=Paper=
{{Paper
|id=Vol-3605/12
|storemode=property
|title=AI in Board Game-based Learning
|pdfUrl=https://ceur-ws.org/Vol-3605/12.pdf
|volume=Vol-3605
|authors=Andrea Tinterri,Marilena Di Padova,Francesco Palladino,Giordano Vignoli,Anna Dipace
|dblpUrl=https://dblp.org/rec/conf/aixedu/TinterriPVD23
}}
==AI in Board Game-based Learning==
https://ceur-ws.org/Vol-3605/12.pdf
AI in board Game-Based Learning
Andrea Tinterri1, Marilena di Padova 2, Francesco Palladino3, Giordano Vignoli4 and
Anna Dipace2
1 IUL Telematic University, Via M. Buonarroti 10, 50122, Florence, Italy
2 University of Foggia, Via A.Gramsci 89/91, Foggia, Italy
3 University of Modena and Reggio Emilia, Via Università 4, 41121 Modena, Italy
4 IESS, Istituto Europeo Studi Superiori, P.zza Prampolini, 2/A, 42121 Reggio Emilia
Abstract
Despite receiving less attention in educational research compared to digital games, boardgames show
great potential as a learning environment in many educational scenarios. They promote acquisition of
disciplinary knowledge and key competences, generate a sense of physical “togetherness”, can be
employed in situation of social and economic disadvantage, and can be modified (or “modded”), for
better alignment with disciplinary content. The use of games in European schools is very limited;
teachers see the potential of games for learning, but their competence in the use of games for learning
is superficial and limited to personal experience. High-performance AI systems such as GPT-4 have
emerged as a potential game-changer in education, as a collaborative partner to assist teachers in
learning design or to automatize decision-making processes. Despite known limitations, trained LLMs
show promise in executing educational tasks. This study explores whether trained High-performance
AI can facilitate teachers in the creation of boardgame-based learning units, by bridging their knowledge
gap in game knowledge and game-based instructional skills. Using the GDBL ID model, the most
comprehensive available instructional model for the creation of boardgame-based learning units, in this
exploratory study we instructed Chat GPT to address two key phases of bGBL design: the choice of the
game for the learning activity and the personalization of the game for constructive alignment and
inclusion. Evaluation of the output by GBL experts highlights the potential of AI tools for bGBL
Keywords
Board Game-based learning; instructional design; GDBL ID; Personalization of games 1
1. Introduction
The advent of Artificial Intelligence (AI) has represented a revolution in different areas, including
education. AI tools may redefine the role of the teacher and the entire education process, but their
adoption is still at an embryonic stage and presents a number of open questions about prospects,
risks and opportunities [1]. The use of Artificial Intelligence in the educational context (AIEd) has
represented a growing interdisciplinary field since the 1970s to improve course design and
expected student outcomes [2]. The aim is the creation of technological, innovative, and
intelligent AI-powered systems to make learning personalized, engaging, and flexible. However,
this process has introduced several opportunities and challenges for educational innovation, such
as a move toward tailoring education to each student, challenging the traditional role of teachers,
within an increasingly complex educational system. Indeed, the literature highlights the
'imperative change': AI is increasingly understood as an inevitable change to be adapted to meet
the needs of a technology-based society. Second, especially in higher education, AI is
decentralizing the figure of the teacher, spreading his or her role and authority among all the
actors and tools involved in the learning process [3]. Artificial intelligence systems serving
teaching and learning processes have distinctive traits that include grouping them into:
Proceedings Acronym: Proceedings Name, Month XX–XX, YYYY, City, Country
tinterri.andrea@gmail.com (A. Tinterri); marilena.dipadova@unifg.it (M. di Padova); anna.dipace@unifg.it (A.
Dipace)
0000-0001-5891-505X (A. Tinterri); 0000-0002-2105-7095 (M. di Padova); 0000-0001-9826-073X (A. Dipace)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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Workshop ISSN 1613-0073
Proceedings
• system supporting the process such as a platform or interactive learning environment to
implement activities.
• tool that fulfills partial requests at different stages of the process like the use of
assessment software.
