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All Journal Online Learning in Educational Research Diversity Logic Journal Multidisciplinary
Razak, Ahmad Zabidi Abdul
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Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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EDUCATION LEADERSHIP Haliza, Siti; Razak, Ahmad Zabidi Abdul
DIVERSITY Logic Journal Multidisciplinary Vol. 1 No. 2 (2023): September: Diversity Logic Journal Multidisciplinary
Publisher : SYNTIFIC

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Abstract

Background. Leadership is the process by which a leader impacts the conduct of his subordinates in order for them to collaborate and work successfully in order for the organization's goals to be met. Research Purpose. This research aims to determine education leadership. Research Method. By gathering literature (material materials) from books, journals, and other sources pertaining to the field of educational administration, researchers employ the literature study approach. In order to create the article, more precise information on educational leadership resources will be gathered from these sources. The material will thereafter be arranged and modified to fit relevant conversations. Findings. The leadership style of a leader in inspiring personnel to carry out corporate operations will decide the firm's or organization's effectiveness. A number of employees expect to be treated by leaders who are open and flexible in their work, while others hope that the leadership corrects more bad work because this will make subordinates work less productive and less participative in decision making. Conclusion. Leadership is a branch of management that includes planning and organizing, but its primary job is to persuade others to achieve their goals.  
Unveiling the Dual Nature of AI in Grading: A Systematic Review of Benefits and Mitigation Strategies for Algorithmic Bias Risdianto, Eko; Rostika, Rince Aida; Razak, Ahmad Zabidi Abdul; Kartika, Hera Anis; Fitria, Jeni
Online Learning In Educational Research (OLER) Vol 5, No 1 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i1.695

Abstract

The use of Artificial Intelligence (AI) in educational evaluation optimizes learning outcomes. This research seeks to address the advantages and difficulties of implementing AI within academic evaluation frameworks with particular emphasis on the algorithmic bias problem and its implications for fairness in education. The absence of a thorough grasp of algorithmic bias, particularly how it can be utilized as a weapon against equitable education, reveals an important gap. We conduct a Systematic Literature Review (SLR) and bibliometric analysis on 121 articles sourced from Scopus published between 2021 to 2025 to trace the trends and examine the impacts and biases of AI on grading systems. The data demonstrates a significant increase in publications beginning 2018, concentrating on topics such as educational applications of AI, automated grading systems, and machine learning. The findings further indicate that though AI improves efficiency and consistency of the evaluations, it heightens the chances of biased outcomes because of non-diverse training data, prejudiced developers, and socio-cultural frameworks that could worsen the situation for already marginalized learners. In summary, this study highlights the critical gaps in bias mitigation strategies arising from the lack of ethical design frameworks, antecedent-free algorithms, and educator prep courses aimed at combating bias. These outcomes serve as benchmarks for the creation of more reliable and comprehensive AI systems for assessments and shift subsequent investigations to focus validation on different cultures and the incorporation of just AI design paradigms
Co-Authors Eko Risdianto Fitria, Jeni Haliza, Siti Kartika, Hera Anis Rostika, Rince Aida