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This work has received funding from the European Union’s Horizon 2020 research
and innovation program under grant agreement No. 882828 (project RAYUELA). The
sole responsibility for the content of this document lies with the authors and
in no way reflects the views of the European Commission. This work has been
partially supported by Grant PID2019-106339GB-I00 funded by MCIN/AEI/
10.13039/501100011033.

Corresponding author: Jaime Pérez (e-mail<EMAIL_ADDRESS>ORCID:
0000-0001-6044-0022).

# Serious Games and AI: Challenges and Opportunities for Computational Social
Science

JAIME PÉREZ  MARIO CASTRO , AND GREGORIO LÓPEZ  Institute for Research in
Technology, ICAI Engineering School, Universidad Pontificia Comillas, Madrid,
28015, Spain

###### Abstract

The video game industry plays an essential role in the entertainment sphere of
our society. However, from Monopoly to Flight Simulators, serious games have
also been appealing tools for learning a new language, conveying values, or
training skills. Furthermore, the resurgence of Artificial Intelligence (AI)
and data science in the last decade has created a unique opportunity since the
amount of data collected through a game is immense, as is the amount of data
needed to feed such AI algorithms. This paper aims to identify relevant
research lines using Serious Games as a novel research tool, especially in
Computational Social Sciences. To contextualize, we also conduct a (non-
systematic) literature review of this field. We conclude that the synergy
between games and data can foster the use of AI for good and open up new
strategies to empower humanity and support social research with novel
computational tools. We also discuss the challenges and new opportunities that
arise from aspiring to such lofty goals.

###### Index Terms:

Serious Games, Artificial Intelligence, Computational Social Science, Novel
Research Tools, Human behaviour

=-15pt

##  I Introduction

Games have existed in all human societies and many other animal species. While
some of the oldest board games, such as Go, Backgammon, or Checkers, are still
played today, video games have become one of the most relevant forms of
entertainment in our society, with budgets and profits far exceeding those of
huge related industries such as cinema [1]. However, since the origin of
games, they have had intentions and benefits beyond entertainment, such as
teaching social norms, strengthening social bonds, or developing imagination
and planning skills.

The rise of video games has had a remarkable social impact, transforming
mentalities and helping to establish new patterns of social interaction [2]. A
prominent example of this trend is the gamification that our lives have
experienced [3], from the workplace (e.g., Habitica, LifeUp) to romantic
relationships (e.g., Tinder, Grindr) or education (e.g., Kahoot!, Duolingo).
One of the main reasons games have such a tremendous impact on players is due
to interactivity, an almost unique feature over other cultural or artistic
elements. This attribute encourages higher motivation, engagement, and empathy
levels than in other media. It is noteworthy that at the same time as the
video game industry is rising, the board game industry continues to grow as
well [4]. We can draw a clear conclusion from all these facts: our society is
highly gamified; we love to play games, and they have enormous potential to
transform how we see the world in a much more profound way than we are often
aware of.

Figure 1: Graphical overview of the paper. The blue boxes are the applications
found for serious games and AI applied to them. The red box indicates the
challenges faced by this union for its use as a research tool. And the green
box indicates promising lines of work in this direction.

Some games —referred to as _serious_ [5]— are explicitly designed for a
primary purpose beyond pure entertainment (e.g., learning new skills,
conveying values, awareness-raising). However, being entertaining is part of
their attractiveness. The first serious games were released in a wide range of
formats, from sports to board games (e.g., Monopoly, Suffragetto), so this
concept precedes the digital era.

The current re-emergence of serious games has coincided with the eruption of
Artificial Intelligence (AI), one of the most impressive game-changers in the
history of humanity. Nowadays, and increasingly so, almost every entertainment
element and digital product are at the service of data analysis and AI
algorithms, and games are no exception. Especially given that the amount of
data available via video games far exceeds any other artistic element. AI is
already transforming society through large digital platforms, social networks,
and recommender systems. The most widespread use of these tools is in
marketing, meticulously analyzing our patterns and tastes to sell us products
and capture our attention as much as possible.

