Gamify: Towards Tailored Gamification Informed by Users’ Personality,
Emotional State, and Demographics
Amal Yassien
1 a
, Youssef Elsharkawy
2
, Alia Elbolock
3 b
and Slim Abdennadher
1 c
1
German International University in Cairo, Egypt
2
German University in Cairo, Egypt
3
American University in Cairo, Egypt
Keywords:
Gamification Mechanics, Personalization, Emotions, Personality.
Abstract:
Recently, gamification has gained world-wide interest in several domains, especially educational ones, to
increase user engagement and learning effectiveness. Using gamification, there are several mechanics that
designers leverage to motivate users to engage with the game (e.g. streaks, progress, time limit, .. etc). How-
ever, it remains unclear which gamification mechanic would be most effective for individuals with different
personality traits and emotional states, age, gender, and field-of-study. To this end, we introduce “Gamify”, a
user-profile empirical study (N = 65) that sheds light on how gamification mechanics, namely rewarding and
penalizing, along with user profile (made up of (1) personality (represented by Big-Five Model), (2) emotional
state, (3) age group, (4) field-of-study, and (5) gender) affect users’ experience. To achieve this, we have de-
signed a trivia-based game, where users have riddles that they should answer using 4 provided choices. Within
our user-study, we had 4 different levels (reward only, penalty only, reward and penalty, no gamification me-
chanic). Our results show that the preference of specific gamification mechanic differed according to user
profiles. For instance, users below 18 preferred being exposed to both the rewarding and penalizing mechanic.
Using Gamify, game developers can create a tailored gaming experience that engages users having different
user profiles.
1 INTRODUCTION
Gamification is a key strategy for enhancing user en-
gagement and experience when interacting with dig-
ital content, as it boosts users motivation by track-
ing their progress and achievements (Khodabande-
lou et al., 2023; Suartama et al., 2023; Weber et al.,
2023; Huang et al., 2023; Hallifax et al., 2020). De-
spite its positive impact, gamification can be coun-
terproductive and ineffective (Khaleghi et al., 2021;
Tan et al., 2023). Using inopportune gamification
mechanic can lead people to (1) take a task light-
heartedly, or (2) being distracted from the primary
goal which the game address (Toda et al., 2018).
Therefore, a need for tailoring gamification mechan-
ics to different user profiles arose. Several studies
have been conducted to address this (e.g. (Rodrigues
et al., 2022; Hallifax et al., 2019)) by construct-
a
https://orcid.org/0000-0002-9327-0426
b
https://orcid.org/0000-0002-5841-1692
c
https://orcid.org/0000-0003-1817-1855
ing user profile based on player typology, personal-
ity traits, or learning style (Pessoa et al., 2023) which
are not exhaustive or mutually exclusive (Carneiro
et al., 2022). To address this gap, we introduce Gam-
ify, an empirical study that aims to understand how
different gamification mechanics (rewarding and pe-
nalizing) along with user profile affect user’s experi-
ence. Within Gamify, we constructed more granular
user profiles that are based on user demographics (age
group, field-of-study, and gender) along with person-
ality traits and emotional state (valence, and arousal)
which is unprecedented (see literature review (Klock
et al., 2020)). Afterwards, we (1) developed Flex-
iLearner, a trivia-based game that is independent of
any topic and context, (2) implemented two different
gamification mechanics, rewards (score) and penal-
ties (health points) within FlexiLearner, and (3) con-
ducted a mixed-study (N = 65) to see how users with
different profiles are affected by different gamifica-
tion mechanics. At the beginning, we aspired to as-
sess the effect of 4 gamification mechanics, but hav-
ing 4 different gamification mechanics would require
Yassien, A., Elsharkawy, Y., Elbolock, A. and Abdennadher, S.
Gamify: Towards Tailored Gamification Informed by Users’ Personality, Emotional State, and Demographics.
DOI: 10.5220/0013290300003932
In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 2, pages 737-744
ISBN: 978-989-758-746-7; ISSN: 2184-5026
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
737

exposing participants to 16 configurations of Flex-
iLearner. To reduce the complexity of our study and
minimize confounds, we opted for reducing the num-
ber of gamification mechanics to two of the most ba-
sic gamification mechanics (rewards and penalty), as
these mechanics would help us draw helpful insights
about management strategy to adopt for different user
profiles. Our results have shown user experience is
impacted by both user profile and gamification me-
chanic adopted. For example, users with low neu-
roticism perceive a less effective gaming experience
when they are penalized for their incorrect choices,
meanwhile male users perceive a less effective gam-
ing experience when they are being rewarded for their
correct ones. Through Gamify, game developers can
construct engaging gaming experience tailored to spe-
cific user profile.