• additional assistant that helps stakeholders better understand and direct the different
features of the process [4].
Intelligent teaching assistants represent a frontier in teaching with AI to provide students and
teachers with a range of functions to help and/or assist them in various activities of daily routine.
Especially important for teachers is support in instructional design to promote effective and
meaningful teaching [5]. For this reason, advanced AI systems such as GPT-4 are emerging as
potential catalysts for change in the educational field. ChatGPT is a conversational assistant,
launched by OpenAI in November 2022, that uses advanced natural language processing
techniques to provide responses to received input. It can generate coherent, systematic, and
information-rich responses [6]. Although there are recognized limitations on its use[7], there is a
need to experiment with adherence to pedagogical frameworks [8] and methods such as
Evolutionary algorithms [9] to develop quality educational resources [10]. For teachers, AI
presents itself as an important resource since the use of intelligent tools elicits positive attitudes
[11] and offers professional development: it improves teaching competence [12], instructional
practices [13, 14], promotes self-reflection [15] and understanding of learning processes.
Generative artificial intelligence tools, when integrated into a pedagogical framework for
instructional design, can enhance the effectiveness of the teaching and learning process. By using
these tools with a design approach, educators can create and deliver personalized and enriching
educational pathways. Implementing the instructional design matrix introduces innovative
methods to engage students, adapt content, and promote personalized learning [16]. However,
most studies analyze the use of AI in education from a predominantly technological point of view,
with few pedagogical lunges and rare moments of experimentation that do not allow for an in-
depth understanding of the complex roles of AI in education and learning processes and the
dimension from a design perspective. To fill these gaps, this research proposes an exploratory
study to understand how to integrate AI in learning design. The purpose of the article is to
investigate a design model for the use of AI in board game-based learning instructional design
processes and use Chat GPT for choosing and customizing games, automizing some steps of the
proposed model.
1.1. A working model for AI in board Game-Based Learning design
The ADDIE model [17] is one of the most well-known tools to design and manage a learning
project. It is based on five main instructional phases which stand for Analysis, Design,
Development, Implementation, and Evaluation (Table 1). A recent paper by Ch’ng [18] discusses
the two distinct approaches that AI can have in instructional design: Assisted Intelligence and
Autonomous Intelligence. The first helps humans to perform tasks faster and better; the second
is based on automated decision-making processes, producing outcomes without human
intervention. As Assisted Intelligence, AI can be used within the ADDIE framework to help
designers in the analysis of needs using learning analytics, by using data to support the design,
development, and implementation of instructional projects, and to help the evaluation process
based on student performance. As Autonomous intelligence, AI can be trained to curate firsthand
the learning journey and plan, generate personalized learning content, provide real-time
assistance, and provide timely and automated assessment and evaluation. A recent systematic
review by Yan et al. [19] has identified 53 different educational tasks that could potentially benefit
from AI-based automation.
Within this context, in this study we discuss and provide preliminary evidence of the use of
Autonomous Intelligence in the context of board-game based learning (bGBL) design. In recent
years, gaming emerged as a valuable educational support, not only for its ability to engage and
motivate students, but also to develop knowledge, skills, and competences [20–23]. Whereas
research in the last decades has mostly focused on digital games [24], board games have a
longstanding tradition of use for educational purposes [25, 26] which is accompanied by growing
empirical evidence [27]. Board games, far from being supplanted by their digital counterparts,
are enjoying significant commercial success [28] driven by an ever-increasing number of original
games. Board games are active, social, fun, and situated experiences which can be leveraged in a
variety of educational scenarios [29, 30] to promote acquisition of disciplinary knowledge [27],
key competences [31, 32], generate a sense of physical “togetherness” [33], can be employed in
situation of social and economic disadvantage, and can be modified (or “modded”), to better align
with disciplinary content [34–36]. Researchers have especially focused on the modification (also
called “modding”), or personalization, of commercial board games [37] to better align with state
curricula [38], educational contexts or specific learning goals [39–41] However, only recently
researchers have started addressing the issue of guiding teachers in evaluating [42] and choosing
[43] commercial board games for instructional purposes.
