Artificial Intelligence Versus Arab Universities: An Enquiry into the Saudi Context

<p>The emergence of generative Artificial Intelligence (AI) is disrupting every sector of the global economy and the information society. The education industry, similarly, has both educators and students exploring ways to utilize AI. This study investigates the adoption of AI in higher education among undergraduate mathematics education students using the four constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research focuses on Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, and their influence on students’ Behavioural Intention to adopt AI tools. Gender was included as a moderating variable in the relationships between these constructs and behavioural intention. A sample of 142 undergraduate mathematics education students participated in an online survey to measure their perceptions and intentions of AI adoption in their academic work. The findings revealed that all four independent variables and gender had significant direct effects on Behavioural Intention to use AI. This indicates that students perceive AI tools as valuable for enhancing academic performance, easy to use, influenced by social factors such as peers, and supported by adequate facilitating conditions such as technical infrastructure. However, gender did not emerge as a significant moderator in any of the relationships between the UTAUT constructs and Behavioural Intention. This suggests that male and female students exhibit similar adoption patterns toward AI technologies in this context. These results contribute to the growing body of literature on technology adoption in education by confirming the applicability of the UTAUT model within a mathematics education-focused cohort of students while highlighting that gender differences may not play a critical role in shaping intentions toward AI adoption. Future research could explore additional moderating variables or extend this analysis across other disciplines for broader generalizability.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0188/a.php" alt="Hit counter" /></p>

Purpose Generative Artificial Intelligence (AI), gamification, and digital literacy have transformed higher education by enhancing student engagement, personalizing learning, and fostering digital competencies. This study investigated the impact of three dimensions of Generative AI—knowledge, willingness, and concerns—on students’ perceptions of learning and academic performance in Saudi Arabia. It also explores the roles of gamification and digital literacy. Both Gamification and digital literacy moderate the relationship between Generative AI and educational outcomes, whereas digital literacy affects the association between AI factors and students' perceptions of learning and performance. Design/methodology/approach A quantitative research design was adopted to collect data from 550 postgraduate students across various Saudi universities and disciplines. This cohort was chosen because of its heightened capacity to engage critically with advanced educational technologies. Employing two analytical approaches, the measurement model (validity and reliability) and structural equation modelling (hypothesis testing), statistical analysis was used to assess the relationships. Findings The results demonstrated that all the proposed relationships were significant. Knowledge, willingness, and concerns regarding generative AI were positively related to both PLE and PAP (H1: β = 0.328, p = 0.000), (H2: β = 0.192, p = 0.001), (H3: β = 0.294, p = 0.000), (H4: β = 0.149, p = 0.002). (H5: β = 0.138, p = 0.003) respectively. In addition, gamification and digital literacy exhibited significant moderating effects, as indicated in the results section, amplifying the positive impact of AI integration on students’ academic perceptions and outcomes. Originality/value This study provides empirical evidence of the interplay between Generative AI, gamification, and digital literacy in higher education, delivering practical insights for policymakers, educators, and technology developers. This underscores the importance of digital literacy training programs and policies promoting responsible, gamified AI integration, enhancing effective and inclusive learning environments in Saudi Arabia and beyond.

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the emerging fields in education technology. Whilst it has been around for about thirty years, educators remain unclear as to how to take full pedagogical advantage of AI on a broader scale and how it could actually have a meaningful impact on teaching and learning in higher education. This paper aims to evaluate Artificial Intelligence within Higher Education, focussing on the opportunities and challenges it presents. It also investigates the educational implications of emerging technologies on the way students learn and how institutions teach and evolve. The paper gathers some examples of the introduction of AI in education in a bid to establish equitable, quality education for all. Firstly, the paper analyses how AI can be used to improve learning outcomes, presenting examples of how AI technology can help education systems use data to improve equity and quality in Higher Education. The paper also addresses the benefits and challenges of introducing AI in educational settings, as well as the potential risks of such an endeavour. Finally, we put forward some recommendations for AI in education, with a focus on establishing discussions around the uses, possibilities and risks of AI in education for sustainable development.