2 BACKGROUND AND RELATED
WORK
2.1 Character-Computing: Aspects of
Human Character
From the perspective of the founder of Character
Computing, character is a holistic construct that spans
all aspects that differentiate individuals from one an-
other (El Bolock, 2020). These aspects include user’s
personality, affect, history, beliefs, morals, appear-
ance, preference, and background (El Bolock, 2020).
The main benefit of Character-Computing systems is
to design interfaces and experiences that dynamically
respond to individual needs (Bolock et al., 2020).
Relying on the Character-Behavior-Situation (CBS)
triad, computing systems can (1) learn human char-
acter by inspecting their behavior in a given situa-
tion, (2) predict human behavior in a given situation
in light of their detected character, or (3) create artifi-
cial situations that are plausible and believable given a
well-established behavior and definition of an individ-
ual’s character (El Bolock, 2020). Although adapting
computing systems based on character has enormous
benefits, developers should consider when making
character-based adaptation is necessary (e.g. adapta-
tion for navigation apps is not necessary because their
functionality is basic) (El Bolock, 2020). In order to
capture aspects related to character, researchers often
rely on the Five-Factor Model (FFM) of personality,
also known as OCEAN or the Big Five (John et al.,
1999; McAdams and Pals, 2006) and the Self Assess-
ment Manikin (SAM) to assess the emotional states of
users (Bradley and Lang, 1994). The Big Five Model
categorizes personality into five broad traits: open-
ness to experience, conscientiousness, extroversion,
agreeableness, and neuroticism and has been wide;y
used to predict empirical findings in terms of indi-
vidual’s stable personality traits (Mount and Barrick,
1998). The SAM model relies on two dimensions
to capture emotional state: (1) Valence (the feeling
is pleasant or not) and (2) Arousal (the user is calm
or not) (Bradley and Lang, 1994). Users’ emotional
state is a key indicator of engagement and interaction
within a given experience (Bolock et al., 2020). In
our work, we capture user’s personality traits using
the big five model and their emotional state using the
SAM model to construct user character. Moreover,
we construct our user profile by capturing user’s char-
acter and demographics in terms of gender, age group,
and their field-of-study in line with Elbolock’s defini-
tion of character (El Bolock, 2020).
2.2 Personalized Gamification
Gamification refers to using games in non-
entertainment context (e.g. education) (Sch
¨
obel
et al., 2021). Meanwhile personalized gamification
refers to tailoring game mechanics toward individual
user needs (e.g. gamer typology, gender, age)
(Rodrigues et al., 2022). There are several gamer
typologies, but the hexad and brain hex are two of the
most adopted ones. The hexad typology categorize
players based on their motivation for gamification
into six categories: achievers, disruptors, free spir-
its, philanthropists, players, and socializers, while
brainhex maps each player type to the human body
neurobiological responses and categorizes players
into seven types: achievers, conquerors, daredevils,
masterminds, seekers, survivors, and socializers
(Klock et al., 2020). Also, some researchers tailor
their gaming experience based on domain specific
parameters, such as learning style and learning
activity type (Rodrigues et al., 2022). When com-
paring personalized gamification to one-size-fits all
gamification within education domain, (Xiao and
Hew, 2024) concluded that personalized gamification
boosted students positive emotions and motivation
toward learning. Moreover, (Fischer and Fischer,
2024) classified users into player types (relying on
Hexad Scale) and constructed a decision tree that
assigns various game elements (e.g. badges, points)
according to the player type assigned to a given
user. Similarly, (Shabadurai et al., 2024) analyzed
users’ input during gameplay to infer their player
type and adapt the game mechanics accordingly.
