AI and Learning: A Management Perspective from College Students of Kathmandu Valley, Nepal

Abstract

Background: With the quick integration of Artificial Intelligence in the global education system, it is quite essential that the impact of AI on student learning be determined. Although the studies have started in Nepal, a large gap is still present within the framework of student perceptions towards the influence of AI on learning.

Objectives: The objectives of this research were to examine the perceived effect of AI on the learning outcomes of college-going youth in the Kathmandu Valley, as well as to identify if the effect is considerably different for the chosen demographic: gender, age, and discipline.

Methods: In the current study, a quantitative, cross-sectional survey was administered to 319 college students using convenience sampling. The core instrument measured perceived impact across six dimensions (learning effectiveness, outcome quality, memory retention, motivation, anxiety, and analytical skills) using a 7-point Likert scale combined into a composite dependent variable. Data analysis was done through descriptive statistics, testing the reliability of the scales using Cronbach's alpha, and independent sample t-test and one-way ANOVA analyses to test for mean differences across demographic groups.

Results: The overall aggregate result for the composite score (4.74 out of 7) reflected a mildly positive yet neutral-leaning attitude regarding AI's influence. The reliability analysis corroborated internal consistency for the overall composite scale. More importantly, the results for ANOVA tests did not yield any statistically significant differences for mean composite scores in any of the gender (p=.882), age (p=.344), or fields of study (p=.269) groups. Although some slight shifts did occur in certain descriptive groups (notably Health & Welfare students had the highest mean value), none actually proved statistically significant.

Conclusion: Results provide strong evidence for the existence of a certain consensus among college students in Kathmandu Valley on the integrated effects of AI on learning experiences, which do not seem to be confined within the usual demographic constraints. This sends out a strong signal for emerging approaches toward the integration of AI in education to not be demographically nuanced. However, the absence of significant differences points toward factors other than demographic differences becoming significantly crucial in influencing students’ perceptions.

Novelty: Being one of the first studies to empirically investigate in the Nepalese setting beyond access and general attitudes to explore the perceived effects of AI on the multi-dimensional construct of learning outcomes, the methodical approach through the use of composite measurement and demographic analysis contributes to the local literature’s refinement.