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All Journal Journal of Information Systems and Technology Research
Nurulannisa Abdullah
UITM Cawangan Kelantan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Ethical Implications of Artificial Intelligence in Lifelong Learning: An Empirical Mixed-Methods Study on Educational Equity Human Capital Development Zohaib Hassan Sain; Anni Rahimah; Nurulannisa Abdullah; Nurhana Fakhriyah Imtinan; Chanda Chansa Thelma
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1486

Abstract

The rapid integration of Artificial Intelligence (AI) into lifelong learning creates a range of opportunities and challenges, especially for educational equity and human capital development. AI applications in educational environments have the potential to enable personalized learning, expand access, and improve outcomes. Yet, these advantages raise important issues regarding privacy, bias, and human oversight in education. The main aim of this research is to investigate how AI can support educational equality in lifelong learning environments. The research aims to recognise and respond to ethical issues, such as bias, privacy, and implications for autonomy in learning. The study employs a mixed-methods design that includes quantitative surveys, qualitative interviews, and document analysis to assess these concerns. Descriptive statistics and regression analysis are employed for quantitative data, while thematic analysis is conducted for qualitative data to identify major patterns related to ethical considerations. Results demonstrate that AI integration is significantly and positively associated with perceived educational equity (β​=​0.45, p​=​0.001), while Data Privacy Concern (β​=​−0.30, p​=​0.003) and Algorithmic Bias Concern (β​=​−0.25, p​=​0.042) show significant negative moderating effects. Qualitative analysis identifies regulatory need (90%), data privacy (75%), and algorithmic bias (60%) as dominant stakeholder concerns. The study underscores the imperative of robust ethical governance frameworks to ensure AI technologies advance educational equity equitably and sustainably
Co-Authors Anni Rahimah Chanda Chansa Thelma Nurhana Fakhriyah Imtinan Zohaib Hassan Sain