When harnessing the main difficulties in teacher’s adoption of game-based learning (not limited
to board games, but also including digital games), teachers report that they do not feel adequately
prepared to include games within their curriculum [44, 45] and fear the increased workload [46].
The literature suggests that the main prerequisites for effective GBL adoption include the
knowledge of games [29, 47], the ability to analyze games for their learning potential [48], and
the ability to integrate games in the teaching and learning practice according to the desired
learning goals [49]. bGBL requires teachers to carefully consider the underpinning pedagogic
learning design: For instance, they can use either off-the-shelf games (games designed with a
recreational purpose), educational/serious games (games that have been designed with a
learning-first approach), modify, or personalize, existing games, or create new games ad hoc [50].
Furthermore, games can fill distinct roles within a learning unit according to the scenario for
which they are used [29, 51]: to this aim, teachers must be able to gauge the relative costs and
opportunities for each scenario.
Unfortunately, most teachers do not possess such competencies: according to a study by Persico
et al. [52], Italian and English teachers’ competence in the design of GBL activities is superficial
and restricted to personal experience; this is reflected in a very limited adoption of games in
Italian schools and higher education settings [53, 54]. Teachers seem to be familiar with board
games that they played in their youth, such as Monopoly, Risk, Naval Battle, or traditional board
games, such as Chess, but are not acquainted with modern board games [55]. To this aim, this
study investigates whether AI systems can be used to help bridge the gap in teacher preparation
by assisting them in the effective design and implementation of activities based on the use of
board games. As to our current knowledge there is no available literature concerning the use of
high-performance AI systems to support teachers in the design of bGBL, this explorative study
aims to evaluate the potential of Chat GPT in automating specific tasks in bGBL design, specifically
the choice of games according to the context, learning goals, and game goals and the
personalization of games to obtain a better alignment between game and learning goals.
Within this context, the GDBL ID framework developed by Andreoletti and Tinterri [51] is
currently the most in-depth reference for bGBL design; it has four defining features that help set
it apart from existing instructional frameworks upon which the model iterates and improves:
• It is based on established pedagogical frameworks: the Technological Pedagogical and
Content Knowledge (TPaCK) [56], the Play Curricular Reflection Discussion (PCaRD)[57],
and the Inquiry, Communication, Construction and Expression (ICCE)[58].
• It integrates current best evidence from game studies, pedagogy, and cognitive science
perspectives to game-based learning.
• It focuses on the different roles played by teachers in designing and applying game-based
activities [59, 60].
• It focuses on the personalization of the game and learning experience, through both in-
game and out-game changes to make help constructive alignment and promote
accessibility [34, 37, 38, 61]
The GDBL ID framework is structured to facilitate the design of game-based teaching and
learning activities, considering both instructional guidance as well as the need to progressively
develop critical game literacy required to design and personalize game-based learning
activities. It provides a step-by-step instructional guide structured on the five ADDIE steps
(Table 1): starting from the definition and alignment of learning goals and game goals,
assessment and evaluation criteria to the choice and personalization of the gaming. In
particular, the development phase of the model includes three steps that are specific to bGBL
[42]: game selection, game choice, and personalization (Table 1).
Table 1
Phases of the GDBL ID model (adapted from [50]).
ADDIE Phase of GDBL model Description Specific
to bGBL?
A(nalysis) Analysis of needs Determine students’ characteristics and No
needs
D(esign) Learning outcomes Expected learning outcomes in terms of No
knowledge, skills, and attitudes
Game goals Definition of the goals attainable through Yes
gameplay
Acceptable evidence Quantitative and qualitative data that can No
be obtained to assess learning outcomes
during and after gameplay
D(evelopment) Game shortlist Selection of a shortlist of potential games Yes
Game choice Selection of the game based on alignment Yes
with design goals
Game personalization Modify either the game or the game Yes
experience for better alignment
Instructional strategies Define the lesson plan No
I(mplement) Implementation Realize the activity in the classroom No
E(valuate) Evaluation and revision Evaluate the impact of the activity and No
revise
1.2. Using Chat GPT to automate game choice and personalization.
In this exploratory study, we investigated the quality of Autonomous Intelligence design of Chat
GPT in the design of bGBL. To this aim, we focused on two steps of the GDBL ID model that are
specific to bGBL: the choice of a board game based on the alignment with the context, learning
objectives and game goals, and the personalization of the game to improve this alignment.
we simplified the step of game selection in two ways:
First, the process is stepwise and does not allow backtracking from analysis to the choice of
games, as in the original model ([50], p. 127).
