The Impact of AI-Based Learning Tools on Student Motivation and Academic Self-Concept

The present research examined the effects of AI-based learning tools on the academic self-concept and the motivation of students at the university. Quantitative research design was used involving the students or 250 in total who participated in the study using structured questionnaires. Pearson correlation was used in searching the relationship between AI tools usage and motivation, regression to calculate whether it is possible to predict academic self-concept using AI tools, and moderation analysis was used to find the influence of teacher support. Correlation of these results was strongly positive resulting in AI-based learning tools and student motivation with a high correlation (the more the students use AI-based learning tools the more likely they will be motivated). Regression analysis showed that the attainment of academic self-concept is strongly predicted by the use of AI tools such that, the more the AI tool was used by the student, the more his/her self-concept of academic ability is high. Furthermore, it was discovered that the AI tool impact on motivation is forcefully elevated by teacher support. These observations demonstrate that although AI tools are useful in enhancing learning attitudes and beliefs, it is not possible to realize them fully without the need of guidance and encouragement of teachers. The versatility of the study in the category of students of different ages, faculties, and years strengthens the overall generalizability of the results. On the whole, the study indicates that the use of AI in learning tools and the high level of support of teachers may help improve the learning process of students due to its personalization, interactivity, and motivating nature.