Keyword: quality education

ABSTRACT: The total quality management (TQM) is a management practice based on the idea of continuous quality improvement of an organization’s products and services, identifying and eliminating problems right from the root, with the participation of all members. For
an educational institution, TQM can improve the quality of education, help with the learning process, provide an atmosphere with less risk of inadequate decisions, and many more. In educational institutions, the implementation of TQM is the responsibility of process approach, management responsibility, customer focus, and continuous improvement. Specifically, digital infrastructure, digital technology, and digital capabilities affect TQM practices directly. This article provides a synthesis and analysis of findings from 51 international publications on the implementation of TQM in secondary schools. This study revealed that more than the commonly stated advantages, addressing the soft facets including student/stakeholder contentment, leadership, continuous enhancement of processes and systems, teacher coaching, and effective use of technology are critical elements for successful implementation of TQM. In addition to that educational managers have to make an espousal to TQM as an omnibus management philosophy, as a supportive culture and enabling the involvement of all stakeholders.

ABSTRACT: Artificial intelligence (AI) has advanced rapidly in recent years and has become widely integrated across various fields, including education. This paper seeks to provide a comprehensive examination of the current state of AI in education by exploring its potential to revolutionize learning experiences through personalized approaches and data-driven multifaceted tools, while also highlighting important challenges that require consideration to ensure its responsible development and implementation. AI shows great promise to personalize instruction for each student based on assessments of their individual strengths, weaknesses, interests, and learning preferences. However, several challenges still necessitate careful examination of AI’s implications on education. Issues like algorithmic bias, the digital divide between socioeconomic groups, and concerns around reduced critical thinking skills all require addressing. If not developed and applied judiciously with these challenges in mind, AI risks exacerbating rather than alleviating existing inequities and hindering the cultivation of higher-order cognitive abilities. Through a comprehensive review of the relevant literature regarding AI’s current and potential roles in education, this paper identifies several key considerations around learning outcomes, challenges, and implications. Findings from interpretative structural modeling analysis also reveal the importance of balancing AI capabilities with safeguarding against potential downsides like those mentioned above. It is imperative that AI integration in education is approached responsibly with an understanding of both its promise and risks to learning to ensure its successful and equitable implementation for all students.