Second, we limited game selection to the description of eight modern board games that were fed
to the AI. The reason for this choice is that, since we referred to human experts to evaluate the
quality of Chat GPT output, we had to make sure that they were competent in the games analyzed.
Thus, the research questions for this study are the following:
1. Is Chat GPT able to identify salient features of modern board games?
2. Is Chat GPT able to choose appropriate board games according to context, learning
objectives and game goals?
3. Is Chat GPT able to define adequate personalization strategies to better align existing
games according to context, learning objectives, and game goals?
4. Is Chat GPT able to provide reasonable explanations for the choices made?
2. Methods
In this exploratory study, we analyzed Chat GPT’s ability to automate two steps of the GDBL ID
model specific to bGBL, namely Choice of the game and Personalization. To this aim, we asked a
trained teacher to provide the Analysis of needs, learning goal, game goals and expected learning
outcomes for a bGBL activity (Table 1). In parallel, the researchers curated a list of eight modern
board games (Carcassonne, Catan, Codenames, Concept, Dixit, Monopoly, Pandemic, Risk) that the
AI could choose from to develop the activity. The games were all commercially successful modern
games who had previously been used by the expert evaluators in GBL activities. Successively, we
wrote the prompts stepwise to allow Chat GPT to perform the different phases:
a) Describe the eight games according to Theme, Structure, Genre, and Main Mechanisms
[43, 51].
b) Choose the most appropriate game for the learning task according to the given contest,
learning goal, game goals, and expected learning outcomes, providing a rationale for
the choice according to five pre-established criteria (Table 4).
c) Propose up to three in-game or around-game personalization [37, 51, 62] to better
align the game experience with the goals of the activity.
For this study, we used Chat GPT with GPT-4 model (3rd of August 2023 version). We set the
following parameters according to Sridhar et al. [8].
• temperature = 0.7 (standard)
• max_tokens = 5000
• top_p = 1 (standard)
• frequency_penalty = 0
At the moment of writing, there is clear evidence that the quality of the prompts is instrumental
to improve the quality of the model answers [63, 64]; however, there is not yet an established
methodology as to how to build effective prompts in AI-assisted and AI-autonomous instructional
design. For this study, we manually designed prompts adhering to the recommendations of
Bozkurt and Sharma [63], White et al. [65] and Liu [64]. We decided to use specific rather than
generic prompts [64]. We first defined a persona and structured key ideas when they referred to
specific aspects of the model [65]. We provided context for the answer and, for game selection
and personalization steps, fed the model a few examples [66]. We present the prompts in tables
according to Sridhar et al. [8]. We also asked Chat GPT to provide post hoc summary tables of the
answers given (Tables 3, 6, and 7). Furthermore, to ensure that Chat GPT answers could be
replicated, we ran the same prompts on two different machines, with separate Open AI account,
to check for internal consistency of answers. ChatGPT has limited ability to evaluate the accuracy
of the generated information, as it lacks the ability to assess the credibility of the data it was
trained on [67]. Thus, for the evaluation of the answers, we asked three bGBL experts to
independently evaluate AI answers according to an evaluation rubric based on four dimensions:
Game description, choice of the game, personalization of the game, and rationale provided. We
asked the experts to provide short qualitative feedback for each dimension.
3. Results
First, the AI was asked to provide a short description of the games pre-selected according to their
main characteristics (Table 2). This step was taken to ensure that the AI was able to extrapolate
the salient features of the games in line with the GDBL ID model ([51], p. 129).
Table 2
The table shows the user message specifying the context for the game descriptions to be generated.
