Effects of Artificial Intelligence-Related Factors on Students’ Learning Behavior: The Role of AI Bias, Cultural/Social Factors and Technology Acceptance

Abstract

Artificial Intelligence (AI) is a crucial education technology that impacts students' learning experiences and outcomes. But there are a number of factors related to AI that can impact students' learning behavior, such as Bias in AI, Subjective Norms, Perceived Ease of Use, and Perceived Usefulness. Hence, in this study, the impact of these factors on students' learning behavior was studied. The quantitative research design was used, and the data were gathered from 200 university students using a structured questionnaire. SPSS was used for statistical analysis which included descriptive statistics, Pearson correlation and multiple regression analysis. Descriptive statistics showed that the mean scores of Bias: M = 3.6375, Subjective Norms: M = 3.5050, Perceived Ease of Use: M = 4.0483, Perceived Usefulness: M = 4.1250, and Learning Behavior: M = 3.9940 were all positive. The results of the correlation analysis indicated that all the independent variables were positively correlated with Learning Behavior variable at significant level. Perceived Usefulness (β = .437, p = .000) mostly predicted Learning Behavior, followed by Bias (β = .270, p = .000) and Perceived Ease of Use (β = .167, p = .003). Subjective Norms had no significant impact on Learning Behavior (β = .043, p = .447). The results indicate that positive learning behavior is more likely to be seen when students feel usefulness, ease of use, and fairness of AI technologies. The study highlights the need to improve the usefulness and usability of AI systems and decrease bias in educational technologies.