The Application and Challenges of Artificial Intelligence in Personalized Learning for Higher Education

The advent of Generative Artificial Intelligence (GenAI) has had a profound impact on higher education, offering numerous benefits, including the enhancement of teaching quality and efficiency, the promotion of educational equity, and the facilitation of personalised learning. Nevertheless, this technology is not without its challenges, including concerns about data privacy and security, over-reliance on GenAI, content accuracy, and potential impacts on employment. It is incumbent upon governments and higher education institutions to adopt a proactive stance by continually developing and refining policy frameworks, enhancing education systems, and cultivating students' critical thinking skills. Adopting this approach will optimise the potential benefits of GenAI while mitigating the potential risks, thus fostering excellence in higher education in the AI era.

The rapid emergence and integration of generative artificial intelligence (GenAI) in higher education present both challenges and opportunities. There is a critical need to consolidate empirical evidence, existing practices, assessments, and normative discussions to reflect on the recent advancement and inform adaptation in higher education policies. Asia presents a compelling context in which to explore the impact of GenAI on higher education due to its diverse educational landscape, rapid technological advancements, and unique socio-cultural dynamics. This Special Issue examines the inclusion of GenAI in higher education in Asia and reflects on the challenges and opportunities brought about by this sweeping revolution. Ten articles in this special issue examine GenAI and its application in higher education from diverse perspectives. Synthesizing these fresh findings based in Asia, this introductory article proposes a framework of actionable strategies for integrating GenAI into higher education that may inform education policymaking and administration in the years to come.

This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.

The advancements in Generative Artificial Intelligence (GenAI) can provide opportunities for enriching educational experiences, but at the same time raise concerns regarding academic integrity. Many educators have expressed anxiety and hesitation when it comes to integrating GenAI in their teaching practices. Thus, recommendations and guidance from institutions are needed to support instructors in this new and emerging GenAI era. In response to this need, this study explores different U.S. universities' academic policies and guidelines regarding the use of GenAI tools (e.g., ChatGPT) for teaching and learning, and from there, gains understanding of how these universities respond and adapt to the development of GenAI in their academic contexts. Data sources include academic policies, statements, guidelines, and relevant resources provided by the top 100 universities in the U.S. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Primary concerns lie in ethical usage, accuracy, and data privacy. Most universities actively respond and provide diverse types of resources, such as syllabus templates, workshops, shared articles, and one-on-one consultations; focusing on a range of topics, namely general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings provide four practical pedagogical implications for educators when considering GenAI in teaching practices: 1) accepting GenAI presence, 2) aligning GenAI use with learning objectives, 3) evolving curriculum to prevent misuse of GenAI, and 4) adopting multifaceted evaluation strategies. For recommendations toward policy making, the article suggests two possible directions for the use of GenAI tools: 1) establishing discipline-specific policies and guidelines, and 2) managing students' sensitive information in a transparent and careful manner.

Generative AI tools and empowerment in L2 academic writing

Education has different meanings as per different approaches. The quality in education is a complex and multicultural process. This study was conducted to check the Quality in higher Education. Four higher institutions were taken as sample. Out of which two were from public sector. The theoretical frame work consisted of the dependent variable which was, “Quality in Higher Education”, and the independent variables which were Management, Faculty, Infrastructure, Research Facilities and Evaluation Criteria. Overall results showed that majority (73%) respondents were in view that there is quality in education in higher education institutions. The most important factor which helps to maintain quality in higher education is the evaluation criteria in examinations (responded 23%). The lack of proper infrastructure is the main problem of education institutions (responded 35%). The above results show that quality in higher education is an essential tool to boost up the education sector of any country.

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to whole-hearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communi-cators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation.

