Andrade, F. R. H., Mizoguchi, R., Isotani, S. (2016) The Bright and Dark Sides of Gamification.
Proceedings of the International Conference on Intelligent Tutoring Systems. Lecture Notes in
Computer Science v.9684, pp. 1–11.
The Bright and Dark Sides of Gamification
Fernando R. H. Andrade¹, Riichiro Mizoguchi², Seiji Isotani¹
¹University of São Paulo, ICMC, São Carlos, SP, Brazil¹
Japan Advanced Institute of Science and Technology, Ishikawa, Japan ²
Abstract. Everything in life has a bright and a dark side; and gamification is
not an exception. Although there is an increasing number of publications discuss-
ing the benefits of gamification in learning environments, i.e. looking into the
bright side of it, several issues can hinder learning because of gamification. Nev-
ertheless, it seems that only few researchers are discussing the dark side of using
gamification in learning environments and how to overcome it. Thus, in this pa-
per, we discuss some of the problems of gamification, namely, addiction, unde-
sired competition, and off-task behavior. Furthermore, to deal with both bright
and dark sides of gamification at the same time, we propose a framework for
intelligent gamification (FIG) that can offer the necessary infrastructure for ITS
to personalize the use of gamification by monitoring risk behavior, exploring how
best use game design elements to avoid their overuse and finally supporting “fad-
ing” mechanisms that gradually reduces the use of gamification and help students
to concentrate on learning and not only on extrinsic motivators.
Keywords: Gamification; Intelligent Tutoring Systems; Addiction; Framework.
Everything has a bright side and a dark side like a coin, which has a head and a tail,
and Gamification is not an exception. Usually when people find a good thing, they tend
to focus only on its bright side. However, they should always be aware of its dark side,
to use it appropriately.
In the past few years, Gamification has been drawing attention from different areas,
with the promise of increasing users’ engagement, motivation, and promoting changes
in behavior . By introducing mechanics and elements from games, several compa-
nies and research groups have been trying to increase learners’ performance, commu-
nication between different groups of people, and promote better health care and healthy
habits . Specifically, in the educational field, several studies have been studying dif-
ferent techniques and benefits of using gamification to raise students’ engagement level
and reach the flow state with significant findings [2–4].
Although several positive effects of using gamification has been found to date, par-
ticularly to improve student’s performance and increase engagement , researchers
and educators are ambivalent about using game like materials in education since they
could cause addiction and increase the externalization of behaviors that can hinder
learning [5, 6].
This fear should be taken seriously since many recent empirical research reports the
benefits of gamification as unexpected side effects, and not as a result of a well-thought-
out design [1, 3, 4]. It shows that the gamification implementation techniques are still
unconsolidated. Yet, according to two literature reviews on the topic, there are no stud-
ies addressing the potential negative effects of gamification in Intelligent Tutoring Sys-
tem (ITS) or any other kind of Virtual Learning Environment (VLE) [1, 7].
Thus, the main goal of this work is to discuss the potential harms of using game
elements in an ITS and propose a general framework to use gamification in an intelli-
gent way. Considering positive and negative aspects and suggesting ways to fade the
gamification elements to cope with addiction/dependence on gamification.
The remainder of this paper is structured as follows: Section 2 describes the related
works. Section 3 discusses the dark side of gamification and the proposed framework.
Section 4 presents our envisioning application of the framework and how to use it. Sec-
tion 5 concludes the paper with our final thoughts and the directions towards the vali-
dation of our Framework for Intelligent Gamification (FIG).
2 Background: gamification, flow and addiction
Kapp  defines gamification as “Using game-based mechanics, aesthetics and
game thinking to engage people, motivate action, promote learning, and solve prob-
lems”. The definition of the concept changes slightly according to different authors, but
the core idea remains the same, that is, gamification as a tool to “increase engagement
in some activity using game features, providing enjoyment and fun” [1–3, 9, 10].
The motivational background of gamification usually relies on the SDT (Self Deter-
mination Theory) , which considers that a human being has three basic needs: com-
petence, relatedness, and autonomy. Based on the degree of a person’s needs and the
kind of activity, he/she can be more or less motivated to perform some activity. Ac-
cording to this theory, the user levels of motivation , vary from amotivated (without
any motivation to perform the activity) to intrinsically motivated (when the user doesn’t
need any external incentive to perform it). Thus, the gamification theory proposes that
by introducing game elements in an environment to satisfy some of the user’s needs, it
is possible to make the activities more attractive, even if he/she is not intrinsically mo-
The most common game mechanic applied in educational environments is the re-
ward system based on fast feedback about the students’ performance in the form of
points, trophies and badges and the division of the domain content in small units repre-
senting game levels [12, 13]. Furthermore, the use of leaderboards is also a common
tool to stimulate competition [1, 14].
