Impact of Machine Learning Algorithms in Decision-Making With Serious Games in the Education and Healthcare Sectors

Introduction

Since the increase of digital era, video games attract the audience of all age groups and gaming industries are growing gradually with the involvement of growing gamers. The ratio of the number of kids and adults playing games is also increasing and they are getting attracted towards entertainment and fun. Serious games can be developed for a player to play the game in single-player or multiplayer mode using devices like video games, PCs, PlayStation, laptops, tablets, smart phones, or wearable gadgets. The environment generated can be 2-dimensional or 3-dimensional. In a 3D environment, special sensors, devices, or play stations are required for generating virtual environment and playing games. Unlike 3D, a 2D environment requires basic devices and requirements (Abd-Alrazaq et al., 2022).

In general, all games are addictive in nature because of the design, user interface and level of difficulty. This addictive nature of the game, when used for education and healthcare, results in the best performance for learners and patients. The main goal of the serious game is to involve the people in the required task by turning the disadvantage of the gaming to an advantage. Decision-making is the key feature in the process of making serious games that are carried with machine learning algorithms. Through the decision-making process, the skill level of the user in playing the game is analyzed by collecting and monitoring the activities of the learner. After analyzing the user’s skill level in playing the game, the appropriate action is taken to improve the learner.

To further increase the performance of these games, machine learning algorithms are used. Machine learning algorithms play a vital role in the selection of an accurate level of challenge by analyzing the performance of the learner. Machine learning algorithms are used to produce results similar to the intelligence of the human by understanding the nature of the learner, problem and the environment. Thus, by studying what a learner is capable of, the algorithm can simulate the environment to involve the learner in more such activities.

Designing games for learning and training in the education sector simplifies the process for educators by offering a clear knowledge and clarity of the topic with less effort. By gathering game statistics, it also assists instructors in gaining a comprehensive knowledge of the areas in which the learner lacks abilities. For learners, it facilitates the learning process while playing the game, which is more engaging for grasping an idea than continually memorizing it.

Serious games are employed in the healthcare industry by both professionals and patients. For professionals, it can be used to examine a patient's health improvement or for demonstrating reasons to learn the effect of medications. It can also be used as a treatment or to educate patients about the impacts of sickness and how to prevent it. For professional training Serious games can be used to lower the cost associated with a critical, risky, or unexpected situation that is difficult to perform in reality. Professional training in such instances may be significantly more successful and cost effective if performed in Serious Games (SG) using a virtual reality environment.

With the unexpected epidemic, the education and healthcare sectors are presently at their best, necessitating more time and energy from experts in both areas. It is beneficial and effective to invest in resources such as serious games, which may help professionals focus on their core task. With the growth in online activities, it might prove beneficial to invest in learning and training activities in an entertaining environment. Because integrating serious games for students and patients decreases the amount of time spent on tasks such as evaluation, feedback, and acquiring learning data (De Gloria et al., 2014).

The rest of the chapter is structured as Literature review, that provides the background knowledge and origin of the serious games, Research Methodology, that describes different machine learning algorithms used in designing different serious games developed in education and healthcare sectors, results and discussions, that compares different ML algorithm with its own merits and demerits along with how ML algorithms impacts in designing the serious games, and conclusion suggest the future developments in serious games.