[USER] You are a curricular development expert system focused on board Game-based learning
(bGBL). bGBL is a learning strategy aimed at promoting knowledge through the use of board games
in the classroom. Your first task will be to describe five board games according to the following
characteristics: 1) Theme [147 characters] 2) Structure. [219 characters] 3) Genre. [257 characters]
4) Main mechanism (or mechanic). [211 characters] The user will provide you with a few examples,
then will give you the names of the board games you must describe.
[4 sample descriptions (315 characters)]
Chat GPT provided descriptions for the games summarized in Table 3:
Table 3
Chat GPT-generated table summarizing the description for each game.
Game Theme Structure Genre Main mechanism
Dixit Abstract, Multiplayer Party Game, Storytelling, Voting
Imagination Family
Pandemic Global Epidemic Cooperative Strategy, Hand Management, Set
Family Collection, Point-to-point
Movement
Codenames Espionage, Word Two-Team Party Game, Word Association, Deduction,
Association Game Family Team-based play
Concept Abstract; Multiplayer Party Game, Deduction, Pictorial
Communication Family Communication
Carcassonne Medieval Multiplayer Family, Tile Placement, Area Control
Southern France Eurogame
Monopoly Real Estate, Multiplayer Family, Roll and Move, Trading,
Capitalism Strategy Property Management
Risk Global Multiplayer Strategy, Area Control, Dice Rolling,
Domination, Wargame Player Elimination
Warfare
Catan Island Settlement Multiplayer Family, Trading, Hand Management,
Strategy, Route Building
Eurogame
All experts independently rated Chat GPT’s ability to describe the main feature of the games as
“Good”. Experts agreed that descriptions were generally accurate, with a few imprecisions. One
expert noted that “The themes identified were sometimes very articulate (Medieval Southern
France) and sometimes slightly inaccurate (Codenames is mostly abstract).” Second, we asked
the AI to choose among the described games which fitted better with the provided instructional
context, learning goals, and game goals (Table 4).
Table 4
The table shows the user message specifying the context for the choice of the game and, in blue, Chat
GPT’s response.
[USER]The user will provide you the context, learning goals and game goals of a bGBL learning unit.
You will evaluate which of the games you described (Dixit, Pandemic, Codenames, Concept,
Carcassonne, Monopoly, Risk) is more suited to achieve the learning and game goals. You will argue
this decision based on five key indicators. 1) Accessibility 2) Setting (time required to set up and
play the game) 3) Content (whether the content/thematic knowledge is used to address game
challenges or is merely accessory 4) Learning Curve 5) Opportunities to assess success standards
provided internally by the game.
[Translated from Italian] Class III secondary school. The class consists of 24 pupils and is rather
heterogeneous in terms of interest, application in study and participation in teaching activities. The
class group shows lively but essentially responsible behavior. The levels of basic skills are
heterogeneous. [362 characters]. Pupils with Special Educational Needs: 1 student with DSA -
dyslexia, reading and writing difficulties. Benefit from special teaching materials and the use of
computers to overcome the barriers of dyslexia. 1 student with ADHD - Attention
Deficit/Hyperactivity Disorder, difficulty maintaining attention for prolonged periods; benefits from
short learning sessions and frequent breaks.
LEARNING GOAL: The learner interacts effectively in a variety of communicative situations, through
dialogic modes that are always respectful of the ideas of others. Uses metalinguistic knowledge to
understand the meanings of texts more precisely.
EXPECTED LEARNING OUTCOMES:
Intervene in a conversation or discussion, class or group, with relevance and coherence, respecting
timing and turns to speak and making a positive personal contribution.
Use one's knowledge of text types to adopt functional strategies for understanding while listening.
GAME OBJECTIVES:
Use metalinguistic skills to understand context and communication skills to interact effectively.
Take on different roles to achieve specific goals in the game.
Use active listening and constructive dialogue strategies to counter opposing arguments.
Use dialogue and metalinguistic skills to reach a win-win agreement.
Use communication and metalinguistic skills to solve problems in a game environment that
simulates a real-world situation.
