Search Results for
"Students"

In the wake of the fourth industrial revolution, artificial intelligence is gaining momentum and is widely applied in various aspects of life, particularly education. This study investigates the factors influencing students' use of artificial intelligence (AI) in learning, focusing on students at Ho Chi Minh City University of Industry. The research uses a combination of the technology acceptance model and the theory of planned behavior to examine the relationships between subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention. Combining these technological models brings new insights into the context of AI that can support or hinder user behavior through bias. The results were then analyzed based on the least squares linear structural model, with 390 students participating in the survey using the stratified sampling approach. The study found that perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning. Subjective norms also play an essential role in shaping students' image and intention to use AI. The research also highlights the importance of self-efficacy, perceived enjoyment, playfulness, output quality, result demonstrability, and job relevance in influencing students' perceptions and use of AI. The findings of this study underscore the need for educational institutions to create a supportive environment that encourages students to use AI in learning. In contrast, AI technology creators need to focus on simplifying the user experience to make AI tools more accessible and easy to use. These practical recommendations of the research can guide policy and design decisions in the field of educational technology. Finally, in place of a conclusion, the study also aims to open up further approaches for AI platforms in academia. © 2025 Malque Publishing. All rights reserved.

The higher participation of England’s ethnic minority students on vocational courses at the age of 16 initiates them onto the ‘lower tier’ pathway in tertiary education. Lower tier implies participating in certain vocational courses and entering lower status universities. Consequently, ethnic minority students in England have a weaker labour market position upon graduation. Using semi-structured interviews, this article explores the decision-making process of 12 British Pakistani students, as they are initiated on this lower tier pathway. The findings suggest that decision-making is a negotiation of student biography with ethnic capital and ‘racial warehousing’. This interplay funnels many ethnic minority students towards the lower tier pathway, where selecting certain vocational courses is deemed as the only pathway feasible for progression. This study sheds light on the need to promote alternative vocational pathways to England’s ethnic minority students. © 2023 The Vocational Aspect of Education Ltd.

The persistent skills mismatch between higher education outcomes and labour market needs presents a significant challenge for graduate employability, particularly in fast-evolving sectors such as artificial intelligence (AI) and data science. This paper introduces a data-driven internship framework designed to bridge this gap by optimising student placements, providing real-time feedback, and ensuring academic curricula remain aligned with labour market demands. The framework integrates three systems: the Student Industry Placement System (SIPS), which uses machine learning to match students with relevant internships; the Intern Learning Management System (ILMS), which tracks skill development through continuous feedback loops; and the Industry Profiling System (IPS), which leverages predictive analytics to provide real-time labour market insights. The framework was evaluated through a mixed-methods approach, incorporating pilot program data from five universities and 15 industry partners and simulated data based on historical internship trends. The SIPS algorithm achieved a placement accuracy of 89%, significantly improving over traditional methods (60-65%). 78% of students received continuous feedback through the ILMS, resulting in measurable improvements in technical skills (30% increase in coding proficiency) and problem-solving abilities (25% improvement). The IPS enabled universities to update 15% of their curricula in response to real-time labour market data, particularly in high-growth sectors such as AI and digital transformation. 68% of the students secured full-time employment within six months of graduation, with 65% in roles directly aligned with their internships. These findings demonstrate the framework's scalability and potential for reducing skills mismatch, making it a viable model for other emerging economies. © 2025 IEEE.

This article examines how vocational school principals in Israel view their relationships with their students' parents. Interviews with 26 vocational school principals reveal three predominant themes: lamenting the parents' lack of involvement, describing the parents through the deficit discourse, and adopting a managerial strategy of operating a ‘school without parents.’ This approach to parents carries implications, such as implementing many improvisations in light of the lack of a recognized cultural script of a ‘school without parents’, making educational and therapeutic decisions in place of the parents, taking the role of surrogate parent, and preferring caring practices over the cultivation of the students' academic capital. The Discussion explores these findings through the concept of symbolic violence. Moreover, the Discussion examines the impacts of the principals' descriptions and the symbolic violence accompanying them on establishing and maintaining social inequality and educational stratification. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.