AI has demonstrated its potential to analyze and improve our understanding of
the dynamics of our societies, social interactions, as well as individual and
collective behaviors. For this reason, we firmly believe that the synergy
between serious games and AI offers an exceptional window of opportunity for
large-scale, non-invasive, and inexpensive social studies, leveraging their
disinhibition and entertainment effects, along with interactivity, to collect
large amounts of meaningful data. Moreover, games’ ”casual” and playful nature
can help break down conventional communication boundaries, encouraging
participants to interact openly and discuss topics that might otherwise be
complicated or too sensitive. In Figure 1 we can find a visual overview of
this paper’s content to guide and facilitate the reading of the document.
Including applications of serious games (Section II), the role of AI in them
(Section III), and the new lines of work and challenges opening up for
employing them as research tools (Section IV), especially in the computational
social sciences [6]. Finally, Section V presents the main conclusions drawn
from this research.

##  II Applications of Serious Games

The upsurge that serious games have been experiencing in recent years may lead
us to think this is a new phenomenon. However, the origin of serious games
dates back to the 1970s. Clark C. Abt is credited for coining the term
_serious games_ , defining them as ”games with an explicit and carefully
thought-out educational purpose that are not intended to be played primarily
for amusement”. Clark C. Abt studied the potential of games as a vehicle for
political, educational, or marketing ideas. Another of the leading figures in
the history of serious games is Ian Bogost, the author of seminal books on the
theory behind them, such as ”Persuasive Games: The expressive power of video
games” [7]. His research has leveraged video games for political, educational,
and business use in the 21st century.

Even though both concepts mirror the same social phenomenon, it is relevant to
highlight the distinction between gamification and serious games. Gamification
consists of using and integrating game elements into non-game concepts, while
serious games refer to the design of entire games for non-playful primary
purposes. Although both are concepts from the last century, they have
resurfaced in the academic and commercial arenas in recent years.

Among the first serious video games, we find examples where they are employed
to convey particular values (e.g., Captain Bible in the Dome of Darkness, The
Oregon Trail, Mobility), disease awareness (e.g., Captain Novolin), or
military training (e.g., Bradley Trainer). Nevertheless, the line between
”normal” and ”serious” games is quite blurred regarding serious games used to
convey specific beliefs or ideologies. Like any artistic or intellectual
creation, video games always carry an implicit political and philosophical
perspective. For example, popular video games such as Papers Please or This
War of Mine convey strong political messages that raise fundamental questions.
Yet, they were not developed under the idea of being explicit ”serious games”.
On the other hand, video games such as The Sims or SimCity are very
politically charged. Still, they are not usually perceived as such since they
represent a situation closer to our day-to-day life.

Focusing on serious games that consider themselves as such and have been
designed for that purpose, we find many fields where they have demonstrated
their usefulness on numerous occasions:

### Education

In this section, we focus on serious games designed for the player to learn a
series of concepts of a specific subject. To do so, the players must
demonstrate their knowledge during the game and score their performances.
Education has been one of the main focuses of action for serious games, based
on the principle that learning while having fun is possible and efficient.
This field has been explored so extensively that success and failure factors
have even been analyzed in depth [8] [9]. Prominent examples of building STEM
skills might include Garfield’s Count Me In [10], Minecraft: Education Edition
[11], the Kahoot DragonBox maths apps [12] and the LightBot coding apps [13].
Serious games for educational purposes have also become popular in higher
medical education [14], although some authors question their usefulness at
such high educational levels, possibly serving as complements to more
traditional learning methods [15] [16].

### Training

Closely related to education, this category refers to games designed for
players to learn and practice specific skills that will enable them to perform
those actions in the real world with improved safety, confidence, and
knowledge. This approach is widely used in companies where human failure is
critical or costly. One of the best-known examples is flight simulators, such
as Microsoft Flight Simulator [17], where aspiring pilots must spend hours
practicing before flying an actual commercial aircraft. There are also notable
examples of training healthcare professionals [18], cybersecurity trainees
[19] [20], and law enforcement agencies or military forces [21] [22]. Another
widespread use is training to manage complex business situations or the
administration of teams and resources, used both in actual private companies
[23] [24] and universities [25] [26].

