International Journal of Higher Education Management

In today’s dynamic business environment, Machine Learning (ML) or algorithm-based, data-driven models are essential for competitive advantage and strategic planning. This study aims to demonstrate the effectiveness of ML models - specifically the standard K-means clustering algorithm in identifying patterns that can inform strategic business decisions. A synthetic dataset was generated to simulate real-world business data scenarios, and the K-means algorithm was applied both with and without data pre-processing techniques such as scaling. The results indicate that although K-means remains a powerful and widely applicable clustering method, its performance is significantly improved by proper data scaling and identification of the optimal number of clusters. The findings of this study offer valuable insight how to develop business strategies over complex business scenarios.

Emotional Intelligence is a vital strategy that should be emphasized in both public and private higher education institutions as it involves recognizing emotions as valuable sources of information that support effective interaction within social contexts. EI encompasses the ability to control one’s own emotions, understand other feelings, and influence through processes and behaviors accordingly. Integrating EI into university settings can improve students’ cognitive approaches not only through academic theories but also through engaging them in practical activities such as economic simulation models and consultation circles. These methods foster the extraction of economic insights that can inform policymakers’ decisions. 

Simulation models significantly influence both students and policymakers. However, their current use is largely centered on improving students’ knowledge, skills, and overall character development. While this educational benefit is widely recognized, the potential impact of these models on policymakers is overlooked. It is essential to acknowledge that policy makers can benefit greatly from engaging with students through consultation circles, which provide innovative ideas, and collaborative economic solutions.

This paper argues for the relationship between economic simulation models developed by academic advisors in public and private institutions as tools of emotional intelligence and their role in promoting economic integration. This study aims to explore student-led economic simulation models in universities by examining their effectiveness, practices, activities, and outcomes. Using qualitative methods specifically interviews and surveys with university students and academic advisors, this study aims to determine whether the evidence supports or rejects the relationship between both variables

The active version of post-independence National Education Policy was unveiled by the Government of India on July 09, 2020.The foremost objective of the newest version of the education policy is to endorse holistic education across the country. The policy aims to bridge the gap between classroom teachings and industry requirements through integration of various skill development courses, entrepreneurship education and multidisciplinary courses. The policy also emphasizes exposure of innovation and creativity into the mind-set of the students. The early exposure to entrepreneurial education, skill based education will make the student job-ready and hopes to fulfil the vision of ‘Atmanirbhar-Bharat’. The introduction of the vocational education, multidisciplinary courses, industry academia linkage will make the Indian youth more employable ready than just being a mere graduate. The introduction of Academic Bank of Credit, multiple entry –exit system, will act as a boon to students. The policy aims to transform the previous version of exam oriented evaluation system to more value oriented evaluation system. The study aspires to find out, how the modern version of the education policy will effortlessly transform the classroom learnings in to career building.

This paper pushes the conversation about authentic leadership by introducing the variable of identity. Recent discourse on authentic leadership identifies it as an effective and desirable approach to leadership, but to date the conversation has failed to acknowledge that some in leadership are more able to pursue authenticity given their privileged identities, and others in leadership face more risk in authenticity given their minoritized identities. Using key understandings of privilege and authenticity, along with personal testimonies, we provide recommendations for both those with privileged statuses and those with minoritized statuses on how to effectively embrace and employ authentic leadership.

This paper explores how Artificial Intelligence (AI) is transforming curriculum development and teaching and learning practices in postgraduate education. Drawing on a multi-case qualitative research design across six global higher education institutions, the study investigates the mechanisms, implications, and challenges of AI adoption at the postgraduate level. The paper is underpinned by a robust conceptual framework that integrates the Technological Pedagogical Content Knowledge (TPACK) model, transformative learning theory, and collaborative intelligence.

The findings suggest that AI enables responsive curriculum design, fosters collaborative knowledge creation, and reconfigures the roles of educators. At the same time, the study highlights significant disparities in institutional readiness, ethical ambiguities, and concerns about cognitive outsourcing and student originality. Through interviews with curriculum leaders, academic staff, and policy advisors, and triangulated with institutional strategy documents and AI implementation frameworks, the research provides a multidimensional view of current practices.

The paper offers both conceptual contributions and practical implications. It argues that AI can catalyse curriculum innovation when aligned with educational values and pedagogical intentionality. The study concludes with recommendations for curriculum designers, educators, institutional leaders, and policymakers to ethically and effectively integrate AI into postgraduate programmes. This research contributes to a growing body of knowledge calling for critical engagement with AI in higher education, especially at the postgraduate level.