Bibliographical Analysis of Artificial Intelligence Learning in Higher Education

Abstract

This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of disciplines involved in AI education literature and that quantitative methods were the most frequently used in empirical studies. Four areas of AI education applications in academic support services and institutional and administrative services were revealed, including profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. This chapter reflects on the challenges and risks of AI education, the lack of association between theoretical pedagogical perspectives, and the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions of AI education.

New Model of Inclusive Higher Education Practices in Megacities’ Universities

“…These processes should incorporate student feedback to assess the effectiveness of inclusive practices and make necessary adjustments. As per Ali & Abdel-Haq [25], this feedbackdriven approach helps institutions fine-tune their inclusive policies and practices. Hence, the quality of inclusive higher education is characterized by a curriculum that accommodates diverse learning styles, inclusive pedagogical practices, faculty development, access to support services, cultural competence, and the use of technology to enhance accessibility.…”

“…Quality, inclusive education emphasizes pedagogical practices that cater to students' unique needs. This may involve the use of various teaching methodologies, including interactive, experiential, and collaborative learning [25]. Inclusive pedagogical practices enhance student engagement and academic achievement.…”

How AI hype impacts the LGBTQ + community

“…Underrepresentation of people of colour decreasing accuracy [67] Decreased autonomy and decision-making power for female workers and patients [68] Classification bias of people of colour leads to discriminatory impacts across financial services [69] Centring of dominant characteristics as normative negatively impacting disabled people [70] Hype has been a significant aspect of artificial intelligence research and development since the 1950s. While there have been notable advancements in the field of artificial intelligence in recent years, much of this progress can be attributed to the availability of Big Data and increased computing power rather than substantial strides in what is commonly understood as "intelligence" by the general public [98][99][100].…”

The Design of Mechanisms via Artificial Intelligence

“…The history of artificial intelligence (AI) dates to the 1950s, when researchers first began exploring the possibilities of creating intelligent machines (Ali & Abdel-Haq, 2021). Early AI research focused on creating programs that could mimic human intelligence, such as playing chess or solving mathematical problems.…”