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The Impact of AI-Suggested Content and Resources on Student Curiosity and Explorative Learning

Year: 2024

Type: article

Abstract: As educational landscapes evolve, the potential of AI to fuel curiosity and explorative learning among students has sparked growing interest. This study explores how AI-suggested content, student motivation, and Complexity of AI-suggested content drive curiosity and proactive learning behaviours in students. Through exploratory and confirmatory analysis using SPSS and AMOS, it is revealed that AI-suggested content and resources (ACR) and student motivation level (SML) significantly elevate curiosity and engagement. In contrast, certain combinations, such as high content resources and Complexity of AI-suggested content, may unexpectedly hinder exploration. Notably, demographic factors like age, gender, and education showed no significant impact, underscoring the universal potential of AI in personalised learning. These findings highlight the value of tailoring AI resources and fostering motivation to cultivate curiosity, offering a roadmap for educators and developers aiming to unlock the full potential of AI in education.

Source: Journal of Artificial Intelligence Machine Learning and Neural Network

DOI: 10.55529/jaimlnn.51.1.13

Author: Michael Gyan Darling

Institution: University of Skills Training and Entrepreneurial Development

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4405137873

FWCI:

Cites: 13

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Related to: 10