Perceived Artificial Intelligence Support and Reliance in Instructional Media Generation: A Survey of Malaysian ESL Teachers
Keywords:
artificial intelligence; English as a Second Language; instructional media generation; perceived support; teacher reliance; teaching experienceAbstract
Artificial intelligence (AI) is increasingly used to generate instructional media, yet how perceived support is associated with reliance remains underexplored in English as a Second Language (ESL) teaching. This study examined ESL teachers’ perceived AI support and self-reported AI reliance in instructional media generation and tested whether perceived support and teaching experience predicted reliance. A quantitative cross-sectional survey used purposive sampling to recruit 111 Malaysian ESL teachers who had used AI for instructional purposes. The two five-item scales demonstrated strong internal consistency (AI support, ? = .902; AI reliance, ? = .923). Exploratory factor analysis supported their empirical distinctiveness (KMO = .876; Bartlett’s test, p < .001). Teachers reported high perceived AI support (M = 4.178, SD = .497) but moderate reliance frequency (M = 2.822, SD = .840). Reliance was strongest for creating visually engaging or customised media and weakest for using AI-generated frameworks to structure instructional content, suggesting greater delegation at the production layer than at the pedagogical design layer. Hierarchical regression showed that perceived AI support positively predicted reliance (? = .287, p = .002), whereas teaching experience negatively predicted it (? = ?.274, p = .003). The final model explained 19.7% of the variance. The findings position AI reliance as task-specific and suggest that perceived support may encourage its routine use within instructional preparation. Professional development should strengthen teachers’ capacity to evaluate, adapt, and retain pedagogical control over AI-generated materials.
https://doi.org/10.26803/ijlter.25.7.28
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