### Awareness

Thanks to the almost unique characteristic of interactivity, games evoke deep
levels of empathy, making them an ideal vehicle to convey an awareness of
relevant social issues. A classic example is Darfur is Dying [27], which
sought to tell the story of the humanitarian crisis in the Darfur region of
South Sudan. However, we can find examples on a wide range of topics, such as
drug consumption and trafficking [28] [29], cyberbullying [30], gender
equality [31], misinformation [32] [33], climate change [34], and
environmental sustainability [35] [36] [37] [38].

### Health Treatments

This category is framed in healthcare but focuses more on patients than
professionals. Well-known examples might be the Wii Fit and Brain Training
games, which aim to have fun and stay fit (physically and mentally)
simultaneously. Other notable examples can be found in the field of mental
health therapy [39] [40], increasing self-efficacy and physical activity in
people with chronic diseases [41] [42], helping the learning process and
support of children with autism [43] [44], palliative care and memory training
for the elderly and/or people with dementia [45] [46], and guidance and
motivation in rehabilitation processes [47] [48] [49] [50]. Notably, in 2020
the US Food and Drug Administration approved the first video game-based
treatment, EndeavorRx, targeting children between the ages of eight and twelve
with certain types of Attention Deficit Hyperactivity Disorder (ADHD) [51].

### Recruitment

If we combine games’ interactivity with players’ ability to make decisions in
a well-designed environment, we can infer some behaviors or aspects of the
players’ abilities with reasonable confidence. For this reason, serious games
have also been used to optimize the recruitment process in private companies
[52] [53] and even in military forces [54]. In these games, players are
presented with complex situations where they must make decisions and act under
certain constraints or pressures. A recent notable example is the
CodinGame111CodinGame https://www.codingame.com platform, where users practice
their programming skills while playing, and many tech companies recruit
profiles they find interesting. Another great example is the GT Academy222GT
Academy https://www.gran-turismo.com/es/academy/, a competition in which the
best players of a car racing video game have the opportunity to become
professional drivers.

### Marketing & Propaganda

When the game is developed primarily for marketing purposes, it is often known
as an ”advergame”. This category of games aims to convey ideas and create
desires unintrusively and easily customizable way. It should not be confused
with games that introduce advertising during gameplay for economic profit. The
principal medium for these advergames is smartphones due to their
proliferation, ease of development, and everyday use among young people.

Major brands such as Volkswagen, Magnum, Chupa Chups or M&Ms have developed
advergames. Concerning the Recruitment category, in some cases, companies seek
to present and profile themselves through these games to attract new employees
and trainees or discover talent.

Likewise, there have also been attempts to use video games as a tool to
disseminate electoral campaigns, such as the video game Corbyn Run [55], or to
encourage citizen participation in public decisions [56] [57].

### Science & Human-based Computation

This category encompasses games to advance scientific knowledge in some way.
One of the most common approaches is employing human players to perform
seemingly trivial tasks, either too costly, too complex, or unfeasible with
finite computational resources. These tasks may include labelling data,
transcribing text, using common sense, or activities based on the human
experience.

One of the first examples of this category was ”The ESP Game” [58], in which
players, grouped in pairs, had to guess the photo labels their partner had
come up with. Google’s reCAPTCHA333reCAPTCHA
https://www.google.com/recaptcha/about/ is a recent example that has followed
this approach of using human players to label images while identifying
legitimate users for accessing online resources. Another successful example
was ”EteRNA” [59], where players had to design RNA sequences that fold into a
particular form. The solutions were evaluated to improve computer-based RNA
folding prediction models. Other prominent examples might be ”Foldit” [60] to
predict protein structures, ”Eyewire” [61] to map retinal neurons,
”MalariaSpot” [62] to help diagnose malaria cases, ”Phylo” [63] to optimize
alignments of nucleotide sequences, or ”Quantum Moves” [64] to improve how
atoms move in a quantum computer.

