Cada vez es más común interactuar con productos que parecen “inteligentes”, aunque quizás la etiqueta “inteligencia artificial” haya sido sustituida por otros eufemismos. Desde noviembre de 2022, con la aparición de la herramienta ChatGPT, ha habido un aumento exponencial en el uso de la inteligencia artificial en todos los ámbitos. Aunque ChatGPT es solo una de las muchas tecnologías generativas de inteligencia artificial, su impacto en los procesos de enseñanza y aprendizaje ha sido notable. Este artículo reflexiona sobre las ventajas, inconvenientes, potencialidades, límites y retos de las tecnologías generativas de inteligencia artificial en educación, con el objetivo de evitar los sesgos propios de las posiciones extremistas. Para ello, se ha llevado a cabo una revisión sistemática tanto de las herramientas como de la producción científica que ha surgido en los seis primeros meses desde la aparición de ChatGPT. La inteligencia artificial generativa es extremadamente potente y mejora a un ritmo acelerado, pero se basa en lenguajes de modelo de gran tamaño con una base probabilística, lo que significa que no tienen capacidad de razonamiento ni de comprensión y, por tanto, son susceptibles de contener fallos que necesitan ser contrastados. Por otro lado, muchos de los problemas asociados con estas tecnologías en contextos educativos ya existían antes de su aparición, pero ahora, debido a su potencia, no podemos ignorarlos solo queda asumir cuál será nuestra velocidad de respuesta para analizar e incorporar estas herramientas a nuestra práctica docente.

Although several definitions of gamification can be found in the literature, they all have in common certain aspects: the application of strategies, models, dynamics, mechanics and elements of the games in other contexts than games, and the objective of producing a playful experience that fosters motivation, involvement and fun. In this paper, our approach gamifying the learning process of a subject is presented. Our experience throughout time in using games and gamification in learning have led us to propose, lately, a personalized, automated and gamified learning system. As a result of this experience and after several years of continuous feedback from our students, we have learned several lessons on how to approach the task of gamification. These lessons are summarized in the following concepts: fun, motivation, autonomy, progressiveness, feedback, error tolerance, experimentation, creativity and adaptation to the specific case. The final aim is sharing our experience and opening a debate about what key elements the gamification lies in.

Many researchers consider Gamification as a powerful way to improve education. Many studies show improvements with respect to traditional methodologies. Several educational strategies have also been combined with Gamification with interesting results. Interest is growing and evidence suggest Gamification has a promising future. However, there is a barrier preventing many researchers from properly understanding Gamification principles. Gamification focuses of engaging trainees in learning with same intensity that games engage players on playing. But only some very well designed games achieve this level of engagement. Designing truly entertaining games is a difficult task with a great artistic component. Although some studies have tried to clarify how Game Design produces fun, there is no scientific consensus. Well established knowledge on Game Design resides in sets of rules of thumb and good practices, based on empirical experience. Game industry professionals acquire this experience through practice. Most educators and researchers often overlook the need for such experience to successfully design Gamification. And so, many research papers focus on single game-elements like points, present non-gaming activities like questionnaires, design non-engaging activities or fail to comprehend the underlying principles on why their designs do not yield expected results. This work presents a rubric for educators and researchers to start working in Gamification without previous experience in Game Design. This rubric decomposes the continuous space of Game Design into a set of ten discrete characteristics. It is aimed at diminishing the entry barrier and helping to acquire initial experience with Game Design fundamentals. The main proposed uses are twofold: to analyse existing games or gamified activities gaining a better understanding of their strengths and weaknesses and to help in the design or improvement of activities. Focus is on Game Design characteristics rather than game elements, similarly to professional game designers. The goal is to help gaining experience towards designing successful Gamification environments. Presented rubric is based on our previous design experience, compared and contrasted with literature, and empirically tested with some example games and gamified activities.

The design and development of Serious Games is a complex task, including a considerable risk of failure. Many attempts end up in non-fun, non-engaging games that fail to meet the purpose of improving education. Many different proposals have been published in the form of design frameworks, with the aim of helping practitioners succeed. Although these frameworks define and explain relevant concepts and guidelines, there is lack of focus in iterative methodologies. These methodologies have proven valuable in other areas on engineering and are also used by commercial game designers. This work proposes the introduction of iterative design for Serious Games and presents an early stage methodology, along with an example of the core mechanic of a game and a prototype for learning the concept of slope of a line.