[CHAT GPT]
Codenames:
Accessibility: High. Word-based game, suitable for language-focused objectives.
Setting: Quick setup, games last about 15-30 minutes.
Content: Central. The game revolves around understanding and communicating word associations.
Learning Curve: Low. Simple rules, easy to grasp.
Opportunities for Assessment: High. The game provides immediate feedback through correct and
incorrect guesses. [1152 characters]
While Concept is also a strong contender due to its emphasis on non-verbal communication, the
specificity of the learning goals around metalinguistic knowledge and active dialogue make
Codenames a slightly better fit for this particular context.
The three experts agreed that Codenames was the best choice among the available games in terms
of alignment for the context. One expert noted that Codenames, despite being a game based on
words, was playable also by student with dyslexia. Another noted that “the game's short duration
and setup, coupled with its "tight" gameplay, (...) fits well with the characteristics of the class and
the presence of an ADHD student”. Furthermore, the experts agreed on the quality of the choice
considering learning goals. “Codenames is a sensible choice for the goal of developing
metalinguistic competence, as it allows reasoning on secondary or metaphorical meanings of
words. It also has different roles and teamplay which help improve effective communication
between students” wrote one expert. Finally, all experts agreed that the chosen game was the best
choice also considering the game goals defined by the teacher. One expert adds: “Codenames fits
all the game goals of the units, with the partial exception of the condition "simulates a real
environment" as the game is mostly abstract. Another sensible choice would have been Pandemic,
which was however less focused on metalinguistic communication”. The last step that we asked
the AI to automatize was the choice of personalizations that could help a tighter alignment
between the goals of the learning unit and the game of choice. A few examples of in-game and
around-game personalization [51] were given to the AI as part of the input (Table 5).
Table 5
The table shows the user message specifying the context for the game personalization.
[USER] You can personalize (modify) the game you selected to better align with the needs analysis,
learning objectives and game goals. Personalization can be either in game or around game. In game
personalization implies altering the actions of the player via changes to the game rules and/or
pieces. Around game personalization acts on the game experience, including interaction among
players, between players and the teacher as well as the context where the game activity takes place.
The user will provide a few examples of in game and around game personalization [669 characters].
The AI suggested three personalizations, two around-game and one in-game, summarized in
Table 6.
Table 6
Chat GPT-generated table summarizing the game personalization proposed.
Personalization Description Rationale
Type
Around Game Play in couples for peer learning and Promotes collaboration and peer
support learning. Supports students with special
education needs.
Around Game Insert moments of reflection after Encourages deeper understanding,
each round/game for deeper discussion, and alignment with
understanding and discussion metalinguistic objectives.
In Game Modify luck/skill balance by Promotes more dialogue, emphasizes
introducing “discussion tokens” to metalinguistic understanding, and
promote more dialogue and offers an additional strategy layer.
metalinguistic understanding
The decision to play in couples was motivated by the AI system as follows:
• It allows stronger students to support peers with challenges, fostering collaboration
and peer learning.
• The student with ADHD, who benefits from short learning sessions, can switch roles
with their partner, ensuring active engagement without being overwhelmed.
• The student with DSA (dyslexia) can have a partner to help with reading, reducing the
barrier and allowing them to focus on the metalinguistic and communicative aspects
of the game.
The second personalization, the inclusion of moments of reflection after each round, is aimed at
having students discuss and reflect on in-game events, such as their choice of clues and the
rationale behind them, how they interpreted the clues given by the spymaster, and the
metalinguistic challenges they faced. According to the AI, this personalization “aligns well with
the learning goals of understanding and using metalinguistic knowledge and effectively
interacting in communicative situations.”
Finally, Chat GPT proposed a new in-game rule: the introduction of “discussion tokens”.
“Each team gets a limited number of these tokens at the start. Using a token allows the team an
additional 30 seconds to discuss and deliberate on a particularly challenging clue.”
According to the AI, this rule “promotes more dialogue and emphasizes the importance of
metalinguistic understanding and communication over the rush of the regular timer. It also
provides an additional layer of strategy, as teams must decide when it's most beneficial to use
these tokens”.
