeled in a way that communicates they are not human. However, the bot should pose a certain personality that reflects their domain and the context of the game. To engage users in the long-term, its necessary that the conversation is bounded to very particular subjects and follows linear conversation routes. The advantage of chatbots is striping away interface complexity and reduce the interaction to a simple chat and graphic views UI. Every bot interaction is about call and response, with the bot publishing comments into the chat thread and the end user responding in the reply area. Bot interactions should be short and precise. It should be impossible to get into a protracted back and forth conversation with a bot; anything above two inputs feels laborious. We followed a structured conversation to avoid dead ends. The custom keyboards permit a limited range of input and can save a bunch of typing. For example, rather than asking the end user to type ‘yes’ or ‘no’, we provide them two mutually exclusive buttons. In this way the responses stay on track and sidestep the complications of parsing unpredictable plain text input. Some limitations of this work include, the concrete role of conversational-AI and the possibility to extract meaningful insights from the interaction with player. Future work will focus on recognition and analysis of emotions. Currently, there are many existing classifications of emotions, but there is no universally accepted emotional model. To understand how the player feels during the game play will help overcome some limitations of the current system and improve the design and interaction aspect of the game.
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CONCLUSION
In this study we discussed the role of gamification and conversationalAI in education and particularly educating children about healthy diet and food waste management. We proposed a novel conversational tutoring game "CiboPoli" specialised in teaching children
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