Linkages Among AI Elements Affecting Quality and Value of Education

Abstract

Artificial intelligence (AI) has advanced rapidly in recent years and 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 decision-making 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 modelling 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.

Received: 27 July 2024 | Revised: 28 November 2024 | Accepted: 14 February 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Conyribution Statement

Sneha Seth: Conceptualization, Software, Formal analysis, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization. Hans Kaushik: Methodology, Validation, Investigation, Writing - review & editing, Supervision, Project administration.