All three experts agreed that the proposed personalizations would be very fitting given the class
context. One expert notes: “Playing in couples is potentially beneficial for students with special
needs and allows them to play the game together with their peers. The classroom shows interest
in discussion when appropriately stimulated, therefore the other personalization could improve
cognitive, social, and emotional engagement with the learning activity”. The experts judge that
the personalizations are a good fit with the learning objectives, although one notes: “The rationale
for ordered discussion is, in my opinion, not treated adequately. The choice of tokens for group
discussion is interesting but this could make the order of player intervention chaotic. Maybe
defining a set number of tokens for each player could mitigate this issue”. Another expert notes
that “the personalizations provide opportunities for students to intervene and discuss (...)
however they barely address the second learning goal related to knowledge of different types of
text”. The experts also convene that the personalizations align significantly with the game goals,
and all three experts agree that they are sufficiently easy and could realistically be implemented
in the classroom setting.
The experts were asked to rate the quality of the rationale provided by the AI for the choices
taken. All experts agreed that the rationale for the game selected was good but could be better
motivated. All three note how synthetic the explanation is. One expert suggests that the prompt
could specify a point-per-point comparison with the excluded games. The second highlights that
the discussion concerning “accessibility” did not mention students with special needs and how
the game suited them; “furthermore, the AI seem to interpret differently the cue-giving in
Codenames compared to Concept, whereas it is basically the same mechanism”. Finally, the third
expert hypothesizes that a more specific request prompt would have probably returned a more
satisfying explanation. Conversely, the experts agree that the rationale for the excluded game is
very convincing (Table 7).
Table 7
Chat GPT-generated table summarizing the game inclusion/exclusion rationale.
Game Criteria for Inclusion Criteria for Exclusion
Codenames Emphasizes word associations, N/A (Selected game)
cooperative nature, and active
listening. Aligns with metalinguistic
and communicative objectives.
Dixit Promotes creativity, storytelling, Abstract nature may not directly address
and interpretation. metalinguistic knowledge or focused
communication skills.
Pandemic Fosters teamwork, strategic Theme and mechanics more about disease
thinking, and collective decision- control, less on metalinguistic understanding
making. or structured conversation.
Carcassonne Emphasizes spatial reasoning, More about building territories than active
strategy, and area control. dialogue or metalinguistic challenges.
Monopoly Involves negotiation, property Can be lengthy and might not focus on
management, and strategic metalinguistic and communicative skills
decision-making. targeted.
Risk Promotes global strategy, player Focused on warfare and global strategy, less
interactions, and battles on active communication.
Concept Emphasizes non-verbal While focusing on non-verbal communication,
communication and understanding it might be less direct in addressing
of universal icons. metalinguistic understanding compared to
Codenames.
One expert notes that “The AI does an impressive job of identifying differences between Dixit and
Codenames, considering that both are language- and cue- based game”. Finally, two experts
considered that the rationale given for the personalizations is convincing but should be better
explained and more user-friendly if addressed to non-experts. The expert evaluations are
summarized in Table 8.
Table 8
Summary of the expert evaluation for Chat GPT performance.
