A Study on Enhancing Learners’ High Awareness Ability With Generative Artificial Intelligence based on A Response Surface Methodology

Digital literacy plays an important role in teachers’ professional competence, especially in the context of digital transformation in education. With the advancement of digital technology, enhancing learners’ high awareness ability in digital literacy has become a key step in literacy development. On this basis, this study employed the response surface methodology (RSM) to evaluate various test indicators, including the number of words, number of answer points, and number of follow-up rounds, and evaluation indicators, including accuracy, expansion, and awareness, for generative artificial intelligence (GAI). A four-step process was followed that includes basic analysis of experiment results, factor estimation and variance analysis, multifactor response surface analysis, and indicator analysis. The findings indicate that the test factors related to the GAI-generated content had a significant impact on participants’ high awareness levels. These factors exhibited interactive effects, and the response surface model revealed an optimal approach for applying GAI to enhance awareness. Building on these insights, practical suggestions are proposed for using GAI to enhance digital literacy. On the basis of the research conclusions, practical approaches to applying GAI to enhance high awareness ability in digital literacy are proposed. These approaches include employing the critical point perturbation mechanism, role-oriented mechanism, and high awareness thinking chain mechanism. This study employed GAI to investigate learners’ high awareness ability in digital literacy, providing a case for the proper and innovative use of GAI in the context of education. Additionally, it provides information on the educational effectiveness of GAI and offers valuable references for similar studies on GAI applications.