Abstract
The role of AI in education is not doubtful. This chapter critically explores the potential benefits, challenges, and implication of integrating AI game activities in educational settings. It underlines how Al algorithms are vital in promoting critical thinking, problem solving skills, and knowledge retention among learners. Moreover, this chapter also discusses the utilization of simulation, virtual reality, and interactive narrative in Al- driven gaming activities to amplify active engagement and foster a deeper understanding of complex concepts. Additionally, the chapter explores how Al games have the capacity to enhance collaborative leaning, foster social interaction, and facilitate the growth of essential skills needed in the of 21st- century. The chapter addresses the crucial topics of ethics concerns, safeguarding data privacy, and promoting inclusivity. Furthermore, it underscores the potential of AI game activities in addressing the evolving requirements of learners in the digital age and equipping them for a technology-driven future.
TopPersonalized Learning Experience
The field of AI has been rapidly evolving, permeating various industries, applied sciences, medicine and financial sectors. On the contrary, in the education field, learning institutions have not embraced fully the value of technology (Luckin & Cukurova, 2019). Nonetheless, several studies (Mahona & Pacho, 2021; Makira & Owino, 2021; Owic et al. 2021; Pacho, & Adewoyin, 2022; Paschal & Mkulu, 2020; Paschal; Rahayu et al. 2022) confirm that COVID 19 brought the realization that AI adaptation in the education sector was inevitable. As such it is imperative for learning institutions to adapt and leverage the potential of AI to enhance teaching methods and optimize personalized learning process for learners (Tapalova & Zhiyenbayeva, 2022). Innovations in AI technologies have opened new avenues for transforming traditional educational practices especially on how knowledge is delivered thereby fostering a positive personalized learning experience.
AI advancement has raised awareness of various solutions that offer alternatives for learners and foster the adoption of novel teaching and learning methods (Paschal & Mahama, 2023). Further, the progress in AI has significantly contributed to the development of personalized learning pathways in education (Tapalova & Zhiyenbayeva, 2022). Learning pathways assist in comprehending and interpreting the learner’s action as they engage in the process of learning. This is due to distinct knowledge structures based upon their previous experiences and capacities. Moreover, learning paths also unveil the routes of learning as learners navigate through interactive environment Hoatching as cited by (Elshani, & Nuçi, 2021). Such learning trajectory helps learners to acquire knowledge incrementally and empowers them tto take active role of navigating their course of learning.
Learning pathways can also be tailored to facilitate autonomous and self-paced learning encounters. However, it is essential to note that merely personalizing learning experiences does not guarantee the desired learning outcomes (Tetzlaff, et al. 2021). These authors explain that ineffective personalization attempts may arise when adapting to characteristics that are not closely tied to the learning process, such learning styles or relying on static modelling for adaptation. To achieve successful personalized learning, it is crucial to utilize dynamic modelling to assess and adapt to relevant learners’ characteristics that will result in positive personalized learning achievement (Tetzlaff, et al. 2021).
Key Terms in this Chapter
Learning Pathways: refers to the implementing or putting into practice the concept of structured routed that learners follow to achieve specific personalized learning gaols or outcomes.
Collaborative Learning: entails the practical realization and application of an educational approach where learners engage in shared activities, discussions, and group tasks to collectively enhance their understanding, skills, and knowledge.
AI Game Active Learning: encompasses the practical implementation and execution of educational activities that integrate artificial intelligence and gaming elements, encouraging hands-on participation problem- solving and dynamic engagement to facilitate effective personalized learning experiences for learners.
AI Technology: Artificial intelligence is the catalyst for transformation that focuses on meeting the unique personalized learning requirement and demand of the learner
Personalized Learning: Refers to a wide variety of educational programs, academic strategies, learning experiences, and systematic pedagogical approaches designed to address learners ’s specific learning needs, aspirations, and interests on an individual basis.
Simulation-Based Learning: refers to the practical implementation and execution of a teaching approach that utilizes simulations to provide learners with environment closely resembling dream-world situations. This enables them to actively build and refine their knowledge and skills within a controlled and simulated context.
AI-Driven Gaming Activities: Pertains to the practical application and execution of gaming experiences that are guided and enhanced by artificial intelligence algorithms enabling dynamic interactions, personalized challenged and adaptive gameplay based on players’ actions and preference.
Intrinsic Motivation: i s the extent to which an individual voluntarily engages in an activity sorely for the internal pleasure, satisfaction, or inherent interest derived from the activity itself, without external rewards or pressures influencing their participation. It is measured by the individual’s willingness to persist in the activity even when external incentives are absent, the expression of enthusiasm and curiosity during the activity, and sense of personal accomplishment experienced upon completion.
Social Interactions: is defined as observable and measurable behaviours involving communication, engagement, or exchange of information between two or more individuals within a given context. This interaction encompass verbal and non-verbal communication, such as conversations, gestures, expressions, and shared activities. They are assessed based on the frequency, duration, and quality of the observed behaviours, with particular attention to the mutual influence and responses between participants.
AI Algorithms: involves the concrete application and execution of computation procedures designed to enable artificial intelligence systems to process data, learn from it, and make informed decisions or predictions.