Student Perceptions of Artificial Intelligence and Metaverse-Enabled Remote Learning in New Zealand Tertiary Education

Abstract

The increasing development of emerging digital technologies is creating new possibilities for remote tertiary education. Artificial Intelligence and Metaverse technologies offer potential for personalised learning, intelligent support, immersive interaction, and collaboration; however, their educational value depends partly on how students perceive their usefulness, accessibility, supporting conditions, and motivational potential. This study examined New Zealand tertiary students’ perceptions of prospective Artificial Intelligence- and Metaverse-enabled remote learning environments and investigated whether selected technology-acceptance and motivational factors were associated with perceived learning engagement. A mono-method quantitative, cross-sectional survey design was adopted. Data were collected through an online questionnaire comprising 20 Likert-scale items representing six constructs: performance expectancy, facilitating conditions, motivation, perceived ease of use, perceived usefulness, and perceived learning engagement. A total of 356 responses were retained for analysis. Descriptive statistics, reliability analysis, Pearson correlation, exploratory chi-square analysis, and multiple linear regression were conducted. The regression model was statistically significant and explained 33.8% of the variance in perceived learning engagement (R² = .338; adjusted R² = .329; F = 35.773, p < .001). Perceived usefulness (β = .279, p < .001), motivation (β = .218, p < .001), and facilitating conditions (β = .179, p < .001) were statistically significant predictors of perceived learning engagement. Performance expectancy (β = .065, p = .223) and perceived ease of use (β = .089, p = .058) were not statistically significant predictors. An exploratory chi-square analysis also identified a statistically significant association between age and perceptions of virtual teamwork (p = .001). The findings provide preliminary evidence that perceived usefulness, motivation, and facilitating conditions are important considerations in understanding student engagement with prospective advanced remote learning environments. Given the cross-sectional design and purposive non-probability sampling, the findings indicate statistical associations rather than causal effects and should be interpreted within the study sample.