The growth of Artificial Intelligence is no more shrouded to the world we are living in. The advancement of digital technology and Artificial Intelligence has occurred in almost every field, not leaving behind the field of education. Artificial intelligence has revolutionized the way of learning and teaching making the process of learning more efficient and personalized. Artificial Intelligence in education refers to the usage of artificial intelligence tools like machine learning, or natural language processing and technologies in variety of ways to make a better self-relied experience of learning for students. The objective of current study was to investigate the abundant use and effect of using Artificial Intelligence tools for different functions of education or study. This quantitative study used data form 400 research participants who were students from several government and private universities. The present study utilizes convenience sampling. Data collection was from students of higher education of government and private universities. The survey questionnaire of Prof. Dr. Jorg von Garrel was used to get the comprehensive view of student’s utilization of artificial intelligence. The findings provided evidence of usage of Artificial Intelligence tools like Chat GPT with highest frequencies/percentage of almost 80% among other tools. The significant associations were also found between variables. In conclusion, this research study give a valuable insight towards the trend of using the Artificial Intelligence tools by the students for different perspectives of learning and studying.

Artificial Intelligence (AI) is permeating many facets of our daily lives, appearing in household appliances, cell phones and popular online apps. AI has the capacity to revolutionize teaching and learning processes in higher education institutions. The integration of AI technologies in education can lead to personalized learning experiences, improved educational quality and enhanced learning outcomes. However, the adoption of AI in higher education comes with challenges such as ethical considerations and the need to address equity and inclusion issues to ensure that all students benefit from AI advancements. This chapter considers how AI can be utilized in education, while acknowledging the challenges and finding ways to mitigate them. Useful tools include: Bespoke Learning, Intelligent Tutoring Systems, Grading, Collaborative, Learning Assistance, Research Support and Adaptive Learning. The challenges addressed are: ethical considerations, resistance to change and data security and privacy. In navigating the complexities of integrating AI in higher education, institutions must strike a balance between leveraging the transformative potential of AI technologies and addressing the ethical, social and technical challenges that accompany their implementation. By prioritizing ethical considerations, addressing resistance to change and safeguarding data security and privacy, higher education institutions can harness the benefits of AI to enhance teaching and learning practices, foster innovation and prepare students for success in the digital age.

The rapid development of artificial intelligence (AI) has significantly influenced various sectors, including education. A notable advancement is the use of AI language models in assessments. AI language models, such as GPT-4.0, are being trialled to develop innovative tools to enhance assessment processes. The adoption of AI in higher education has been met with enthusiasm and scepticism from university management and academics. As such, it has created much uncertainty and anxiety in higher education. This qualitative study draws on the unified theory of acceptance and use of technology. It explores the question of university academics' perspectives on the multifaceted impact of AI language models on educational assessment. The study's objective was to determine university academics' perspectives on assessment in the context of radical innovations in AI technology to establish proclivities for technology adoption and the extent of instructional responses and practice. A sample of 29 academics, drawn from a South African university, responded to an online open-ended schedule of questions and an open-coding, thematic analysis was applied. The findings revealed that in the main, participants believed that AI could make student assessment processes more efficient and improve the quality of assessments such as by generating and validating multiple-choice questions, emphasising intrinsic motivation, and creating a more effective formative feedback process. The data also revealed participants' pleas for circumspection and responsive institutional policy and cautioned about (un)witting student transgressions and criminality. Given the relative novelty of generative AI in the academic arena and the exploratory nature of this study, this article offers tentative implications for theory and practice.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

