## 1 Introduction  
Research into the use of artificial intelligence in education is rapidly increasing as there are several benefits that can be yielded through its integration in teaching and learning practices [1]. Nonetheless, there are challenges and issues that need to be addressed,such as ethical considerations, security and privacy issues, algorithmic bias concerns, technical limitations, etc. [2; 3]. Therefore, emphasis is being put on addressing these issues and on effectively integrating artificial intelligence tools and solutions to provide students with more adaptive and personalized learning experiences which can lead to increased learning outcomes [4; 5]. Adaptive learning focuses on automatically providing interactive and dynamic content, feedback, and experiences that are modified according to the knowledge, preferences, and skills of learners [6; 7; 8]. Personalized learning puts emphasis on customizing the learning experience based on each learner's preferences and needs to aid them in achieving their learning goals [9; 10]. To tailor instructional strategies, activities, and material to each learner, focus is placed on the development and integration of intelligent tutoring systems [11; 12]. Intelligent tutoring systems can identify, track, and monitor learners' cognitive, behavioral, and psychological states [13; 14]. Additionally, intelligent tutoring systems aid students in acquiring domain-specific, cognitive, and metacognitive knowledge through their complex computational models [15; 16] and offer student-centered, self-paced, interactive, and customizable learning experiences [17]. The computational models used within intelligent tutoring systems include various knowledge types, models, and components and focus on learners' cognitive, emotional, and motivational states to offer effective technology-enhanced learning environments [12; 18; 19]. Intelligent tutoring systems aim to offer learners learning experiences that surpass those offered by a sophisticated human tutor by adapting to the unique characteristics and traits of each learner [20; 21]. In well-defined domains, intelligent tutoring systems can increase students' learning outcomes [22] and perform almost as effectively as human tutors [23] while they are still being outperformed by human tutors in ill-defined domains [24]. As a result, their integration in educational settings in both K-12 education and higher education can yield positive learning outcomes whether they are used as the primary or supplementary method of instructional delivery [15; 16; 18; 25]. Yet, there still remain several open challenges related to the development and integration of intelligent tutoring systems that need to be examined and addressed [19]. In addition to more personalized learning experiences, students are also seeking to be engaged in and learn within more immersive and interactive learning environments [26; 27]. Extended reality technologies are being more widely adopted and used in educational settings to offer learners such environments and experiences [28]. Due to the benefits that they can bring in teaching and learning, research into the use of augmented reality [29] and virtual reality [30] as well as the integration of artificial intelligence in extended reality [31] in education is increasing. Specifically, in the context of education, augmented reality focuses on embedding virtual information that learners can perceive through their senses in their physical environment; thus, enriching the way in which they perceive, interact with, and communicate with their surroundings and digital content [32; 33; 34; 35]. Furthermore, virtual reality focuses on fully immersing learners in virtual reality environments that are characterized by high levels of realism, interaction, and immersion and simulating learners' physical presence within them [37; 38; 39].