Exploring‍‌‍‍‌‍‌‍‍‌ the role of student engagement in mediating the impact of AI

Suleymanova Shoira

doi:https://journal.nordicuniversity.org/

Abstract

Using PLS-SEM, this paper explores the effect of artificial intelligence (AI) personalised learning on student academic results in tertiary education in Uzbekistan from the sample 66 respondents. Despite the rapid proliferation of AI tools in higher education, empirical evidence from Uzbekistan remains scarce. Findings demonstrate the positive influence of personalisation through AI on student engagement, however, the direct link from AI personalisation to learning outcomes is not significant (β = -0.146, p = 0.291). Based on mediation analysis, engagement serves as a full mediator between AI personalisation and learning outcomes. In other words, the mere presence of technology is not enough to enhance academic performance - its effectiveness is determined by the extent to which it triggers student engagement. Self-efficacy was identified as another main factor influencing learning outcomes, and ethical awareness was shown to have a statistically significant negative effect (β = -0.515, p = 0.004), which could be interpreted as the critical-awareness of AI being at odds with effective tool use in a digitally developing context. Apart from addressing a geographic blind spot, the paper also discusses its implications to educators and institutions that are implementing AI technologies in education.

Keywords

artificial intelligence

personalised learning

student engagement

self-efficacy

PLS-SEM

Uzbekistan

higher education

Nowadays AI is influencing not only which is the foundation of social progress — education — but in fact all aspects of society. Educational institutions have been gradually abandoning conventional education methods and embracing the use of advanced and AI technologies. In particular, AI plays a major role in learning by adapting the instruction to each learner’s knowledge level and by working with the students to fill up the gaps in their understanding. The use of adaptive learning and learning analytics methods exemplify this: the systems are customised to the students’ needs and obliged to merely provide learners with the needed information within a short frame of time.

References

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Rights & License

Creative Commons Attribution 4.0 International (CC BY 4.0)

This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.