As the global reach of online higher education continues to grow, universities are increasingly accommodating students from diverse cultural backgrounds (Tereshko et al., 2024). This can present a number of challenges including linguistic barriers (Ullah et al., 2021), cultural differences in learning style (Omidvar & Tan, 2012), cultural sensitivity in course design (Nguyen, 2022) and perceived isolation when students feel their perspectives or experiences are not reflected or valued in the learning environment (Hansen-Brown et al., 2022). Ensuring active engagement and reasonable learning outcomes in such a environments requires distance educational systems that are not only adaptive but also culturally resonant (Dalle et al., 2024). Both embodied and virtual AI-Agents have great potential in this regard as they can facilitate personalized learning and adapt their interactions and content delivery to align with students' cultural context. In addition, Generative AI (GAI), such as, Large Language Models (LLMs) can amplify the potential for these culturally aware AI agents to address educational challenges due to their advanced capacity for understanding and generating contextually relevant content (Wang et al., 2024). This chapter reviews existing research and suggests the usage of culturally aware AI-Agents, powered by GAI, to foster engagement and improve learning outcomes in culturally diverse online higher education environments.

This study investigates the integration of individual human traits into an empathetically adaptive educational robot tutor system designed to improve student engagement and learning outcomes with corresponding Engagement Vector measurement. While prior research in the field of Human-Robot Interaction (HRI) has examined the integration of the traits, such as emotional intelligence, memory-driven personalization, and non-verbal communication, by themselves, they have thus-far neglected to consider their synchronized integration into a cohesive, operational education framework. To address this gap, we customize a Multi-Modal Large Language Model (LLaMa 3.2 from Meta) deployed with modules for human-like traits (emotion, memory and gestures) into an AI-Agent framework. This constitutes to the robot's intelligent core mimicing the human emotional system, memory architecture and gesture control to allow the robot to behave more empathetically while recognizing and responding appropriately to the student's emotional state. It can also recall the student's past learning record and adapt its style of interaction accordingly. This allows the robot tutor to react to the student in a more sympathetic manner by delivering personalized verbal feedback synchronized with relevant gestures. Our study investigates the extent of this effect through the introduction of Engagement Vector Model which can be a surveyor's pole for judging the quality of HRI experience. Quantitative and qualitative results demonstrate that such an empathetic responsive approach significantly improves student engagement and learning outcomes compared with a baseline humanoid robot without these human-like traits. This indicates that robot tutors with empathetic capabilities can create a more supportive, interactive learning experience that ultimately leads to better outcomes for the student.

For ages, AI has always been portrayed as the antagonist in pop culture and movies, be it the iconic HAL 9000 in 2001: A Space Odyssey, Auto in Wall-E, T-1000 in the Terminator series, or Ultron in Avengers: Age of Ultron. But is this the future of AI that we are really heading towards? Will every AI program become sentient, self-aware, go rogue, and cause massive destruction? The future of AI brings endless possibilities and applications that will help simplify our lives to a great extent. It will help shape the future and destiny of humanity positively. So, how will the future of AI affect humans?

The use of robotic tutors in primary school classrooms is one step closer according to research recently published in the open access journal Frontiers in Computational Neuroscience. Dr Imbernòn Cuadrado and his co-workers at the Department of Artificial Intelligence in Madrid have developed an integrated computational architecture (ARTIE) for use with software applications in schools. "The main goal of our work was to design a system that can detect the emotional state of primary school children interacting with educational software and make pedagogic interventions with a robot tutor that can ultimately improve the learning experience," says Luis Imbernòn Cuadrado. Online educational resources are becoming increasingly common in the classroom, although they have not taken into sufficient account that the learning ability of primary school children is particularly sensitive to their emotional state. This is perhaps where robot tutors can step in to assist teachers.

There has been a large body of research demonstrating that students that receive one-on-one tutoring perform, on average, significantly better than students learning via conventional classroom instruction when tested on the same material (Bloom 1984; VanLehn 2011). During tutoring, the teacher has the ability to tailor the instruction and support to the individual learner, creating a personalized learning environment for each student. Research involving robotic agents Figure 1: Child interacting with a NAO robot in a tutoring as tutors indicates that the physical presence of a robot tutor scenario can increase cognitive learning gains (Leyzberg et al. 2010). Further research shows that a robot tutor employing relatively simple personalization strategies can benefit the that on-demand help is useful in interactive learning environments learner (Leyzberg, Spaulding, and Scassellati 2014).