##  III Role of AI and Data Science in Serious Games

Games have long been the test bed for AI as they provide a controlled
environment with simple rules for algorithms to learn sophisticated
strategies. However, in recent years data science and AI have found a
different use for games as sources of vast amounts of player data. Moreover,
thus be able to get relevant information about the human players that may be
useful both outside and inside the game itself.

Nevertheless, serious games are a particular branch of the gaming industry, so
the AI and analysis techniques and their purposes differ noticeably. The
significant heterogeneity in the goals of serious games also implies
significant technical differences among them. Despite this heterogeneity, we
can discern main branches encompassing all major applications of AI and
analytics in serious games:

### Assessment

Game-based assessment is a fruitful field in serious games [65], primarily
used in education, training, and recruitment. Players are scored based on
their knowledge or skills in a particular subject. Pellegrino et al. [66]
stipulate three primary purposes of assessment: (i) to assist learning
(formative assessment), (ii) to evaluate the player’s capabilities, and (iii)
to evaluate programs. In general, collecting, analyzing, and extracting
information through educational serious games is known as Game Learning
Analytics [67].

The main difference with traditional evaluation methods or test gamification
is that game-based assessment also uses in-game and interaction data (e.g.,
response times) to evaluate the player. Numerous authors have demonstrated the
utility of using additional in-game data to evaluate students [68] [69] [70]
or to predict learning results [71] [72]. It has also been successfully tested
in recruitment processes [73]. Although nowadays, they are more of a
complement to the traditional exam-based assessment.

The techniques used are very diverse, from simple descriptive statistics and
correlations to supervized machine learning algorithms (e.g., linear
regression, decision trees, Naive Bayes, Neural Networks) [74] [75]. More
rarely, some papers use knowledge inference with Bayesian networks [76] [77],
which explicitly allows the application of psychological or mental state
models, but a flawed model will negatively influence the results
significantly.

This branch of AI applications in serious games is one of the most researched
and developed, thanks to the technology push changing how education is
delivered. However, much work still needs to be done, especially in
demonstrating that they can be better than traditional approaches [78].

### Game Design & Validation

Game design is planning the content, rules, and mechanics of a game to create
valuable interactive experiences. The large number of artistic and technical
factors involved in this process make any analytical information about the
players extremely valuable. Game validation employs data and evidence to
verify and calibrate the game tasks and their difficulty. In the case of
serious games, in addition to maintaining engagement, we also want to ensure
that the game meets its primary objective (e.g., to train players in a
particular skill, increase awareness of an issue, etc.).

Data-driven serious game design has flourished in academia in recent years,
where we can find successful examples of the use of analytical techniques to
design, improve, personalize, and validate these games [79] [80] [70] [81]
[82]. This category is also closely related to the previous one (Assessment),
as it is almost essential to use data-driven validation during the game
development stage to calibrate the players’ evaluation [83] [84]. Such
analytics can go a step further to adapt in real-time the difficulty of the
game [85] [86] and even detect when the player is frustrated [87].

In this category, due to the particular aspects of design and validation of
each game, the most commonly used techniques are descriptive statistics and
visualizations [88] [89] [90], Randomized Control Trials (to test the
usefulness of the intervention) [91] [80] and unsupervized machine learning
algorithms (to find similar types of players and common patterns in the game)
[80].

Using these analytical techniques enables creators and researchers to ensure
that their games are entertaining, engaging, and well-designed to fulfill
their objectives.

### Player modeling & Profiling

Player modeling is the creation of computational models to detect, predict and
characterize the human player attributes that manifest while playing a game
[92]. These models can be any mathematical representation, rule set, or
probability set that maps parameters to observable variables and are built on
dynamic information obtained during game-player interaction. On the other
hand, player profiling usually refers to categorizing players based on static
information that does not alter during gameplay (e.g., personality, cultural
background, gender, age). Despite their dissimilarities, these two concepts
can complement each other, contributing to more reliable player models.

The main objective of studying players is to understand their cognitive,
affective, and behavioral patterns. Recent advances in AI have demonstrated an
impressive ability for these same goals that player modeling sets out to
achieve. Although, at the moment, there is a significant lack of
interpretability in complex models. AI for player modeling is a perfectly good
fit solely when explainability is not a hard constraint.

