With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods and students’ learning processes. While acknowledging that AI advancements offers numerous advantages and contribute significantly to societal progress, excessive reliance on AI within education may give rise to various issues, students’ over-dependence on AI can have particularly severe consequences. Although many scholars have recently conducted research on artificial intelligence, there is insufficient analysis of the positive and negative effects on higher education. In this paper, researchers examine the existing literature on AI’s impact on higher education to explore the opportunities and challenges presented by this super technology for teaching and learning in higher educational institutions. To address our research questions, we conducted literature searches using two major databases—Scopus and Web of Science—and we selected articles using the PRISMA method. Findings indicate that AI plays a significant role in enhancing student efficiency in academic tasks and homework; However, when considering this issue from an ethical standpoint, it becomes apparent that excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology. Therefore, our research provides essential guidance for stakeholders on the wise use of artificial intelligence technology.

This paper aims to analyze the impact of access to Information and Communication Technologies (ICT) on the private returns to higher education (HE) focusing on gender inequality in 2020. Methodology: To evaluate the above impact a set of Mincerian equations will be estimated. The proposed approach mitigates biases associated with self-selection and individual heterogeneity. Data: The database comes from the National Household Income and Expenditure Survey (Encuesta Nacional de Ingresos y Gastos de los Hogares, ENIGH) from 2020. Results: Empirical evidence suggests that individuals that have HE have a positive and greater impact on their salary income compared to those with a lower educational level, being women that do not have access to ICT those with the lowest wage return. Policy: Access to ICT should be considered as one of the criteria that integrate social deprivation in the measurement of multidimensional poverty. Likewise, it is necessary to design public policies that promote the strengthening and creation of educational and/or training systems in technological matters for women. Limitations: No distinction was made between individuals that graduated from public or private schools, nor was income from sources other than work considered. Originality: This investigation evaluates the impact of access to ICT on the returns to higher education in Mexico, in 2020, addressing gender disparity.

The COVID-19 epidemic caused unexpected complications, complexities and challenges in higher educational institutions (HEIs). In order to promote and strengthen the role of women leadership, this study aimed to clarify the unique challenges faced by female leaders at Saudi HEIs during the epidemic, find possible solutions to these challenges, and provide policy as well as management implications. A systematic literature review (SLR) was conducted, examining 27 records (i.e., research papers, articles and conference studies). The data were qualitatively analysed and categorized based on themes like challenges faced, opportunities recognized, and solutions proposed. Findings highlighted women leaders in Saudi HEIs grappled with multiple challenges, including technological barriers, cultural constraints, and increased workloads. Merging challenges with solvable strategies offers a forward-looking perspective, advocating for systemic changes that can shape a resilient and inclusive future for HEIs in Saudi Arabia.

Over the past decade, the integration of technology, particularly gamification, has initiated a substantial transformation within the field of education. However, educators frequently confront the challenge of identifying suitable competitive game-based learning platforms amidst the growing emphasis on cultivating creativity within the classroom and effectively integrating technology into pedagogical practices. The current study examines students and faculty continuous intention to use gamification in higher education. The data was collected through an online survey with a sample size of 763 Pakistani respondents from various universities around Pakistan. The structural equation modeling was used to analyze the data and to investigate how continuous intention to use gamification is influenced by, extended TAM model with inclusion of variables such as task technology fit, social influence, social recognition and hedonic motivation. The results have shown that task technology has no significant influence on perceived usefulness (PU) where as it has a significant influence on perceived ease of use (PEOU). Social influence (SI) indicates no significant influence on perceived ease of use. Social recognition (SR) indicates positive influence on perceived usefulness, perceived ease of use, and continuous intention. The dimensional analysis indicated that perceived ease of use has insignificant influence on perceived usefulness. Both PEOU and PU exhibit positive influence on attitude. Hedonic motivation (HM) and attitude were observed to have a positive influence on continuous intention (CI). Moreover, gamification is found to efficiently and effectively achieve meaningful goals by tapping intrinsic motivation of the users through engaging them in playful experiences.

The Malaysian government’s heightened focus on Technical and Vocational Education and Training (TVET) reflects a strategic move towards economic and social development, particularly in addressing youth unemployment. Recognizing the potential of TVET to contribute to these goals, there is a specific emphasis on enhancing the marketability of women in the workforce from the current 62 percent to an ambitious 95 percent. However, a notable gender gap persists in entrepreneurial pursuits within the TVET sector in Malaysia, with female representation lagging. To bridge this gap, this study aims to construct a comprehensive framework that nurtures future-ready female TVETpreneur talent. This initiative aligns with the Malaysian Higher Education Blueprint, 2021–2025, i.e., fostering a diverse and innovative workforce. An extensive literature survey was conducted to identify the factors influencing female TVET students’ entrepreneurial intention. The literature revealed that social psychological and organizational approaches are commonly used to explore and analyze the relationship between the influence of female TVET students’ talents and behavior, their exposure to entrepreneurship, mentorship and support programs, role models in TVET, curriculum design, and access to resources. A comprehensive theoretical framework was developed based on these findings, which offers significant insights related to enhancing TVET opportunities for women and advancing Malaysia’s economic and social development goals in a sustainable way.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.