One of the main goals of using gamification is to keep users in flow. The flow is a
state of deep concentration in which the user becomes so engaged in the task that he/she
loses self-awareness, and track of time. Also known as optimum experience flow;
a highly desired state by game developers, considering that they want to keep the player
entertained and engaged as much as possible.
The idea of using gamification in learning environments to put students in a flow
state while they are learning is quite attractive to be implemented [2, 16]. On the other
hand, a number of studies has been conducted addressing the flow state as a factor
associated to game addiction. For example, Sun conducted a research with 234 us-
ers, in which they found evidences that associate addiction in mobile games with per-
ceived visibility and flow. Perceived visibility is related to the notion of being noticed
by peers and in a position of social presence. Gamification designers also seek to in-
corporate this characteristic in the systems, by using leaderboards and sharing user
achievements, thus fulfilling the relatedness needs of the students according to the SDT.
In another study, Jeong and Lee  examined whether Big Five personality traits can
affect game addiction according to psychological, social, and demographic factors. To
do so, the researchers used data from a survey of 789 game users in Korea, seeking
associations and the results showed that the neuroticism trait apparently increases game
addiction. They also observed that a general self-efficacy affected game addiction in a
negative way, whereas game self-efficacy increased the degree of game addiction. Be-
sides that, loneliness enhanced game addiction, while depression showed a negative
effect on the addiction. In the context of education, these findings could mean that a
student who is confident in his abilities to perform the task is less prone to addiction
than a student without confidence, and if the student only has confidence in his game
skills, he is more susceptible to addiction.
3 The Dark Side of Gamification
The gamification approach originates in the industry with a strong appeal from mar-
keting and service . In the context of learning, to increase students’ engagement re-
searchers and professionals have been trying to bring flow experience and immersion
to VLE. Even though improving learners’ engagement using game elements is a highly
attractive idea, contrary to the marketing perspective, the goal is not to make the student
loyal to the system, but rather increase his learning.
Therefore, we believe that gamification can be good, as long as it is controlled and
monitored. If such measures are not taken, then this could adversely affect the effec-
tiveness of the system and hinder learning. In the following paragraphs, we will present
three problems that may appear by adding game elements and mechanics without care-
Off-task Behavior: If the gamification system is untied to the educational outcomes,
the game features can be a distraction to the user. In this case, even if the user likes to
use the system, he will not learn more from it. For example, the introduction of re-
sources that provides relatedness to users, such as chats and forums. These resources
are not directly related to the learning experience, allowing to the student to spend time
in the system without focusing on learning. Another example are the customization
features, those are a very important to promote immersion, but also, allows spend time
in the system without learning.
Undesired competition: Leaderboards are a common resource to promote competi-
tion, and sense of competence. Still, it can be harmful for students with low perfor-
mance and low self-efficacy, since they can feel forced in a competition with their
peers, which can negatively affect their sense of competence and result in the reduction
of their interest and engagement.
Addiction and Dependence: Based on the literature[6, 17, 18], some game features
and sensations like flow can be regarded as addictive factors. Thus, addiction could be
a potential problem in gamified environments. Unlike the behavior of alcoholics or
gambling addicts, addiction in such environments should not have greater effects such
as loss of personal property or family disruption. However, our concern is the kind of
dependency created by the game-like experience in education, as the students can re-
source to “game the system” in order to get rewards or they may not be able to learn
without gamification features.
In the first scenario, the student could change the focus from learning the subject to
other aspects provided by the system gamification. For instance, earning points to get
a higher position in a leaderboard or unlock one exclusive or rare content in the system
and gain visibility with his peers. Typically, high positions in ranks or acquisition of
virtual goods in a gamified application depends on the progress of the system main
objective, but it is not uncommon for students to seek alternative strategies to get their
desired results . In the second case, the student creates a dependence of game ele-
ments to stay engaged in the system. In other words, the student is only capable to focus
on the system and acquire some knowledge if it has game elements or some kind of
extrinsic reward for his effort. To identify this condition, the system demands infor-
mation about the relationship of the student with the game elements.
Since the evolution in the gamification in a well-designed system is highly correlated
to the success and the learning outcomes, the gamification overuse may go unnoticed;
therefore, a constant monitoring of the interactions between the user, the system and
the gamification features is required.
4 Framework for Intelligent Gamification (FIG)
There are few initiatives towards gamification taken by academics aiming at the im-
provement and the consolidation of gamification. Previous works on gamification have
proposed frameworks with different perspectives, but to our knowledge none of these
have discussed how to deal with the negative implications of gamification [20, 21].
However, as discussed before it is crucial to deal with both sides of gamification, not
only using its potential to increase the engagement, but also controlling this use of gam-
ification to avoid the creation of new problems.