Hooshyar et al. [93] conducted a systematic literature review that profoundly
analyzes the computational and data-driven techniques used for player modeling
between the period of 2008 to 2016. As this is such a broad and promising
field, the variety of algorithms used is immense: descriptive statistics and
correlations, path/network analysis, supervized learning (e.g., Neural
Networks, Linear Regression, Hidden Markov Models, Decision Trees),
unsupervized learning (e.g., k-means, Linear Discriminant Analysis, Self-
Organizing Map), probabilistic algorithms (e.g., Bayesian / Markov Networks),
evolutionary methods (e.g., Genetic algorithms), reinforcement learning
methods (e.g., Multi-armed bandits), etc. Most of the computational methods
used are model-free, meaning they do not impose strict assumptions on the
model. However, there are also some model-based approaches (e.g., Bayesian
hierarchical models) [94] [95] that yield more interpretable and explicit
models (e.g., psychological or cognitive) than those which do not impose
strict assumptions on the model. For instance, these models can infer the
player’s hidden parameters or mental states.

Player modeling can be helpful both inside and outside the game itself. The
most straightforward goal is to improve the game design, tailoring the content
to increase engagement and enhance the gaming or learning experience [96].
Although outside of serious games, we find some prominent examples, such as
Left 4 Dead [97], where an AI tracks player behavior and adapts future waves
of enemies to maintain rhythm and tension. Perhaps the most famous example is
the video game Silent Hill Shattered Memories [98], which uses a psychological
approach where an AI system tries to manipulate players’ emotions using the
Five Factor Model of personality [99]. Outside the game itself, the most
common use of player modeling in the gaming industry is for personalized
marketing campaigns, since the commercial sector is very interested in
understanding customer behaviors and preferences. In these cases, the games
are often presented as free to play in exchange for an intrusion into personal
privacy [100]. Besides the ”advergames” discussed in the section Marketing &
Propaganda, a famous example outside serious games is Farmville [101], which
monitored the players’ behavior to adapt Amazon marketing campaigns to them.
This business model is particularly hazardous for younger users, its main
target.

In academia, especially in psychology, experiments have been conducted using
games (serious and non-serious) for research, but primarily focusing on
analyzing how the player’s personality is projected in the gameplay patterns
[102] [103] [104] [105] [106]. However, studying psychological characteristics
or phenomenology using serious games seems an up-and-coming field, especially
if we introduce AI techniques into the equation.

##  IV Challenges and New Horizons

In the previous sections, we have discussed the main applications of serious
games and the current trends in their synergies with data science and AI. In
this section, we take up the argument outlined in the introduction about the
great potential of serious games together with AI to serve as research tools,
particularly in computational social sciences [6], examining the most critical
challenges and promising new lines of work to meet this objective.

As argued in the Introduction section, games allow research to be
entertaining, provide high levels of empathy, and have a disinhibition effect
that is highly sought after in social investigations. Games can evoke dynamic
and complex emotions in players, the manifestations of which are difficult to
capture with the traditional approaches of empirical psychology, affective
computing, or cognitive modeling research. This is primarily due to their
ability to introduce the player to a continuous mode of interaction, which
could generate complex cognitive, emotional, and behavioral reactions [92].
Therefore, the use of serious games as research tools may contribute to the
advancement of human-computer interaction and the progress of our knowledge of
human experiences.

We can already find some splendid examples of the use of games as large-scale
social research tools, such as The Moral Machine Experiment [107], which uses
a gamified environment to explore the moral dilemmas surrounding autonomous
cars. To do so, they use the framework of the classic trolley problem and
study participants’ responses to variations in different parameters (e.g.,
number of people who would die, age, gender, etc.) and the cross-cultural
differences in this decision-making [108]. We can also find some noteworthy
examples that use serious games to explore collaborative and trusting
behaviors [109] [110], understand preferences for charity
donations444MyGoodness! https://www.my-goodness.net/, or even fight cybercrime
[111].