Background: Generative Artificial Intelligence (GenAI) technologies such as ChatGPT, Bard, and Claude have emerged as transformative forces in higher education, reshaping teaching, learning, assessment, and research practices. While their integration offers significant potential for personalisation, pedagogical innovation, and productivity enhancement, it simultaneously raises complex concerns regarding ethics, academic integrity, data privacy, and institutional readiness. The fragmented nature of existing research, particularly across Global South contexts such as South Africa, highlights the need for a comprehensive synthesis to guide responsible and equitable GenAI adoption. Purpose: This study systematically reviews recent empirical and conceptual research to examine GenAI readiness, adoption determinants, ethical implications, and regional disparities within higher education. It further identifies theoretical limitations within established frameworks, Technology Acceptance Model (TAM), Technology-OrganisationEnvironment (TOE), Technology Readiness Index (TRI), and Theory of Planned Behaviour (TPB) and proposes a hybrid conceptual framework integrating socio-cultural and ethical dimensions. Methods: A PRISMA-guided systematic literature review was conducted across Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar. Out of 192 identified studies, 18 met the inclusion criteria (2023-2025). Thematic synthesis combined inductive and deductive coding to map patterns across readiness, adoption predictors, ethical issues, and policy trends. Results: Findings reveal four key readiness dimensions: technical access, psychological preparedness, ethical literacy, and institutional support. Predictors of adoption included perceived usefulness, ease of use, selfefficacy, and leadership support, while barriers centred on ethical uncertainty, data privacy concerns, and infrastructural limitations. Conclusions: A hybrid TAM, TOE, TRI, TPB framework, extended with socio-cultural and ethical components, is proposed to explain GenAI adoption holistically. The study recommends targeted AI literacy, ethical governance, and context-sensitive policies to foster equitable and sustainable integration of GenAI in higher education.

Artificial intelligence (AI) systems have become ubiquitous daily, yet many are unaware of their presence. The advancements in artificial intelligence have contributed significantly to higher education by changing how we approach problem-solving. The transformative potential of AI technology in education and training cannot be overstated. Currently, educators are utilizing AI systems to identify individual learning needs and experiences, make data-driven decisions and allocate resources more effectively. This chapter aims to equip readers with a comprehensive knowledge of the application of AI-powered decision-making in higher education. With the valuable insights and resources provided, readers can confidently integrate and leverage the potential of AI in their decision-making processes. To fully realize the potential of AI, educators, and leaders must have a fundamental understanding of its capabilities and ethical considerations. This knowledge will enable them to engage confidently and critically with AI technology and maximize its benefits.

Generative artificial intelligence in dentistry: A narrative review of current approaches and future challenges

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).

This study investigates the influence of generative artificial intelligence (GAI), specifically AI text generators (ChatGPT), on critical thinking skills in UK postgraduate business school students. Using Bloom’s taxonomy as theoretical underpinning, we adopt a mixed-method research employing a sample of 107 participants to investigate both the influence and challenges of these technologies in higher education. Our findings reveal that the most significant improvements occurred at the lower levels of Bloom’s taxonomy. We identify concerns relating to reliability, accuracy, and potential ethical implications of its application in higher education. The significance of this paper spans across, pedagogy, policy and practice, offering insights into the complex relationship between AI technologies and critical thinking skills. While highlighting the multifaceted aspects of the impact of AI in education, this article serves as a guide to educators and policymakers, stressing the importance of a comprehensive approach to fostering critical thinking and other transferable skills in the higher education landscape.

Generative Artificial Intelligence (GenAI) is changing the landscape of higher education and profoundly affecting college students' learning performance and learning psychology. This study explored the types of behaviors that college students use GenAI and the mechanisms that influence academic burnout. The data were from 369 full-time undergraduate students through a questionnaire, and the hypotheses were analyzed using SPSS 27.0 and the Amos 28.0 plug-in. Results showed that undergraduates demonstrated high levels of GenAI use and acceptance in their academic endeavors, with GenAI assisted homework receiving the highest scores. Significant differences in the performance of GenAI users were observed on gender, academic achievement, and major category. SEM revealed that plagiarized GenAI use and GenAI-assisted classes directly increased academic burnout. In contrast, the effects of GenAI alternative search tasks, GenAI assisted homework, and GenAI evaluation feedback on academic burnout were indirect, mediated through plagiarized GenAI use and GenAI assisted classroom responses. Specifically, there are complex mechanisms of influence between GenAI use behaviors and college student burnout. This study brings new perspectives to the existing literature and articulates valuable insights around the use of GenAI in higher education practice.

This chapter examines the increasing role of Artificial Intelligence (AI) in higher education, focusing on its integration, implementation strategies, and the ethical challenges that arise. It provides insights into how generative AI (GenAI) tools and large language models (LLMs) can be used effectively in classrooms, offering educators and administrators practical guidance for navigating this evolving landscape. The chapter also highlights potential future developments of AI in higher education and explores professional development opportunities to help institutions adapt. By addressing both the benefits and challenges of AI, the chapter aims to empower higher education professionals with the knowledge and tools needed to use AI to enhance teaching, learning, and overall educational experiences. Ultimately, it provides a guide for effectively incorporating AI to support and transform educational practices.