In order to address this, we propose a framework based on the ITS architecture that
considers the information required to implement gamification with personalization and
can process its impacts on the students and potential harms. Further, we propose a strat-
egy to reduce the participation of overused elements by fading. Thereby, our framework
proposes to increase the engagement aligning the gamification strategies to gamer pro-
files and also to identify and handle misuses resultant from the gamification in learning
environments, which, for the best of our knowledge, was not addressed by neither the
academic community nor the industry. In Figure 1, we present the proposed framework
and its components, which are explained in the following subsections.
Figure. 1. Framework for Intelligent Gamification.
In this work, we are not approaching the domain content gamification, in this sense
the gamification in this framework is a layer independent of the pedagogical objectives
proposed by the tutor, allowing dynamical customization. Once it interacts with the
student in order to satisfy the motivational needs of competence, relatedness and au-
tonomy, but do not change the pedagogical objectives proposed by the learning de-
signer. Currently, most of the studies only use static elements without or with at least
few personalization options, however, the game design literature and also the results of
empirical studies provide evidences indicating the need to consider user individual pref-
erences [1, 10].
a) Gamification model. A game element can be considered as a game component,
it will behave according to the game mechanic attached to it, and will interact with
the user when a game event is triggered due an action taken by him . The gam-
ification model contains all the possible game events that can be triggered in the
system and that are controlled and regulated by the Controller Component.
b) Student Model. The main goal of gamification is to affect the students’ motiva-
tion and behavior. In order to do so by using an intelligent approach, it is necessary
to hold enough information in the Student Model. Thus, we propose a student
model divided in five small groups of attributes, as presented in Figure. 2 and
explained in the subsequent item.
b.1) Knowledge attributes. This group contains the traditional information of the
Student Model in terms of domain knowledge or skills they learn. There are sev-
eral ways of representing students data regarding the information used by the ITS
Tutor Module to make decisions in order to provide a better quality of content and
hints. Thus, it is not in the scope of this study to address the way of representing
these data. However, it is important to clarify that there is indeed a need for data
on the student’s performance, so the knowledge base should be able to provide
these data to considerations about improvement or decreasing of student perfor-
b.2) Psychological attributes. It contains information about the student’s person-
ality traits and data on mood. As said in the previous sections, several studies
shown that the personality traits influence learning and addiction behaviors, in this
sense, the information about the students’ personality trait is a useful tool to pro-
vide evidences of an undesirable condition.
b.3) General behavior attributes. They are responsible for storing information
about the student’s habits not related to learning. Game addiction shares several
symptoms and characteristics with different kinds of addictions, so it is necessary
to expand the knowledge about the user in order to obtain evidences of a problem
b.4) Interactions patterns attributes. The system logs record the session length,
dates, time between tasks, estimated required time to finish that tasks and the in-
formation about the interaction with the game elements. Therefore, the interaction
patterns attributes contain the analyzes of those information such as mean of in-
teractions during sections, number of tasks performed by section, mean time to
solve tasks, frequent subjects, total amount of logins, mean length of the sessions.
b.5) Gamer profile attributes. In this framework, we are considering that students
may have different gaming habits and preferences in order to provide a suitable
set of game elements and mechanics.
Figure. 2. Student Model.
c) Interaction Patterns. The interaction patterns contain the representation of an ex-
pected behavior in the system. This model represents the observable data such as
time to finish contents, number of interactions, and frequency of system use. The
interaction patterns also contain the model of expected interactions with the game
elements. This model will vary according to the gamer profile approach and the
gamification model, since it has to represent the regular interaction pattern for a
student in the case of static gamification model, or the standards for a group of stu-
dents in the case of a gamification model based on different profiles.
d) Psychological Patterns. The psychological patterns represent the information that,
when matched with the situation of one student, provide evidence that this student
may be in a risk group. It can be represented by a set of rules, preset by experts or
by a series of factors that can be used by the Reasoner to inference about the student
a) Assessment Component. The assessment component is responsible for collecting
the student’s observable and interactive data and update student model
b) Behavior Reasoner. The Behavior Reasoner is the component responsible for an-
alyzing the student’s data in order to identify risk behavior. To perform this task,
the component compares the information contained in the Student Model with the
standards model in the Interaction Patterns and Psychological Patterns. When it
identifies anomalies in the student behavior, the Reasoner may inform a human sys-
tem administrator, such as a teacher, to take an action or, as we propose in this paper,
to inform the situation to the Controller, triggering changes in the gamification
c) Controller Component. The Controller is the component is responsible for the set-
tings of the gamification layer, and in order to do so, the controller needs to cross
the information contained in the student model, gamification model, and behavior
Reasoner component. In a customizable approach, the student would be able to in-
teract with the controller, changing the suggested gamification components or pa-
rameters and, at the same time, giving information to the controller, which will
change the student’s gamer profile attributes, if needed. When the Reasoner identi-
fies that a user needs to change his interactions with some elements, the controller
may act changing the value attributed to that element in order to fade this element
for the user interest. Our definition of Fade represents the change in the attributes
of the element in order to make it less attractive or difficult to access, like changing
its colors or moving it to an area that receives less attention.
