Artificial Intelligence in Learning: Assessing Perception, Motivation, and Academic Performance

Abstract

The advancement of artificial intelligence (AI) has become an essential platform for educational institutions, learners, and countless stakeholders.  The use of AI enhances learning and improves academic achievement. Thus, the purpose of this study is to investigate the impact of learners’ AI perception and AI motivation on the academic performance of learners using empirical data from Kathmandu, Nepal.

This study used descriptive, causal-relational research with a deductive approach to reflect the general characteristics of respondents, assess the association between variables, and investigate the influence of predictors on response variables while testing the formulated research hypotheses. Further, this study used primary sources of data gathered from undergraduate students.  In addition, cross-sectional data were collected for analysis.  The convenience sampling strategy was used in this study. 

A structured questionnaire of 319 was delivered to the intended respondents, and only 182 (57.05%) were usable. The study employed statistical tools, including Cronbach’s alpha for reliability assessment, descriptive statistics such as frequency, percentage, mean, and standard deviation to characterise respondents' background information, correlation analysis to explore relationships between variables, and regression analysis to evaluate the impact of predictors on the outcome variable.

The study results indicated a considerable beneficial influence of learners’ AI perception on academic achievement, suggesting that a good learner impression of artificial intelligence use enhances academic success. Similarly, a favourable and substantial impact of AI motivation on academic performance was identified, indicating that an encouraging perspective of learners towards adopting artificial intelligence improves their educational outcomes.

The results of this study may benefit academic institutions, policymakers, and other stakeholders in tailoring teaching and learning methods to provide appropriate learning platforms in the contemporary day. Further Research in the future might be conducted in a variety of geographical areas, to include more aspects that influence academic achievement and artificial intelligence.