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 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.

The serious game industry has been a subject of interest for the educational community in the last few years. However, despite their potential benefits when compared to traditional e-learning, use of serious games in education has been hampered by difficulties, including that of deploying games in multi-platform settings and leveraging data generated by their players to improve the learning outcomes of those players. This increasing uptake requires to simplify serious games application for both teachers and developers. This paper proposes a standards-based approach to improve serious games deployment and evaluation. Building on existing and upcoming standards will allow games to be seamlessly integrated into the learning management systems that are already present in most higher education institutions. We present initial experiments that put the proposed approach into practice using two specific tools: uAdventure and SIMVA.

This is an Innovative Practice Full Paper. Serious Game learning analytics can provide insight to improve both the learning process and the lifecycle of games. Due to the complexity and diversity of topics involved, from game design to implementation to tracing the user, teaching game learning analytics to engineering students is challenging. We have created a teaching approach built on an integrated set of tools to minimize the boilerplate setup and configuration typically required when building game learning analytics from disparate modules. Our approach relies on the combination of an authoring environment that eases the creation of adventure and location-based games, a standards-based interaction tracker, and a cloud-based analytics framework. In this environment, students can design and implement serious games with associated analytics models from the very beginning, allowing them to experiment from early on with analytics to improve their games' lifecycles. We have piloted this approach in two university courses for two years, where students prototyped serious games and then used analytics to understand how their users interacted with their games. The use of analytics helped our students to reflect on and refine their designs. While our approach can be applied with any tools, our authoring environment and analytics tools are available as open-source projects to facilitate development, prototyping, or testing of games with analytics.

Despite the proven benefits of Serious Games when compared to traditional e-learning, uptake of game-based learning in mainstream education is still very low. Increasing uptake requires making suitable games easier to deploy as an activity type in existing e-learning platforms, without compromising game quality or increase development cost; and leveraging data generated by players to improve their learning outcomes. We briefly discuss available e-learning standards to address these issues and describe our current standards-based approach to both game deployment and evaluation (including learning analytics).

The e-UCM group is working to simplify the processes of both creation and scientific validation of educational adventure games, by making the entire process of game creation, data acquisition, and analysis more transparent and integrated. This paper presents the integration of the uAdventure authoring tool with SIMVA, a tool to simplify validation experiments, thus encompassing the full process of serious games authoring and validation.

Teachers often wish to integrate activities from disparate sources into their courses. For example, gamified activities, mediated through technology, can promote the type of active learning required to develop higher-level engagement by students. However, unless the activities have been designed to facilitate it, integrating their analytics into a single dashboard can require significant development effort. A general solution to such heterogeneous analytics integration can be of great value, by presenting a single view of student actions throughout the different parts of a course. We describe the problems presented when integrating the analytics of three heterogeneous stand-alone activities, in the context of a EU project to improve software engineering teaching. The idea is to increase student engagement via gamification, and explore the design space of possible solutions for providing integrated analytics over the heterogeneous activities. We then describe the design of a proof-of-concept implementation, based on the use of both xAPI trackers and simple CSV files for information exchange, single sign-on, a minimal class management web application, and updates to the analytics platform to allow dynamic changes in the multi-level analysis. The resulting approach can be readily applied to similar heterogeneous scenarios.

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.

Game engines and developer-friendly authoring tools have greatly simplified entertainment game development. However, this does not extend to serious games which, in particular, require involvement of non-developer stakeholders. We present the first evaluation of uAdventure, an easy-to-use game development environment for narrative point-and-click graphic adventure games. uAdventure is a re-implementation of the previously-validated eAdventure environment on top of the Unity game engine. The idea is to get most of the advantages of the Unity professional environment at a fraction of its complexity, and without requiring programming knowledge to use it. uAdventure include educational-oriented affordances, such as assessment and learning analytics; and has been formatively evaluated by heterogeneous users with different degrees of technical knowledge. The results of our evaluation show much simpler story creation for profiles that had no previous knowledge of the engine, and positive feedback from more technical profiles which would use the tool as a prototyping tool for complex projects.

Game learning analytics has a great potential to provide insight and improve the use of games in different educational situations. However, it is necessary to clearly establish what the learner's requirements are and to set realistic expectations about the learning process and outcomes. Application of game learning analytics requires pedagogically informed policies that settle the learning goals and relate them to analysis and visualization; and a supporting infrastructure that provides the mechanism on top of which it is executed. Both concerns can be addressed separated: on the one hand, there is a Learning Analytics Model (LAM) which describes how the analysis is carried out, interpreted as learning, and presented to stakeholders; and on the other hand, an underlying analytics system that concentrates on performance, security, flexibility and generality. An important advantage of this separation is that it allows LAM authors to concentrate on their area of expertise, limiting their exposition to the actual mechanism used underneath. However, LAMs built for a single game fail to account for the frequent case where games and their analytics are aggregated into larger, overarching plots, games or courses. This work describes an extension to an existing game learning analytics system, used in RAGE and BEACONING H2020 projects, which manages multilevel analytics through improvements to both policy and mechanism; and introduces meta-Learning Analytic Models, which characterize learning in hierarchical structures.

Educational games (aka serious games, SG) are powerful educational contents. However, they are costly to develop, and once developed, SGs become dependent on software and hardware combinations that may become obsolete, such as Adobe Flash or Java Applets. Addressing these problems would allow a much greater use of SGs in education. The eAdventure authoring tool, developed by the e-UCM research group, addressed high development costs, and resulted in the creation of multiple SGs in collaboration with different institutions. However, eAdventure's Java Applets have become increasingly difficult to run due to platform obsolescence. To maintain the benefits of the eAdventure platform and user base, we have created new platform called uAdventure: an SG editor built on top of the game engine Unity that allows for the creation of educational adventure games without requiring programming. Since Unity is supported on a majority of platforms (including mobile). By developing SGs with uAdventure, the games become future-proof, as they can be updated and retargeted for new platforms as required. In this sense, uAdventure improves the lifecycle of SGs by reducing both authoring and maintenance costs.