There is a need to increase the number of students, especially women, choosing programming and STEM disciplines. We need innovative approaches in schools to better engage students and awake their interest in computer science. This paper addresses the need to create tools that effectively support the learning of programming and the development of computational thinking, highlighting why video games can be an effective educational tool for it and also attract new students to STEM. The Game4Coding Erasmus+ project proposes the design of a video game called CodeQuest, using a game genre that has not been frequently used before to address the teaching of programming, the monster tamer genre. We consider that video games have a number of benefits such as that stimulate active learning, are engaging for a wide range of students, and present information in a way that is attractive to learners. We want to explore this kind of game's effectiveness as a learning tool as well as its effect on the perception of STEM disciplines and programming to attract new public to coding (especially girls).

How do students use artificial intelligence tools in coursework projects when given the liberty to do so, with the only requirement of documenting how, where and why? We describe experiences with two groups of undergraduates in courses related to serious game authoring and human-computer interaction, both carried out in the second semester of 2023. In the serious games course, students were given the option of following a teacher-developed methodology for generating graphical assets for their serious games using a set of generative AI tools. This methodology was explained in the class but not hands on lab was carried out. In the interaction course, students were free to choose which AI tools to use when designing their system or in the development of their project documentation. Despite the limited number of participants (41 in total) we can see very different views and degrees of involvement: while some tried to use AI for as many tasks as possible, others considered that the learning curve for those tools was too steep to be worthwhile. Both experiences included a free-text survey at the end, and taken together, provide insights into how both supervised and unsupervised generative AI use could impact undergraduate projects in similar subjects. In addition to describing how students chose to use the tools, and the main takeaways from their survey response, we also discuss some of the ethical aspects about the access to the tools and what should be the minimal conditions to be met to allow the equitable use of AI in the classroom.

Serious games have several advantages for learning over more traditional activities. One of those advantages is that being highly interactive, they provide a firsthand simulated experience to the users. Serious games can also generate interaction data that can be used to assess learning, both for evaluation and to provide timely feedback for learners or even adapt aspects of the game on the fly. However, this potential is currently far from realized because doing so requires significant investment and expertise as compared to more traditional educational activities. Additionally, game learning analytics continue to be a costly and fragile process. We propose a combination of tools that greatly reduces the associated costs of building serious games with meaningful analytics simplifying the validation of those games and their deployment in real settings. We consider that this can be a key step to building predictive assessment avoiding the need for disruptive external assessment thus bootstrapping serious games to recognize learning through analytics.

Serious games are videogames whose purpose goes beyond mere entertainment. However, serious games use in mainstream education is still limited. The development of serious games is an expensive and complex process that requires the participation of different experts (e.g. domain, educators, graphic artists, and programmers). We consider that new generative AI techniques can help in the prototyping of serious games by reducing and automating some of the processes involved. There is increasing evidence that generative AI techniques such as ChatGPT or GitHub Copilot can increase the productivity of writing or coding tasks respectively. In our case, both prototyping and teaching serious games are complex because of the number and diversity of tasks involved and we are currently investigating whether AI techniques can be used to improve and simplify the process. For example, ChatGPT could support the process of creating the game narrative, and other systems such as Stable Diffusion could ease the creation of some of the graphics resources (e.g., for creating more cohesive and coherent backgrounds). Automating some of the costlier processes of game prototyping can contribute to creating better products by allowing the playtesting of different options for more effective games. As the field of generative AI is in continuous change, this paper presents a working methodology to simplify the development of serious games, that has been instantiated with concrete tools. This working methodology has been piloted effectively by one student from a Master of Design for the development of a serious game and will be tested in a serious games development course. In the article we also explore how these AI techniques can be combined with a game authoring environment such as uAdventure to systematize the development of serious games. The use of Generative AI offers great potential for improving the development of serious games and needs to be further researched alongside its applications for game-based learning education.

Data science applications in education are quickly proliferating, partially due to the use of LMSs and MOOCs. However, the application of data science techniques in the validation and deployment of serious games is still scarce. Among other reasons, obtaining and communicating useful information from the varied interaction data captured from serious games requires specific data analysis and visualization techniques that are out of reach of most non-experts. To mitigate this lack of application of data science techniques in the field of serious games, we present T-Mon, a monitor of traces for the xAPI-SG standard. T-Mon offers a default set of analysis and visualizations for serious game interaction data that follows this standard, with no other configuration required. The information reported by T-Mon provides an overview of the game interaction data collected, bringing analysis and visualizations closer to non-experts and simplifying the application of serious games.

Serious games have proven several advantages when used in education improving students learning. However, games are still complex to deploy in the class for average teachers. Many teachers still do not see games as a powerful tool to improve their teaching work. To this end, it is essential to humanize the game technology making the use of games more transparent to teachers in a way that they get the benefits and avoid most of the game deployment complexity. We consider that Game Learning Analytics is one of the keys to help teachers in the application of serious games in the classrooms. Game Learning Analytics allows to capture data from students' interactions with games and derive information that simplify teachers' tasks. Doing it in a transparent way within the game environment (i.e. stealth assessment) can provide evidence-based data about the learners' knowledge at each point of time. Combining both game learning analytics in near real-time and offline, and stealth assessment for games, it could be possible to leverage their use in classroom settings at real-time making their use easier for teachers.

Serious games validation is a highly complex and burdensome process. To ensure that games meet their intended educational goals, it is necessary to have a clear experimental design and the necessary tools to minimize the errors that may appear in the process. In this article, after describing the most common problems that we have found while validating our own games, we present Simva, a tool designed to simplify the process of validating serious games with formal questionnaires and relating them with learning analytics data, reducing time, cost, and error rates.

Applying games in education provides multiple benefits clearly visible in entertainment games: their engaging, goal-oriented nature encourages students to improve while they play. Educational games, also known as Serious Games (SGs) are video games designed with a main purpose other than pure entertainment; their main purpose may be to teach, to change an attitude or behavior, or to create awareness of a certain issue. As educators and game developers, the validity and effectiveness of these games towards their defined educational purposes needs to be both measurable and measured. Fortunately, the highly interactive nature of games makes the application of Learning Analytics (LA) perfect to capture students' interaction data with the purpose of better understanding or improving the learning process. However, there is a lack of widely adopted standards to communicate information between games and their tracking modules. Game Learning Analytics (GLA) combines the educational goals of LA with technologies that are commonplace in Game Analytics (GA), and also suffers from a lack of standards adoption that would facilitate its use across different SGs. In this paper, we describe two key steps towards the systematization of GLA: 1), the use of a newly-proposed standard tracking model to exchange information between the SG and the analytics platform, allowing reusable tracker components to be developed for each game engine or development platform; and 2), the use of standardized analysis and visualization assets to provide general but useful information for any SG that sends its data in the aforementioned format. These analysis and visualizations can be further customized and adapted for particular games when needed. We examine the use of this complete standard model in the GLA system currently under development for use in two EU H2020 SG projects.

Serious Games can benefit from the commercial video games industry by taking advantage of current development tools. However, the economics and requirements of serious games and commercial games are very different. In this paper, we describe the factors that impact the total cost of ownership of serious games used in educational settings, review the specific requirements of games used as learning material, and analyze the different development tools available in the industry highlighting their advantages and disadvantages that must be taken into account when using them to develop a serious game.