5 Envisioned Application
5.1 Information Gathering
Gamer profile: To model the gamer profile, there are several player types in game
design literature and some new types are proposed considering gamification appli-
cations [22, 23]. The game components in the system have to be consistent with the
player/user types in the chosen typology. The gamer profile is composed of player
type attributes and the values for each of these attributes are updated by the controller
according to the interaction patterns to personalize the gamification and fading for
that specific player.
Psychological Attributes: Two very common tools for data acquisition about per-
sonality traits are the Big Five  and the MBTI (Mayers Briggs Types Indicator)
. However, several researches criticized the use of MBTI as a psychometric in-
strument. Our model is composed of the personality traits, and can contain other
psychological variables that may be used to identify anomalies in the user behavior.
For instance, history of mood changes and history of emotions.
General Behavior Attributes: The function of this model is to store complementary
information about user habits. To this effect, the use of intelligent agents or chatbots
is highly recommended. Such agents can also be used to acquire information about
mood modifications and other behavioral attributes.
Gamification Model: Each gamer profile has a list of adaptation attributes that cor-
respond to the game components that will be available to that specific profile in the
interface. Each attribute can receive a value between 0 (inactive) and 1 (fully active).
The Gamification Model contains the standards for these values, and changing these
attributes affects the standards for the player types.
Interaction patterns: Normal user behavior can be established by experts, pilot run-
ning of the system or by the behavior of the majority of the users in the system.
Psychological Pattern: The psychological pattern represents the risk group in the
system. In this sense, this model has to contemplate the traits, and the associations
with other variables that provide evidences of a risk scenario. E.g. One student that
has the trait of irresponsibility, but solves a number of tasks above the mean of the
other students, in a much shorter time than the required, should be considered as a
candidate for change.
Initially the student provides information about his gamer profile and personality
traits. After that, the Controller consults the gamification model and adapts the interface
to the elements recommended for the student. Then, the assessment component starts
to log the user’s interactions and the intelligent agent interacts with the student in pre-
determined intervals to fill the general behavior model. Once the general behavior
model is populated, the Reasoner starts to compare the patterns periodically, in order
to identify anomalies.
As the Reasoner becomes more knowledgeable about the anomalies in the student
interaction patterns, it generates a list of gamification artifacts
eligible for fading. To
maximize the learners growth capabilities, the fading method has been previously used
to minimize user’s reliance on the system’s help . When an artifact hits the prede-
termined threshold, the Reasoner marks it for the fading process. Once the process
starts, the system agent makes an intervention signalizing the excess of interactions
with that artifact and tracks the user performance and interactions seeking changes in
his behavior. This intervention intends to increase his self-awareness and provides the
opportunity for self-regulation. However, if after a certain period the behavior remains
same, the system starts to fade away the artifact, up to removal, until the number of
interactions go back to normal. After that, the artifact is restored to the original state
and the agent informs the student to observe his behavior.
To identify the implications of fading on the user performance and how much he
depends of gamification to keep motivated, the student is constantly monitored. If dur-
ing the fading process the student’s performance declines, the agent makes an interven-
tion in order to find out whether this is due to fading the artifact. If the reason for the
A Gamification artifact is defined as a composition of a visual game element, that directly
interacts with the user, and the game mechanic, that define how this element will behave.
decline is inherent to the process, it provides evidence with respect to the student’s
dependence on gamification. Nevertheless, in both cases, the element is restored to the
original state and the agent informs the user about the importance of keeping focus on
learning. The artifact is restored so as not to impair their learning. Furthermore, the
intervention will reinforce his self-awareness and provide, once more, the opportunity
for self-regulation, which we believe could be more meaningful since the user knows
that he can be "punished" somehow for his overuse.
6 Concluding Remarks
Most of the time people tend to focus too much on the bright side and overlook the
dark side of matters. Similarly, the interest in gamification has been growing; however,
no one seems to have shown interest in its dark side (negative effects). In this paper,
we identified addiction as the dark side of gamification and addressed the elements used
in gamification that related to this phenomenon and how it occurs in gamified environ-
ments. Further, we proposed a framework to monitor and fade with the gamification
elements to avoid the negative implications of addiction.
Our next steps include providing a detailed addiction model for learning environ-
ments and the experimental evaluation of the fading strategy of gamification elements
and the impact of this strategy in terms of engagement and performance.
The ITS architecture was chosen because such systems consider student information
to make decisions in order to improve learning. However, we believe that the same
reasoning can be applied to any VLE with proper dynamics to interact and retain
enough information about the student and the environment.
Acknowledgments. We thank CNPq and CAPES for supporting this research.
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