Towards Technology-Enhanced English Learning: Gender Analysis of AI Large Language Models (LLMs) Usage
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Abstract
Despite the growing integration of Artificial Intelligence (AI) Large Language Models (LLMs) in education, research exploring gender-specific perceptions and usage remains scarce. This study employed the Unified Theory of Acceptance and Use of Technology (UTAUT) and a mixed-methods sequential explanatory design to investigate the perceptions and usage of ChatGPT among 41 female and 41 male university students for English learning. Data were collected using a technology acceptance scale and structured written interviews and analyzed through descriptive and inferential statistics (t-tests, bivariate correlations, and one-way ANOVA) for quantitative data, and thematic analysis for qualitative insights. Findings indicate no significant gender differences in the overall acceptance of ChatGPT. However, gender-specific trends were observed: males predominantly valued ChatGPT for specific academic tasks, whereas females appreciated its general enhancement of their learning experience. English proficiency levels did not notably affect perceptions of ChatGPT's utility, suggesting consistent recognition of its benefits across varying proficiency levels, thereby refuting previous assumptions about proficiency impacting technology adoption. This study highlights the roles that gender and proficiency level play in shaping the educational use of AI language models.
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References
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