This Collection is intended to examine the use of emerging technologies (data science and artificial intelligence (AI)) to improve teaching and learning in higher education. Additionally, this call is a response to the application of emerging technologies, AI and data science influence on pedagogy and curriculum development as well as features to consider in creating environments for learning. Attention is also given to data-based AI processing paths, dangers, and challenges in teaching programming to non-science disciplines. The call also considers models/theories for teaching and learning in data-based AI and practical examples on the use of predictive/algorithmic-based modelling. Evidence from previous studies highlights the ongoing impact of AI on teaching and learning in higher education (Popenici & Kerr, 2017). Nevertheless, it is also evident that while we hype teaching and learning in data-based AI, some categories of knowledge acquisition regarding for instance models for teaching and learning ought to be human-centric. This is because, it is equally evident that research does not converge adequately on what it means to have a of students’ cognition (Tuomi, 2018). For instance, in terms of curriculum delivery and dependent on the complexity, it is quite evident that adaptive learning platforms are more suitable for machine learning or data-based AI regarding wide variety of educational usages. In close link with pedagogical related activities, it will mean we need far more depth in range regarding stimulus such as inputs with reference to multi-touch screens and virtual reality (VR) in university learning processes. In effect, in terms of assessment of learners for instance, and equally dependent on complexity, computer-based assessment for earning (CBAfL) coupled with personalised feedback need to be developed. That is to say, teaching and learning in data-based AI models ought to be focused on present levels of cognition. This should also account for the type of questions designed to articulate right levels of difficulty index, while granting the most information related to the students’ present state of development together with progression rate (Popenici & Kerr, 2017). However, implied challenges to overcome may include barriers to adoption. Even though several barriers were noted, notably the implied challenges rested upon the lack of or the need to understand educational data and the associated fits. There is also need to understand learners and associated privacy issues together with ethical related concerns of students’ data. Such implications pave the way for future work in many forms. One such form of future work is the need-to-know what results in appropriate knowledge in human-centric approach, coupled with the form of acquisition, creation, and learning. It would also mean there is the need to examine further procedure in regulating cognitive processes. This would mean attention and emotion involved in learning processes as well as both the social and practical implication for learning, while using data-based AI, given that they sometimes are unrealistic. Meaning, there is the need for data-based AI capabilities, which should be far more and closely linked with or resemble human intelligence as opposed to simplified models of learning biological intelligence. Thus, it is important to rethink AI and data science processing dominate model of reflexological, and behaviouristic models of learning inspired by both Pavlov and Thorndike. Additionally, massification as well as wider participation in higher education coupled with students’ numbers increasing, class sizes, staff cost make data-based AI an attractive possibility which has become far more evident in the midst of Massive Open Online Courses (MOOCs). Similarly, the drive for personalised learning resulting from teacherbot, or cloud-lecturer, used for blended delivery courses or fully online courses are beginning to be computing solutions targeting administrative aspect of teaching, content delivery, administrative feedback and supervision are the alternative to traditional teaching assistants.

Despite the recognition of the abundance of use cases of data-based AI and the effects of computing algorithms influence on mundane activities of education, strictly speaking, education remains human centric endeavour. As such, educational-technological reforms should rather open new possibilities for higher education’s teaching and learning, which should simultaneously highlight the urgent need to be cognisance that data-based AI does not replace teachers. Instead, there is an opportunity for augmentation via such tools as VR and augmented reality (AR).

In essence, as opposed to replacing teachers, data-based AI in higher education ought to rather extend human abilities in terms of teaching, learning and research together with administrative and leadership roles. Consequently, the following form the subthemes of the Collection:

In essence to this Collection, the contribution of the Collection lies in examining the following aims:

• AI and data science research influence on pedagogy and curriculum development and simultaneous shift from one to another

• The emerging features which have gained prominence in AI and data science research community of practice in creating environments for learning computation and programming

• AI, data science and big data processing paths, dangers, and challenges in teaching programming to non-science disciplines

• Models for teaching and learning in data-based AI and practical examples on the use of predictive/algorithmic-based modelling

• AI/Computer-Based Assessment for Learning in the Digital Age

• Utilize various digital technologies to support and enhance education

• Implement tools such as Sketchpad, GIS, SmartPLS, MATLAB, Geogebra, Blockchain, JAWS, VR, AR, MR, IoT, and Robotics to create innovative learning experiences

• Develop AI-based diverse assessment types to evaluate different learning outcomes

• Emerging technology based - design formative, summative, diagnostic, and dynamic assessments that align with learning objectives and outcomes. through human computer interaction

• Development of innovative AI-driven adaptive learning platforms and assessment tools

• Enhanced learning experiences through immersive metaverse-powered STEM education

• Create and utilize VLEs to support Open Educational Practices (OEPs) and Open Educational Resources (OERs)

• Develop VLEs that facilitate collaboration, resource sharing, and open access to educational materials

• Utilize AI to support various aspects of digital education, including content delivery, assessment, and feedback

• Implement AI tools for automated grading, personalized feedback, and intelligent tutoring systems

• Enhance accessibility and flexibility of learning through mobile computing

• Develop mobile applications and platforms that support e-learning and m-learning, enabling students to learn anytime, anywhere

• Leverage the metaverse to create immersive and interactive learning experiences

• Design virtual labs and simulations within the metaverse, allowing students to experiment and learn in a controlled, risk-free environment

By examining the aforesaid topics, we could understand both the nature and scope and provide spur for the initiation of further discussions on teaching and learning in data-based AI.

Keywords: Educational Digital Technologies; Artificial Intelligence (AI); Data Science; Curriculum Development; Human-Centric Learning

The emerging technologies such as Artificial Intelligence (AI), 5G and 6G cellular communications, Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) BCIs and biofeedback, robotics, drones, games, etc., as well as Metacognition, Mindfulness, Executive Functions and Emotional intelligence, on one hand, play an important role in the transformation of the school for its future model operation, and on the other, they are the tools for the acceleration and improvement of the society, the economy, the equality for all, the inclusive growth, the personal development, the brain rewiring and self-improvement.

We welcome researches, studies, and literature reviews, of exploitation of the emerging technologies in all domains of education, for assessment, intervention, improvement, acceleration and fostering all the aspects of education and knowledge acquisition.

Keywords: Education; STEAM; Artificial Intelligence (AI); 5G and 6G cellular communications; Virtual Reality (VR); Augmented Reality (AR) and Mixed Reality (MR); BCIs and biofeedback; robotics; drones; games; Metacognition; Mindfulness; Executive Functions; Emotional intelligence