Machine Translation Literacy and Intention Predict Perceived Learning Performance in Students

University students who report higher machine translation literacy alongside a stronger intention to use machine translation tools also report more favorable perceived learning performance, according to an investigation published in Current Psychology. The findings demonstrate clear positive statistical associations between students’ technology-related capabilities and their subjective evaluations of language learning success, providing fresh insight into how digital translation aids interact with higher education.

Machine translation tools have become increasingly embedded across the daily language learning activities of university students, yet the specific mechanisms connecting technology usage to students’ perceived learning performance have remained insufficiently understood. To explore these pathways, researchers Siyu Zou and Yanjun Yang examined how underlying technology-related beliefs, machine translation literacy, and machine translation intention operate together to shape subjective learning outcomes.

The investigation framed technology-supported learning by integrating two established conceptual models: the Unified Theory of Acceptance and Use of Technology, known as UTAUT, and Conservation of Resources theory, known as COR. By combining these frameworks, the authors conceptualized machine translation literacy as a resource-based capability that allows learners to effectively evaluate, regulate, and strategically apply machine-translated outputs within academic settings.

To gather empirical evidence, the authors administered a cross-sectional survey to 973 university students recruited from three universities located in Jiangxi Province, China. The resulting dataset relied entirely on self-reported survey responses to assess the participants’ core technology beliefs, digital literacy levels, translation intentions, and perceived learning performance.

The researchers evaluated the survey responses through three distinct analytical approaches: partial least squares structural equation modeling (PLS-SEM), importance–performance map analysis (IPMA), and artificial neural network (ANN) analysis. Applying these complementary methods allowed the team to systematically test structural relationships while evaluating the relative importance and performance of the predictive factors.

The structural equation modeling results revealed that both machine translation literacy and machine translation intention had positive associations with perceived learning performance. In examining the antecedents of literacy, performance expectancy, effort expectancy, social influence, and facilitating conditions all displayed positive associations with machine translation literacy. When predicting machine translation intention, performance expectancy, social influence, facilitating conditions, and machine translation literacy were each positively associated with intention, whereas effort expectancy demonstrated no statistically significant association.

Further evaluation using importance–performance map analysis identified machine translation intention as a relative priority for improvement, whereas machine translation literacy was identified as an established strength to maintain. Additionally, the artificial neural network analysis determined that machine translation literacy was the most salient predictor of students’ perceived learning performance, with machine translation intention ranking as the second most influential predictor.

Collectively, the results suggest that learners who exhibit a greater willingness to utilize machine translation and a stronger capacity to evaluate, regulate, and strategically apply translated outputs tend to perceive better learning performance. However, the authors noted that these findings reflect self-reported perceptions from a cross-sectional sample and do not establish verified improvements in objective academic achievement.

Subject of Research: Machine translation literacy, usage intention, and perceived learning performance among university students

Article Title: Machine translation literacy and perceived learning performance among university students: evidence from a UTAUT–COR framework

Article References: Zou, S., & Yang, Y. (2026). Machine translation literacy and perceived learning performance among university students: evidence from a UTAUT–COR framework. Current Psychology, 45(22), Article 1770. https://doi.org/10.1007/s12144-026-10288-1

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10288-1

Keywords: machine translation, machine translation literacy, machine translation intention, perceived learning performance, UTAUT, Conservation of Resources theory, performance expectancy, effort expectancy, social influence, facilitating conditions, partial least squares structural equation modeling, importance-performance map analysis, artificial neural network analysis, cross-sectional survey, university students, Jiangxi Province, technology-supported learning, resource-based capability