The growth of video games as a popular form of entertainment has generated significant interest in their potential as powerful educational tools. Serious games ^[1][2], a term used to describe video games with a “serious purpose”, have emerged as a means of converting video games from simple forms of entertainment into purposeful learning experiences. The integration of video games into educational practices has been extensively studied and has been shown to be effective in enhancing learning outcomes. For example, research has demonstrated that video games can foster transformational play and support science education ^[3]. Additionally, a meta-analysis by Zhonggen ^[4] showed that the use of serious games has a positive impact on learning outcomes.

Serious games are considered to be a promising means of familiarizing people with various fields of computer science, which is heavily used in everyday life. A particularly vital and constantly advancing field is Artificial Intelligence (AI). As more people and organizations adopt AI, it turns out that learning its concepts is considered quite complex and is accompanied with various difficulties. Serious games might be a promising means for dealing with these difficulties. However, although there are several games for cultivating algorithmic or computational thinking skills ^[5] and programming ^{[6][7][8][9][10]} in various contexts, there are very few games for learning AI algorithms, and this provided the motivation for the research presented in thereis paper.

Designing an effective game for learning AI algorithms, or generally a serious/educational game, is not considered an easy task. As Van Eck states, “games are not effective because of what they are” ^[11] (p. 18) but instead because of: the material they incorporate; players’ actions while playing; the right level of difficulty; continuous interaction of the player with the game and appropriate feedback; and a logical balance between the time and effort needed for implementing the game and the learning outcomes achieved by playing it. Several researchers on the other hand conclude that serious games do not achieve their full potential neither in learning nor in entertainment ^[12][13], while achieving a balance between education and fun is rather challenging ^[14]. As a matter of fact, Natucci and Borges consider the balance between pedagogy, emotions and game elements as one of the greatest challenges in serious game design ^[15]. Brisson et al. ^[16] highlight the importance of AI and personalization toward achieving such a balance.

2. Difficulties in Learning AI Algorithms

Mastering AI algorithms can be a formidable challenge for numerous students. This results from the intricate nature of AI and its algorithms coupled with the scarcity of learning materials to simplify these concepts. The hurdles that come with comprehending AI algorithms can be grouped into multiple domains, including mathematical foundations, conceptualization, and visualization. Mathematical foundations. To comprehend how AI algorithms work, it is necessary for learners to have a deep understanding of complex mathematical concepts such as probability and statistics ^[17]. Nevertheless, according to Alpaydin, many students struggle to grasp these concepts, which can pose challenges in successfully applying AI algorithms to real-world scenarios. Conceptualization. The complexity and abstraction of AI algorithms make them challenging to decipher for learners, ascertaining their inability to perceive the interdependencies between diverse algorithms and the concepts they represent. In accordance with a study conducted by Shute et al. ^[18], learners grapple with the conceptualization of intricate algorithms, often hindering their proficiency in understanding and applying these algorithms. Visualization. Understanding complex AI algorithms, such as deep neural networks, can be challenging for learners due to their intricate and interconnected nature ^[19]. Visualization plays a critical role in comprehending these algorithms, according to Goodfellow and his colleagues, as it helps students understand the underlying principles and relationships between different algorithms. Lack of visualization skills can, therefore, hinder a learner’s ability to understand and apply these algorithms in real-world scenarios.

3. Methods of Teaching AI Algorithms

To overcome the challenges of learning AI algorithms, several methods have been proposed to help learners understand these concepts. Some of these methods include visualizations, hands-on experience with AI tools and techniques, and serious games. Visualizations. Visual aids are a useful approach for enhancing the learners’ understanding of complex AI algorithms ^[20]. Lauscher recommends the use of visualizations to clarify the mathematical foundations of these algorithms and facilitate learners’ comprehension of the intricate processes involved. Specifically, visualizations can be helpful in explaining the structure and function of deep neural networks, which are among the most challenging types of AI algorithms to comprehend visually. Hands-on experience. Furnishing learners with practical exposure to AI tools and techniques constitutes another effective strategy for augmenting their comprehension of AI algorithms. Shute et al. ^[18] recommended the provision of hands-on experience with AI algorithms as a means of cultivating a profound understanding of these algorithms, which can prove beneficial in real-world situations. This approach can facilitate learners’ acuity in deciphering how AI algorithms operate in practice.

