Effects of Artificial Intelligence-Related Social Factors: Ethics and Access on Students’ Learning Behavior

Abstract

The rise of Artificial Intelligence (AI) in education has presented higher education institutions with fresh prospects for enriching the learning experiences for students. But the effectiveness of AI is contingent on several social factors that impact the interaction that students have with AI technologies. Thus, this study explored the effect of the social factors related to Artificial Intelligence, such as Access and Ethics, on students learning behaviour. Access was assessed via accessibility to AI technology, institutional support, adoption of AI, digital equity and inclusive access, and Ethics was assessed via human sensitivity and transparency. The design of the research is quantitative and the respondents of the research are 200 students of the university which are filled in a structured questionnaire. SPSS was used for statistical analysis such as descriptive statistics, Pearson correlation analysis and multiple regression analysis. According to descriptive results, there were positive perceptions for Access (M = 3.2342), Ethics (M = 3.5700) and Learning Behavior (M = 3.9940). The results of Pearson's correlation analysis showed positive correlations between Access and Learning Behavior (r = .383, p < .01) and Ethics and Learning Behavior (r = .438, p < .01). Also, regression analysis showed that both Access (p = .001, β = .234) and Ethics (p = .000, β = .334) were significant factors in students' learning behavior. Ethics was the most predictive factor. The study findings suggest that ethical issues, including transparency and human sensitivity, as well as the equal access to AI technologies and institutional support, have a positive impact on students' learning behaviour. The results underline the significance of fostering responsible use of AI and enhancing the availability of AI tools in the context of higher education.