Linkages Among AI Elements Affecting Quality and Value of Education

What is it about?

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 interpre-tative 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.

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Photo by Steve Johnson on Unsplash

Photo by Steve Johnson on Unsplash

Why is it important?

After a careful analysis of the literature, it was found thatsince AI has been used in education, researchers have becomemore interested in this field. Numerous studies have confirmed,both qualitatively and quantitatively, the importance of a num-ber of parameters in the adoption, management, and difficultiesencountered by teachers and students using AI in classrooms anduniversities. Scholars, researchers, and decision-makers each havea unique set of perspectives. The Soft Systems Methodology (SSM)is a structured approach to addressing socially perceived prob-lematic circumstances. It is focused on taking action. It arrangesthoughts regarding such circumstances so that remedial action canbe done. This approach is applied in a pluralistic and complicatedenvironment. Since the components in this research come from a varietyof sources, including students, teachers, institutions, and personalaspects, it can be viewed as complicated. There are numerousmethods in which any one factor can affect another. As a result,interpretative structural modeling (ISM) analysis was employed inthis study as part of SSM to categorize the components and dividethem into hierarchical tiers based on their interactions with oneanother. AI implementation is therefore a complex problem thatdepends on many variables, and there are probably many relatedvariables acting as a challenge that could be affecting its successfulimplementation. This model illustrating the relationships betweenthese crucial variables would be extremely helpful to policymak-ers and decision-makers in defining the focus areas. The goal ofthe researchers’ work is to make it easier for AI-related policiesand technology to be adopted and implemented. Furthermore, theexisting situation is significantly more accurately described by thepragmatically defined interactions between the elements than byeach aspect taken into account independently. The ISM is a usefultool in these kinds of circumstances because it makes it possibleto derive the general structure of a system from the relationshipsamong its constituent elements.

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