Along with designing adaptive games based on
player type, (Lopez and Tucker, 2021) compared
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user performance in adaptive and non-adaptive
gamified application along with non-gamified and
counter-adaptive ones, and concluded that users
performed best in the adaptive gamified application
condition. (Hallifax et al., 2020) investigated the
impact of user profile (motivation and player type)
and gamification mechanics on learner motivation
and engagement and concluded that tailoring games
using both player type and motivation boosted user’s
motivation. Following the same pattern, (Rodrigues
et al., 2021) used user profile (gender, experience in
gamification research, education, preferred gaming
genre) to tailor gamified systems and also compared
user’s experience within tailored gamified systems
to that in one-size-fits-all one. They conclude that
users perceived their experience as motivating and
need-supporting when using tailored gamified sys-
tems. (Hallifax et al., 2019) conducted an empirical
study to identify how different user profiles (in terms
of personality traits, hexad, and brainhex) link to
specific game mechanic by constructing storyboards
of each game mechanic and then making participants
assess which game mechanic should be considered
while tailoring the gamified experience.
2.3 Research Gap
So far, related work relied upon character computing
systems to construct (1) recommender systems (e.g.
(Bolock et al., 2020)), (2) identify how user profiles
are linked to specific game mechanic (e.g. (Halli-
fax et al., 2019)). There is also a major research
interest in comparing personalized gamified systems
to one-size-fits-all-ones (Xiao and Hew, 2024) and
non-gamified systems (Lopez and Tucker, 2021). Re-
searchers have also used user-profiles to tailor gam-
fication mechanics, but often relied on (1) single di-
mension profile consisting of player type (e.g. (Fis-
cher and Fischer, 2024)) or (2) dual-user profiles (e.g.
(Rodrigues et al., 2021; Hallifax et al., 2019)). How-
ever, it remains unclear to developers which gamifica-
tion mechanic to enable for certain user profile. More-
over, little is known regarding how user profile (e.g.
gender) affect users performance in tailored gamifica-
tion (Klock et al., 2020). Therefore, Gamify aims
to identify how rewards in terms of scores and
penalties in terms of health points link to each fac-
tor within a granular multidimensional user pro-
file that considers users’ stable aspects of character
like personality and demographics along with variable
ones like emotional states.
3 GAMIFY: STUDY DESIGN
To properly link each dimension within user profiles
to specific gamification mechanic, we have conducted
a mixed-subject study with 11 independent variables:
(1) age group, (2) gender, (3) field-of-study, (4) extro-
version, (5) agreeableness, (6) conscientiousness, (7)
neuroticism, (8) openness, (9) valence, (10) arousal,
and (11) gamification mechanic. Each independent
variable represents a dimension within the user pro-
file we constructed and are between-subject variables
except for gamification mechanic one. Gamification
mechanic is a within-subject variable consisting of
four levels: (A) No gamification mechanic, (B) Re-
ward only (score), (C) Penalty only (health point), (D)
Reward and Penalty (score + health point). The aim
of the study is to determine which gamification me-
chanic is best suited to a certain factor within a user
profile by measuring user’s experience while play-
ing the game in each of the four levels. Since it
was not possible to have each participant answer 26-
item questionnaires after each round of the 4 rounds,
we used short version of user experience question-
naire (UEQ) (Hinderks et al., 2018). Figure 1 shows
our experiment procedure and FlexiLearner in each
of the four configurations. To properly prepare for
the main experiment, we have (1) developed a trivia-
based game with 4 question rounds, where each round
is followed by in-game usability questionnaire (More
details in Section 3.1) and (2) made a pilot study with
different game rounds and mechanics details, e.g.,
like 4 hearts for longer rounds containing 7 questions
(More information in Section 3.2).
3.1 Gamify: Apparatus and
Implementation
To draw insights from Gamify, we have developed
a trivia-based game that enables its players to solve
riddles through answering a 4-choice multiple choice
question. When users enter the correct answer their
choice becomes green colored. On the other hand,
when users made an incorrect choice, their choice be-
comes red and the correct choice is highlighted in
green. To make user’s experience flow more smooth,
we have incorporated user experience questionnaire
to be in game. After each game-round, users are
shown a window that contains short version of user
experience questionnaire (UEQ). Within the game,
we have implemented two different gamification me-
chanic: (1) Points, represented as a score that in-
crements by 5 whenever the user answers a ques-
tion correctly, and (2) Health Points, represented as
hearts that their number decrease whenever the user
Gamify: Towards Tailored Gamification Informed by Users’ Personality, Emotional State, and Demographics
739

Figure 1: The figure on the left show a flowchart of our experiment procedure. The figure on the right shows FlexiLearner’s
interface in each of the configuration (levels) within the gamification mechanic factor.
answers a question incorrectly. Flexilearner was im-
plemented using Unity and all its functionalities have
been scripted using C#. During a single game round,
number of questions answered correctly, and the av-
erage question response time along with UEQ scores
are logged to a json file.