This study explores the potential of artificial intelligence–based information systems in supporting educational processes within higher education institutions in Indonesia. The rapid adoption of digital platforms in academic administration and learning management has increased the need for intelligent systems that can enhance efficiency, transparency, and data-driven decision-making. This research aims to examine how artificial intelligence–based information systems are utilized in educational contexts, particularly in relation to curriculum implementation, academic management, and institutional readiness. A qualitative research design was employed using semi-structured interviews with academic stakeholders, complemented by document analysis. The findings indicate that artificial intelligence–based information systems contribute positively to improving administrative efficiency, supporting systematic curriculum evaluation, and facilitating evidence-based academic decision-making. However, challenges related to system transparency, interpretability, and user readiness were also identified as critical factors influencing system effectiveness. These findings highlight that the successful integration of artificial intelligence in education is not solely determined by technological capability but also by organizational support and human factors. This study contributes to the interdisciplinary integration of information systems and educational research by providing empirical insights into the role of artificial intelligence in higher education. The results offer practical implications for institutions seeking to adopt responsible and effective artificial intelligence–based information systems.

The acceptance of, perceived advantages to, and skepticism toward the integration of artificial intelligence (AI) in undergraduate education is investigated in this paper. In this study, a total of 675 students from six Romanian universities answered a self-administered online questionnaire evaluating three main aspects: AI acceptance, AI benefits, and AI skepticism. While AI skepticism has a modest but substantial negative influence (β = −0.113, p = 0.001), results show that AI benefits favorably predict AI acceptance (β = 0.541, p = 0.001). Whereas AI skepticism negatively correlates with AI acceptance (r = −0.124, p = 0.001), correlational analysis reveals a high positive association between AI acceptance and AI benefits (r = 0.544, p = 0.001). Despite concerns about its limitations, the regression model suggests that students’ willingness to embrace AI in education is mostly driven by its perceived advantages. This explains 30.8% of the variance in AI acceptance (R2 = 0.308, F(2, 641) = 142.909, p < 0.001). These results highlight the importance of techniques that improve perceived benefits while addressing uncertainty since they offer insightful analysis of student attitudes regarding artificial intelligence integration in higher education. By guiding policy decisions and educational activities meant to maximize AI-driven learning environments, this study adds to the current conversation on artificial intelligence adoption in education.

<sec> <title>BACKGROUND</title> Intelligent technologies are transforming healthcare delivery, necessitating that nursing curricula prepare students for digitally enhanced practice environments. However, empirical evidence examining nursing students' readiness for technology adoption, particularly through established theoretical frameworks, remains limited in Middle Eastern educational contexts. </sec> <sec> <title>OBJECTIVE</title> The objectives of this study were to (1) assess the level of awareness among nursing students regarding the use of artificial intelligence (AI) applications in nursing education; (2) examine nursing students' perceptions of the potential benefits and challenges associated with AI adoption, guided by the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT); and (3) evaluate nursing students' readiness and preparedness for artificial intelligence adoption by analysing the relationships between UTAUT-based theoretical constructs and behavioural intention to use AI technologies in educational and clinical training contexts. </sec> <sec> <title>METHODS</title> A cross-sectional survey was conducted among 314 undergraduate nursing students at a university in the United Arab Emirates. Data were collected using a validated questionnaire (Cronbach α=.89; content validity index=0.92) measuring UTAUT constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Analysis utilized IBM SPSS Statistics version 28.0, including descriptive statistics, Pearson correlation, and multiple regression </sec> <sec> <title>RESULTS</title> Students demonstrated high awareness (298/314, 94.9%) and training interest (261/314, 83.1%), with favourable perceptions of artificial intelligence's educational benefits. However, practical confidence remained lower (186/314, 59.2%), and three-quarters indicated needing substantial support. Performance expectancy (mean 3.96, SD 0.72) and facilitating conditions significantly predicted behavioural intention. The regression model explained 58% of variance in behavioural intention (R²=0.58; F6,307=71.24; P&lt;.001). Performance expectancy emerged as the strongest predictor (β=.38; P&lt;.001), followed by social influence (β=.20; P&lt;.001) and effort expectancy (β=.19; P=.001). A pronounced gap emerged between theoretical readiness (mean 3.78-4.09) and actual preparedness (mean 2.00-2.56), with insufficient training (mean 2.03, SD 0.76) and limited practical experience (mean 2.13, SD 1.34) as primary deficits. </sec> <sec> <title>CONCLUSIONS</title> The Extended UTAUT framework effectively explained nursing students' artificial intelligence adoption intentions. While students demonstrate positive attitudes, the readiness-preparedness gap highlights urgent needs for structured competency development, faculty training, and phased implementation strategies in nursing education. </sec>

