Abstract
This chapter provides a comprehensive overview of a study investigating the integration of artificial intelligence (AI) in higher education. The chapter examines various applications, strategies, and ethical considerations associated with this integration. It explores how AI technologies can automate tasks such as teaching, learning, and grading, enhancing the efficiency and accessibility of education. Additionally, the chapter investigates the potential benefits of AI in delivering personalised learning experiences, catering to diverse learning needs, and enhancing student engagement through intelligent tutoring systems and virtual environments. Furthermore, the chapter addresses ethical concerns related to AI, including issues concerning data privacy and algorithmic bias. It emphasises the importance of aligning AI applications with educational objectives. It highlights the necessity of providing teacher training and continuously improving AI implementation to ensure that these technologies complement, rather than replace, the human aspects of education.
TopIntroduction
The new technologies and the processing capacity of newly created intelligent machines are intrinsically tied to the future of higher education. Artificial Intelligence (AI) has permeated every aspect of our lives, demonstrating the growing significance of technology (Singh & Hiran, 2022). In recent years, there has been a growing number of artificial intelligence-based educational applications developed. Higher education institutions' governance and internal architecture may change significantly due to the new opportunities and difficulties presented by artificial intelligence developments for teaching and learning. It is impossible to overestimate the significance of adaptive learning technologies systems (ALTS) and Artificial Intelligence (AI) in the classroom (Pardamean, Suparyanto, Cenggoro, Sudigyo, & Anugrahana, 2022).
The phrase artificial Intelligence (AI) evokes visions of massively parallel computers with the capacity to sense and adjust to their surroundings via sensors and other components (Górriz et al., 2020; Šumak, Brdnik, & Pušnik, 2021). Supercomputers with these characteristics can interact more with humans since they have human-like cognition and functionality. AI has been employed in movies in several ways, such as smart buildings that can regulate a room's music, temperature, and air quality based on how people feel (Zhang, Wu, & Calautit, 2022). The conventional meaning of artificial Intelligence, formerly limited to supercomputers, is now being used to embed computer systems in increasing educational circumstances. Examples include robots that, by combining AI, computers, and other supporting technology, can assist students in learning from the very beginning of their education, such as early childhood education. According to Timms, regular chores like pronunciation and punctuation are taught to kids by robots that adjust to their skill level. These robots operate in collaboration with teachers to help teach kids these skills. Multiple studies have shown that web-based and online education has changed over time. Gone are the days when students had to download online materials, study them, and turn in assignments to pass. Instead, students now have access to intelligent and adaptive web-based systems that adapt to their behaviour and that of their instructors to improve their overall learning experience. Education uses Artificial Intelligence to help with administration, instruction, and learning. This research aims to examine and comprehend Artificial Intelligence in education by concentrating on these three domains.
Automating Teaching and Learning
Numerous writers have examined the application of AI to Higher Education (HE), mostly emphasising how AI may enhance student learning possibilities and administrative processes (Pedro, Subosa, Rivas, & Valverde, 2019; Zawacki-Richter, Marín, Bond, & Gouverneur, 2019). AI technologies have the potential to guarantee inclusive and egalitarian access to education. AI allows refugees, persons with impairments, and residents of remote areas to access appropriate learning pathways. Using robotics and holograms enables students with special needs to continue their education in times of emergency or crisis, such as the COVID-19 pandemic at the beginning of 2020 (Sousa et al., 2021). AI can potentially improve several areas, including smart content, personalised solutions for individuals with disabilities (such as the deaf or visually impaired), exponential scalability (e.g. through MOOCs), collaborative learning, and intelligent tools for cooperation (Sayers et al., 2021; Sousa et al., 2021). Computer-supported collaborative learning offers one of its most innovative features when learners are physically present in different places.
Key Terms in this Chapter
Educational Applications: The educational application is software that makes virtual instruction possible and easier. It is not only for students but also for candidates, instructors, experts, online learning environments, and everyone looking to enhance their knowledge or abilities.
Personalised Feedback: In times of uncertainty and isolation, teachers might employ the tactic of personalised feedback to foster social connectivity among students.
Educational Transformation: This refers to systemic changes in the prevailing educational model. Maintaining the core of the conventional teaching and learning process, together with its structure and organisation, separate from theories that support changes or renovations of specific model components.
Grading Automation: One tool intended to assist teachers or organisations in doing assessments more rapidly and efficiently is automated grading.
Adaptive Learning Technologies: Refers to technology that, to improve a learner's performance through automated and instructor interventions, dynamically adapts to the kind or degree of course content based on the learner's skills or skill attainment.