3.2 Gamify: Study Preparation
A pilot testing of a simplified version of FlexiLearner
was conducted on 15 participants to determine the
optimal number of questions per game round for the
main experiment. Our pilot test had three configura-
tions: 3 Questions with 2 hearts, 5 Questions with 3
hearts, and 7 Questions with 4 hearts. We considered
several factors like (1) whether gamification mechan-
ics were noticed by participants without experimenter
intervention, (2) were their function apparent, and (3)
whether participants were willing to play a full-length
version, i.e. 4 game rounds for each gamification
mechanic level. The pilot game builds were devel-
oped using Unity WebGL and hosted on itch.io. Par-
ticipants were randomly selected and asked a series
of questions via voice call to gather feedback. The
results showed that participants noticed hearts and
points, and were willing to play a game four times.
Feedback from participants included comments on
the UI, and recommendations for: adding sound ef-
fects, increasing difficulty, and having fewer lives.
We refrained from adding sound effects to adequately
measure the impact of our incorporated mechanic and
minimize confounds. We have also opted out of in-
creasing difficulty or decreasing lives (hearts) to avoid
inducing unnecessary mental load on users.
3.3 Gamify: Procedure
Participants were screened for English communica-
tion and given 15 minutes to complete the experi-
ment. Afterwards, they complete a Self-Assessment
Manikin (SAM) test to assess their emotional state,
take a BFI-10 test to assess their personality traits, and
provide their age group, gender, and field-of-study.
Then, the experimenter selects a game order config-
uration based on the Balanced Latin Square method.
Participants answered each set of five questions while
being exposed to 4 different levels within gamifica-
tion mechanic factor. After each game round, partic-
ipants fill out User Experience Questionnaire (UEQ),
which assesses the pragmatic and hedonic quality of
the game experience.
3.4 Gamify: Participants and
Recruitment
65 participants (38 male, 37 female) were recruited
by word of mouth and approached arbitrarily by the
experimenters on university campuses or in public ar-
eas. We had 39 participants from Engineering field,
3 from graphic design, 5 from Business Informatics,
3 from Business, 1 from Architecture, 12 from Phar-
macy, 1 from Literature, and 1 from Medicine field.
All participants could speak English fairly well and
could solve riddles in English. Their ages varied from
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under 18 to between 35-44 years with 3 participants
below 18, 46 participants within 18-24 age group, 12
participants within 25-34 age group, and 3 partici-
pants within 35-44 age group.
3.5 Limitations
Using Gamify, we were able to draw useful insights
regarding which gamification mechanic, reward or
penalty, would affect the experience of different user
profiles. Due to the sample size, we have restricted
our analysis to only assessing the effect of each fac-
tor within the user profile along with its interaction
with gamification mechanics affect user experience.
For future work, a larger user study is needed (N
= 10K-20K) to fully link each individual user pro-
file (11-dimensional user profile we constructed) to
a certain gamification mechanic based on user expe-
rience. Also, we acknowledge that our sample has
an age bias, where the majority were young adults,
but this limitation is an opportunity to draw insights
about young adults behavior, as these age groups are
the most productive one in the work sector (Shana-
han et al., 2002). We have targeted users with varying
field of study, but also, most of our sample is from
the STEM field, which is not a limitation but rather
an opportunity to assess the effectiveness of gamifica-
tion mechanics within STEM domain, especially that
gamification is proven to be an effective learning tool
in STEM fields (Ortiz-Rojas et al., 2025), i.e., would
be frequently used.
4 GAMIFY: ANALYSIS AND
RESULTS
In order to link each dimension within our con-
structed user profile toward a gamification mechan-
ice, we relied on Generalized Linear Mixed Models
(GLMM), where we fitted a Cumulative Link Mixed
Model (CLMM) with Laplace Approximation to ana-
lyze user experience results. When we fitted the mod-
els, our primary focus was to identify the fixed effects
of our 11 factors and how the interaction between
gamification mechanic and each individual factor (10
independent factors of user profile) affected user ex-
perience, in terms of overall experience and hedonic
and pragmatic quality.