4. The Role of Serious Games in Teaching Algorithms

The utilization of video games as an instructional tool has garnered considerable attention in scholarly research. In particular, the efficacy of serious games in enhancing learning outcomes has been demonstrated by various studies ^[4]. Serious games offer an immersive and interactive modality of learning intricate concepts that engenders heightened engagement and motivation among learners. However, some players and educators view the use of serious games at a formal context with skepticism ^[21]. The most important concerns refer to the fact that serious games are far less engaging in comparison with commercial entertainment games and the fact that gaming is a “voluntary and self-driven activity” is somehow incompatible with the structured and formal context of a school. As noted in the literature, although the potential of serious games is promising, more rigorous research is necessary for investigating their actual effectiveness in learning ^[22]. Serious games are used as supplementary tools for enhancing traditional educational approaches, but they can play an even more important role in education if supported with appropriate resources, theoretical frameworks and clear goals ^[22]. Serious games have been employed to instruct algorithms in diverse fields, including mathematics and computer science. For example, a serious game was created to teach AVL trees to computer science students, as demonstrated in the work of Wassila and Tahar ^[23]. The game’s design aimed to furnish a comprehensive introduction to AVL trees and to offer learners practical exposure to them. By leveraging interactive and stimulating gameplay mechanics, the game sought to facilitate learners’ comprehension of the subject matter and foster their proficiency in applying graph algorithms. Another example is a serious game developed by Hainey et al. ^[24] which proved to achieve quite good learning results. The game functions as a puzzle-based platformer that requires players to utilize rudimentary programming constructs to advance through each level. It comes with a variety of learning materials, such as tutorials and assessments, which aid students in acquiring programming knowledge and its real-world applications in a game-based environment. Generally, serious games have been utilized for: cultivating algorithmic and computational thinking skills to primary school students ^[5]; introducing secondary school students to programming using various programming languages, such as Python ^[6] and Java ^[7]; and teaching programming to higher education students ^[8]. One of the conclusions drawn in a recent review of educational games for primary school students is the need for carrying out empirical studies for investigating the actual impact of such games on learning programming ^[5]. Moreover, it is highlighted that the implementation of specific features can make the teaching of programming in formal contexts more effective ^[5] and consequently help in dealing with the skepticism of stakeholders ^[21]. The features proposed include ^[5]: learning analytics; support for collaboration between players; enhanced player–game interaction; support for creating customized lesson plans. Serious games have been used with positive affective and cognitive results in the education of programming in various contexts, such as: programming assignments ^[9]; lab sessions ^[8]; and self-learning ^[10]. CMX, a massive multiplayer online role-playing game for programming in C, was used in the labs of an introductory programming course of an Informatics department for five weeks ^[8]. The 76 participants evaluated positively their experience and motivation to continue, while the majority of them performed better in the course in comparison with the students that were taught in the labs with the traditional approach (solving programming exercises in a typical integrated development environment for C). SQL Island, a web-based text adventure game for learning the Structured-Query Language (SQL), was utilized in the context of a programming assignment for an undergraduate Web Programming course ^[9]. Fifty-six students submitted the assignment based on SQL Island and evaluated the game using the MEEGA+ model. Students evaluated positively the player experience and perceived short-term learning, while their performance in the assignment was very good. The authors concluded that a game that is used in the context of programming assignments can have better results if the following features are supported ^[9]: good examples of new programming constructs; tasks connected to the story of the game; ability to register and save progress; usage of learning analytics for automatic assessment and reporting of class statistics. The small number of games for AI and the rather limited research investigating if and how serious games can support students in comprehending AI concepts/algorithms motivated the authors in designing SpAI War. In the paragraphs that follow, the aforementioned AI games are briefly described, while a comparative analysis of the aforementioned games and SpAI War is presented in Table 1. Obsolescence ^[25] is a digital board game designed to educate military decision-makers on dealing with the threats imposed by disruptive AI technologies. In the context of the game, the players participate in competing militaries that build and move forces and interact with AI-based technology cards in order to gain influence points. Obsolescence was evaluated by 48 participants recruited from the United States Air Force and Department of Defense. The evaluation utilized a pre–post survey with questions related to the five learning objectives (LOs) of the game and aimed at evaluating short-term learning. The study showed that the game achieved its LOs, while there was a strong correlation between actual learning and perceived learning. Maestro is an open-source game-based platform under development (https://maestro-ai.github.io/) (accessed on 1 May 2023) that aims to introduce higher education students to robust AI, which refers to countermeasures and the prevention of AI vulnerabilities ^[26]. Maestro utilizes goal-based scenarios and a competitive programming environment. In this environment, students are assigned the roles of the attacker or the defender and work either alone or in teams for devising their attack or defense according to their role. The solutions provided are evaluated for their quality, and students are ranked in a leaderboard. Maestro was positively evaluated by undergraduate students in two offerings of an AI course though a survey. Object-oriented Sokoban solver ^[27] is a game project that can be utilized for teaching both object-oriented analysis and design and AI. When it comes to AI, the Sokoban solver can be used as a case study for: implementing and experimenting on different path-finding algorithms in order to move one or more boxes on a grid with or without obstacles; teaching heuristics in the context of estimating the distance between the player and the goal state. Although this is an interesting approach, no application and/or evaluation results are available. Based on the comparative analysis of serious games on AI, it turns out that the available games have different goals: Obsolescence aims at educating military decision-makers on dealing with the threats imposed by disruptive AI technologies; Maestro aims at educating future AI workforce on dealing with AI vulnerabilities; and Sokoban solver aims at supporting students experiment with AI algorithms in the context of a Sokoban puzzle game by diving into its code. The proposed game SpAI War aims at introducing anyone interested in AI to nine fundamental AI algorithms and machine learning through the application of the algorithms for advancing in a 3D first-person shooter game.