The Impact of Artificial Intelligence-Assisted Learning on Students’ Problem-Solving Skills: The Moderating Role of Learning Motivation

Abstract

This study examines the impact of artificial intelligence-assisted learning on students’ problem-solving skills, with particular emphasis on the moderating role of learning motivation. A quantitative, cross-sectional research design was adopted, with data planned to be collected from 325 undergraduate and postgraduate students enrolled in public and private universities in Pakistan. Primary data were to be gathered through a self-administered questionnaire distributed using Google Forms among students with experience using artificial intelligence tools for educational purposes. The data analysis framework included descriptive statistics, reliability analysis, Pearson correlation, simple linear regression, and moderation analysis using PROCESS Macro Model 1 in IBM SPSS Statistics. The reported analysis figures indicate a positive association between AI-assisted learning and problem-solving skills ((r = .582), (p < .01)), while regression analysis indicates that AI-assisted learning explains 33.9% of the variance in problem-solving skills ((R^2 = .339), (p < .001)). The moderation analysis further indicates a positive interaction between AI-assisted learning and learning motivation ((B = .167), (p < .001)), suggesting that learning motivation may strengthen this relationship. These findings highlight the potential importance of integrating AI tools with motivational and student-centred learning strategies in higher education. The study may contribute to educational psychology and educational technology by informing educators and institutions about the role of AI-supported learning and student motivation in developing problem-solving abilities.