4.1 Gamify: Overall User Experience
The model converged (loglikelihood =
−784.48, AIC = 1798.95) with a significant fixed
effect of study field, personality trait, and emotional
state, where users majoring in Business Informatics
(β = 4.94, SE = 2.02, z = 2.44, p = 0.014), Engi-
neering (β = 3.62, SE = 1.76, z = 2.05, p = 0.040),
Graphic Design (β = 7.24, SE = 2.96, z = 2.45, p =
0.014), Medicine (β = 10.38, SE = 3.49, z =
2.97, p < 0.001), and Pharmacy (β = 4.05, SE =
1.84, z = 2.197, p = 0.03) showed positive overall
experience values. Also, users with low oppenness
(β = 1.71, SE = 0.73, z = 2.34, p = 0.019), and low
arousal (β = 1.45, SE = 0.73, z = 1.99, p = 0.045)
experienced a positive overall experience, meanwhile
those with low valence (β = −1.81, SE = 0.70, z =
−2.58, p = 0.010) experienced a negative overall
experience. There was a significant gamification me-
chanic × age group interaction, where users (1) aging
35-44 showed negative overall experience when
playing the game using gamification mechanic B
(β = −4.49, SE = 2.10, z = −2.14, p = 0.032)
and (2) those aging 25-34 experience a
negative overall experience when playing
the game using gamification mechanic C
(β = −2.25, SE = 1.03, z = −2.20, p = 0.029).
However, users aging below 18 showed a positive
overall experience when playing the game using me-
chanic D (β = 4.14, SE = 1.96, z = 2.11, p = 0.035).
4.2 Gamify: Hedonic Quality
The model converged (loglikelihood =
−645.43, AIC = 1442.86) with a significant
fixed effect of study field, where users major-
ing in Architecture (β = 3.79, SE = 1.87, z =
2.03, p = 0.043), Business Informatics (β =
2.52, SE = 1.18, z = 2.15, p = 0.032), Engineering
(β = 2.53, SE = 1.05, z = 2.40, p = 0.016), Graphic
Design (β = 3.50, SE = 1.76, z = 1.99, p = 0.046),
Literature (β = 4.14, SE = 1.96, z = 2.11, p = 0.032),
Medicine (β = 7.54, SE = 2.05, z = 3.68, p < 0.001),
Pharmacy (β = 3.29, SE = 1.10, z = 2.99, p = 0.003)
showed a positive hedonic quality when playing the
game, irrespective of the gamification mechanic they
were exposed to. There was a significant gamification
mechanic × age group interaction, where users
aging 35-44 showed a negative hedonic quality while
playing the game using gamification mechanic B
(β = −5.24, SE = 1.65, z = −3.18, p = 0.001), and C
(β = −3.45, SE = 1.48, z = −2.33, p = 0.020).
4.3 Gamify: Pragmatic Quality
The model converged (loglikelihood =
−630.12, AIC = 1414.23) with a significant fixed
effect of study field, emotional state, and personality
Gamify: Towards Tailored Gamification Informed by Users’ Personality, Emotional State, and Demographics
741

trait. Users majoring in Business (β = 3.83, SE =
1.78, z = 2.15, p = 0.031), Business Informatics (β =
4.81, SE = 1.64, z = 2.94, p = 0.003), Engineering
(β = 3.11, SE = 1.45, z = 2.15, p = 0.032), Graphic
Design (β = 6.35, SE = 2.45, z = 2.63, p = 0.009),
Medicine (β = 10.81, SE = 2.90, z = 3.73, p < 0.001),
Pharmacy (β = 4.66, SE = 1.51, z = 3.08, p = 0.002)
showed a positive pragmatic quality. Similarily,
users with low openness (β = 1.74, SE = 0.71, z =
2.44, p = 0.014) experienced a positive prag-
matic quality. However, users with low valence
(β = −2.12, SE = 0.68, z = −3.11, p = 0.002) ex-
perienced negative pragmatic quality while playing
the game, regardless of gamification mechanic
shown. We have also observed a significant gam-
ification mechanics × age group, gamification
mechanics × gender, and gamification mechan-
ics × personality traits interaction. Male users
(β = −1.51, SE = 0.75, z = −2.01, p = 0.045)
showed negative pragmatic quality while
playing the game using gamification me-
chanic B, and those with low neuroticism
(β = −1.44, SE = 0.71, z = −2.04, p = 0.041)
showed negative pragmatic quality when they played
the game using gamification mechanic C. However,
users aging below 18 showed positive pragmatic
quality when playing the game using gamification
mechanic D.