Dimension Indicator(s) Excellent Good Average Poor
Game The description of the proposed 🟠🟢🔵
description games is precise in terms of Theme,
Structure, Genre, and Mechanism
Game The chosen game fits the class 🟠🟢🔵
choice context
The chosen game fits the learning 🟠🟢🔵
goals of the unit
The chosen game fits the game 🟠🟢🔵
goals
Game The proposed personalization(s) fits 🟠🟢🔵
personaliza the class context
tion
The proposed personalization(s) fit 🟠🟢🔵
the learning goals of the unit
The proposed personalization(s) fit 🟢🔵 🟠
the game goals
The proposed personalization(s) are 🟠🟢🔵
easy to implement
Rationale The rationale provided for the 🟠🟢🔵
chosen game is convincing
The rationale provided for the 🟠🟢 🔵
excluded game is convincing
The rationale provided for the 🔵 🟠🟢
personalization(s) is convincing
4. Discussion
In this study, we started an exploration of the potential of Chat GPT as a support for teachers in
the design of bGBL learning units. The literature highlights how, despite a generalized trust for
the learning potential and effectiveness of GBL[53, 54], games are seldom used in the school and
other formal learning contexts. This seems due, at least in part, to lack of game knowledge and
lack of GBL competence by teachers. Our hypothesis is that high-performance AI models can help
bridge the gaps in teacher preparation, not as a substitute for the teachers’ creativity and
professional competence but to help them overcome their fears due to lack of confidence with the
medium and jumpstart the use of games in the classroom by supporting the design, development,
implementation, and evaluation of bGBL activities. Our preliminary results indicate that Chat GPT
has the potential to provide effective support in several steps of bGBL design, notably the choice
of games according to the students’ characteristics and needs and the specific learning and game
goals identified, as well as the personalization of in-game and around-game aspects to achieve a
better constructive alignment between the existing game and learning goals. The experts were
largely in agreement that the AI was able to extract the salient characteristics of the games
proposed (RQ1), selected the game that fit best for the proposed activity (RQ2), suggested
sensible, realistic, and useful personalizations (RQ3), and gave reasonable justification for the
choices operated (RQ4). However, according to the nature of an exploratory study, the results are
subject to several limitations: In the first place, the analysis is limited to a single learning unit; it
can be argued that Chat GPT’s ability to provide a sensible choice could be due to a particularly
good match between learning settings and the game of choice. Providing a wider range of learning
contexts, specific learning and game goals falls beyond the scope of this exploratory study:
however, this issue should be explored in further studies to ensure reproducibility. Second, we
restricted the task of AI by only providing the choice of the game from within a limited list. This
effectively prevented us to test Chat GPT ability to select from a wider range of options and could
also have facilitated the game analysis step, as the games were rather heterogeneous in their
nature and mechanics. The model was very good at discriminating and motivating the choice
between games based on linguistic cues (Codenames, Concept) and metalinguistic ability
(Codenames, Concept). However, further, finer-grained analysis between games with similar
mechanisms would be more apt at stressing Chat GPT’s abilities further. Third, due to the
experimental setup we could only test AI-autonomous creation, whereas AI-assisted creation is
likely to be at least as interesting an avenue to develop bGBL units and promote teacher upskilling
in game knowledge and GBL design. Fourth, the setup allowed no comparison between human-
made and AI-made decisions. Future studies could address this issue by blind evaluation of
human-developed and AI-developed GBL units to better understand the model potential. Fifth,
the experts were aware of evaluating AI-created decisions, as they were provided the entire
conversation. This, depending on each expert’s views and beliefs concerning AI, could have
influenced their judgements. Sixth, we could not test empirically the quality of AI-generated
choices. Future research could address this issue by testing AI-designed bGBL units in live
environments and addressing their impact. Seventh, we only tested Chat GPT on the GPT-4 model;
to ensure internal consistency, we ran the same prompt on a different account and obtained
largely overlapping answers (data not shown). However, we are aware that the results obtained
with different models, such as GPT-3.5 or Google Bard, could have led to significantly different
results. GPT-4 being a premium, paid-for tool, its implementation in educational contexts might
be limited. Future research will need to address this issue by focusing on comparative analysis
and prompting techniques.
5. Conclusion
Despite the limitations of an exploratory study, to our knowledge this is the first research
addressing the potential of high-performance AI models in game-based learning design, and the
early returns are, to say the least, very promising. Future research will clarify whether high-
performance AI models can be effectively leveraged to finally allow the diffusion of game-based
learning, overcoming the difficulties that historically prevented the adoption of this extremely
promising, but severely underutilized, learning methodology.
Author contributions
Conceptualization, A.T.; methodology, A.T, F.P. and M. di P.; investigation, A.T.; evaluation, A.T.,
G.V., and F.P.; writing—original draft preparation, A.T., M. di P.; writing—review and editing, A.T.,
M. di P., F.P., G.V., and A.D.; supervision, A.D. All authors have read and agreed to the published
version of the manuscript.
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