On the other hand, the latest advances in AI allow us to analyze vast amounts
of data and find patterns or behaviors that would be very difficult to observe
with traditional analytical methods. So far, the main application given to
large AI models that study our interactions through social networks and
personal data is for marketing purposes and generating monetary value [112].
This practice has been done almost since the beginning of social networks,
without considering the negative social consequences it could have,
particularly for children and adolescents [113] [114]. With this paper, we
also aim to contribute humbly to the ”AI for Good”555AI for Good Global Summit
https://aiforgood.itu.int/,666AI for Good https://ai4good.org/ movements. We
are at a critical social, cultural, and economic moment. We must start to
consider the uses of AI that can benefit society and each individual in
particular. We firmly believe that IA has the potential to help us live better
and also to know ourselves better. Furthermore, to achieve great goals that
improve our society, it is essential to unite forces between different
branches of science (e.g., sociology, psychology, engineering, computer
science, AI, etc.), and we believe that games represent such an excellent
vehicle for this purpose.

However, in order for serious games to be able to meet these major goals, they
must face some critical challenges:

  * •

Game design: Whether a game can serve as a valuable research tool depends
strongly on whether it has good design and playability. Designing a game is a
complex process involving many artistic and technical aspects that can not be
wholly rationalized from a scientific standpoint.

  * •

Validation and generalizability: One of the most complicated aspects of using
serious games as a means of research is demonstrating that their results are
as valid as traditional methods. Although we already have numerous examples in
some branches, such as game-based assessment or the reflection of player
morality into in-game moral dilemmas [115] [116], there is still a long way to
go in this aspect. This is also because each game (and its purpose) is
different from the others and therefore requires individual validation in most
cases.

  * •

Data scarcity: In recent years, it has become clear that to take full
advantage of AI, we need large amounts of data to feed it. Apart from a few
exceptional cases [107], serious games academic experiments suffer from small,
biased, and heterogeneous datasets. If we aspire to use them as social
research tools, we must find ways to get more participants, make the best use
of available data or establish appropriate methods of sharing sensitive data.

  * •

Explainability: Many of today’s AI tools can be highly complex, if not
completely opaque (so-called black box models). The general trend in computer
science also drives this to focus more on prediction than explanation.
However, aspiring to use these tools to study human and social behavior
implies a deep understanding of the outcomes that AI provides. While
considerable progress has been made in explainable AI techniques, many hurdles
still exist [117].

  * •

Ethical considerations: When dealing with personal data (whether anonymized or
not) and AI, we must seek unequivocal ethical standards. The potential
benefits must outweigh the risks, as the participants’ safety and well-being
must be the top priority, especially when dealing with data from children or
people at risk of exclusion. Achieving these standards is genuinely complex
because computer scientists and social scientists tend to have different
approaches to research ethics [118].

Despite the challenges mentioned above, we can also find promising new
horizons and future lines of work regarding the interplay between serious
games and AI:

  * •

Synthetic data: The AI field has extensive experience in developing agents
that aim to win a game [119]. However, in recent years, we are also
experiencing the emergence of novel synthetic data generation techniques
capable of modeling or mimicking human behavior in some aspects [120], and
impressive new data augmentation techniques such as Generative Adversarial
Networks [121]. Concerning the challenge of data scarcity, this is a promising
line of work in which we could make the most of the limited data available and
build models that help us better understand players’ motivations in decision-
making.

  * •

Data sharing: The field of computational social science has faced many
difficulties in finding and sharing open data, especially from private
companies [122]. However, the field of serious games is in a much more
advantageous position in this respect, as it does not involve such an amount
of sensitive data. Moreover, using anonymization and privacy-preserving
algorithms has proven to be very useful in recent years. With this promising
line of work, we can address the poor sample size that traditional social
science has had and share meaningful data from serious games at scale to
enhance collaboration and motivate research.