5 DISCUSSION
5.1 Study Field, Emotional State, and
Personality Traits Impact on
Experience
Our analysis shows that user profile elements like
emotional state, personality traits, and study field
affected user experience irrespective of the gamifi-
cation mechanic adopted. In general, we observed
that users’ study field significantly affected their user
experience, although generic trivia questions were
added in our game. Gamify identified a positive
association between users majoring in STEM fields
like Engineering, Medicine, and Pharmacy and (1)
hedonic, (2) pragmatic, and (3) overall user experi-
ence. Also, users majoring graphic design and busi-
ness informatics showed the same behavior. For users
from artistic background like literature and architec-
ture fields, they reported positive hedonic (i.e. expe-
rience is pleasant) experience. Users studying busi-
ness reported positive pragmatic (i.e. task-oriented
nature of experience) quality. We foresee this find-
ing as an indicator for how gamification can make
learning experience more entertaining in several
fields of study, especially STEM related ones. For
personality traits, we only observed significance for
Openness, where users who have low openness rates
showed positive pragmatic and overall experience, but
openness showed no significant impact on hedonic
quality. Therefore, trivia gaming would be nice for
conventional individuals who prefer routine, which
is in accord with findings from tailored gamification
literature (Klock et al., 2020). Similarly, individu-
als who are calm (low arousal) showed positive over-
all experience, which means FlexiLearner provided a
pleasant experience for them. Lastly individuals with
low valence rates (low pleasantness rate) show nega-
tive overall experience and pragmatic one.
5.2 User Profile and Gamification
Mechanic’s Impact on Experience
Our analysis shows a significant interaction between
age and gamification mechanic with respect to over-
all experience and hedonic (related to pleasantness)
aspect of the experience. Individuals from older
age groups (35-44, 25-34) showed negative overall
experience when exposed to a single gamification
mechanic, e.g., people aging 35-44 showed negative
overall experience when playing the game with the re-
ward or score shown to them. For age group 25-34,
users experienced a negative overall experience when
playing the game with the penalty shown to them.
Moreover, age group 35-44, found the experience to
be less pleasant (negative hedonic quality) when an
individual gamification mechanic was shown. How-
ever, users below 18 showed positive overall expe-
rience, thought the experience was pleasant (posi-
tive hedonic quality), and enjoyed the task performed
(positive pragmatic quality) when both penalties and
rewards were shown within the game. We have also
seen an interaction between gender and gamification
mechanic, where male users enjoyed the task less
when they played the game with reward or score
shown to them. This finding is an addition to tailored
gamification literature, as little is known regarding
how gamification mechanic affect men and masculin-
ity (Klock et al., 2020). Lastly, only Neuroticism,
from personality traits affected pragmatic quality of
experience, where users with low neuroticism showed
a negative pragmatic quality when playing the game
with penalty being shown. This implies that stable,
emotionally resilient people do not enjoy the task
when being penalized (e.g. lose one health point),
which is in accord with findings from tailored gamifi-
cation literature (Klock et al., 2020).
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6 CONCLUSION
Recently, gamification has gained widespread atten-
tion across various fields, particularly in education, as
a means to boost user engagement and enhance learn-
ing outcomes. Designers employ a variety of gami-
fication mechanics, such as streaks, progress points,
and time limits, to motivate users to interact with the
game. However, the effectiveness of these mechan-
ics for individuals with different personality traits and
emotional states remains uncertain. To address this
gap, we introduce Gamify, an empirical study with
65 participants that explores how different gamifica-
tion mechanics—specifically rewarding and penaliz-
ing—interact with user profiles, including personal-
ity (based on the Big Five model), emotional state,
age group, field of study, and gender, influence user
experience. For this study, we developed a trivia
game where users solve riddles by choosing from four
possible answers. Our findings reveal that user ex-
perience with specific gamification mechanics vary
based on their profiles. For example, participants un-
der 18 showed enjoyed the experience when both re-
warding and penalizing mechanics were used. Older
age groups 25-34 and 35-44 showed negative over-
all experience when being exposed to a single gami-
fication mechanic, meanwhile male users enjoyed the
task performed in-game less when being penalized for
their incorrect choices. Using Gamify, we extended
tailored gamification literature by drawing findings on
the negative effects of gamification mechanics along
with positive ones (see literature review (Klock et al.,
2020)). Insights drawn from Gamify can help game
developers create personalized experiences that en-
gage users with different needs and characteristics
more effectively.
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