This qualitative study investigates the opportunities and concerns regarding the integration of artificial intelligence (AI) in higher education. Through in-depth interviews with students, faculty, parents, administrators, policymakers, and employers, the research explores the complex landscape of AI adoption in colleges and universities. Thematic analysis reveals shared anxieties about equity and inclusion, the preservation of human interaction in teaching, potential job displacement, the ethical implications of AI, technical capabilities and resource requirements, the impact on educational quality and student outcomes, and the alignment of AI education with workforce demands. The findings underscore the need for a collaborative and transparent approach to AI integration that addresses stakeholder concerns and prioritizes ethical considerations, pedagogical effectiveness, and societal values.

AI (Artificial intelligence) has permeated the world and currently has an undeniable footprint in most areas of society. In relation to the HE (higher education) sector, AI is becoming a prominent feature. AI in HE is readily accessible through the use of ‘intelligent tutoring systems, teaching robots, learning analytics dashboards, adaptive learning systems and human-computer interfaces’ (Ishak &amp;amp; Jiang 2022:70). Pelletier, Robert, Muscanell, McCormack, Reeves, Arbino, and Grajek (2023:4) aptly highlight the impact that AI currently has on the globe and the need to consider it as a current trend in the HE sector. Huang, Saleh, and Liu (2021:206) similarly argue that it is indisputable that AI has infiltrated the education space. As modern science and technology progress, the advancement in AI has also progressed. Research indicates that AI, when applied to education, has resulted in positive effects which have aided toward teaching reform (Huang et al. 2021:206).

The rapid adoption of artificial intelligence (AI) tools in higher education has introduced new opportunities for learning and research, while simultaneously raising complex legal and ethical concerns related to copyright and intellectual property. This study examined the copyright literacy of librarians and its relationship with their support for AI instruction among students in selected higher education institutions in Tanzania, namely the Tanzania Institute of Accountancy, College of Business Education, Institute of Finance Management, and the National Institute of Transport. The study employed quantitative cross-sectional survey design, using a structured questionnaire administered to librarians. Data were analyzed using descriptive statistics and Pearson correlation analysis. The findings revealed that although librarians were generally well educated and professionally experienced, the majority had not received formal training in copyright or AI. The study concludes that institutional factors, including policy frameworks, training opportunities, and organizational support, play a crucial role in shaping librarians’ involvement in AI education. The study recommends the development of institutional AI and copyright policies, continuous professional development programs, and stronger institutional support mechanisms to enhance librarians’ capacity to promote ethical and lawful use of AI in higher education.

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.

Human-centered approaches are vital to manage the rapid growth of artificial intelligence (AI) in higher education, where AI-driven applications can reshape teaching, research, and student engagement. This study presents the Human-Centered AI for Systematic Literature Reviews (HCAI-SLR) framework to guide educators and researchers in integrating AI tools effectively. The methodology combines AI augmentation with human oversight and ethical checkpoints at each review stage to balance automation and expertise. An illustrative example and experiments demonstrate how AI supports tasks such as searching, screening, extracting, and synthesizing large volumes of literature that lead to measurable gains in efficiency and comprehensiveness. Results show that HCAI-driven processes can reduce time costs while preserving rigor, transparency, and user control. By embedding human values through constant oversight, trust in AI-generated findings is bolstered and potential biases are mitigated. Overall, the framework promotes ethical, transparent, and robust approaches to AI integration in higher education without compromising academic standards. Future work will refine its adaptability across various research contexts and further validate its impact on scholarly practices.