  * •

Causality: The social sciences have traditionally prioritized interpretable
explanations of human behavior, mainly invoking causality through randomized
controlled trials. However, as powerful as these techniques are, they are also
very costly in terms of resources and money. On the other hand, computer
scientists have traditionally been more concerned with developing accurate
predictive models, whether or not they correspond to causal/interpretable
mechanisms. Nevertheless, in the last years, we are experiencing a resurgence
of computational causality techniques [123] [124], even from observational
data (i.e., quasi-experiments) [125], allowing us to explain with greater
robustness the workings of the systems under study. Besides, it makes explicit
the assumptions of the computational model and the scientist performing it,
helping us to make research more open to discussion and to rethink plausible
alternatives for existing explanations. If our ultimate goal is to better
understand individual and collective human behavior, it is critical to
integrate predictive and explanatory approaches to scientific research [126].

##  V Conclusions

Gaming, both for entertainment and utility purposes, has been indispensable
throughout the development of humankind. The flourishing of AI in recent
times, coupled with the vast amounts of meaningful data that can be collected
and transmitted through games, creates a unique window of opportunity to use
serious games as tools for social research.

In this paper, we have reviewed serious games’ main applications and their
synergies with AI. We can already find numerous successful examples of serious
games in education, science, business and social interests. The great
potential of games to transform society should not be underestimated and
deserve more and deeper inquiry. In addition, we have identified some
challenges and promising new lines of work for using serious games as research
tools. By doing so, we aim to motivate researchers to pursue these lines of
work and help them to identify potential applications of serious games for
beneficial social objectives. We also want to encourage interdisciplinary
research, which is essential in this field of science, and which we firmly
believe is how the future of science should ultimately be.

We are at a critical juncture as a society, where we are beginning to realize
that we need to change the motivations and goals by which we make progress. AI
is a game changer that can bring immense benefits or harm to society. It is
time to start breaking new ground in using these technologies for the common
good. What better way to do it than by playing?

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|  Jaime Pérez received the B.S. and the M.Sc. degree in Telecommunication
Engineering from the Universidad Politécnica de Madrid (UPM) in 2018 and 2021,
respectively.
Currently, he is pursuing a PhD in Engineering Systems Modeling at the
Institute for Research in Technology, Universidad Pontificia Comillas, within
the framework of the EU H2020 project RAYUELA (empoweRing and educAting YoUng
pEople for the Internet by pLAying). His areas of interest are Serious Games,
Machine Learning, Deep Learning, Synthetic Data, and Applied Artificial
Intelligence.
---|---
|  Mario Castro holds a Ph.D. in Physics from the Universidad Complutense de
Madrid (2001). He has been the Principal Investigator of 5 Spanish Ministry of
Science research projects. He has participated in 10 projects, including 3
European projects within the framework of Marie Sklodowska-Curie Actions (of
which he has been the IP of the Comillas node) and H2020.
These projects’ central theme was applying Statistical Mechanics methods to
model complex systems, with particular emphasis on modeling and predicting
experimental data. His primary research approach translates analytical and
computational methods into actual experiments. In this sense, most of his
research has been carried out in collaboration with experimental groups from
all over the world, from Immunology to Computational Social Science. He has
been a visiting researcher at the Los Alamos National Laboratory (LANL) and
completed a sabbatical stay at the University of Leeds (2016-2017), of which
he is currently Visiting Professor.
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|  Gregorio López (SM’20) received his PhD in Telecommunications Engineering
from Universidad Carlos III de Madrid (UC3M) in 2014. He currently works as
Assistant Professor in the ICAI Engineering School of Comillas Pontifical
University, where he also serves as the coordinator of the Cybersecurity MSc,
and as Senior Researcher in the Institute for Research in Technology. He
gathers wide experience in close-to-market research gained through his
participation in more than 10 national and European research projects. As a
result of his research activity, he holds an European Patent and has published
more than 20 papers in top-tier conferences and journals. His current research
interests revolve around the optimization of Machine-to-Machine (M2M)
communications networks based on analysis and simulation, cybersecurity, and
data analytics for the Internet of Things (IoT), and the use of technology and
the Internet, being currently the coordinator of the European H2020 project
RAYUELA (empoweRing and educAting YoUng pEople for the Internet by pLAying),
which addresses this latter topic.
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