Thrush, EmilyAlmohammadi, Asaad Hamed2025-07-282025APAhttps://hdl.handle.net/20.500.14154/75988This dissertation explores how Saudi university students perceive and use generative AI tools—such as ChatGPT, Grammarly, and Quillbot—to support their English as a Foreign Language (EFL) learning. Conducted at one of the largest educational institutions in Saudi Arabia, the study employs a mixed-methods design grounded in the Technology Acceptance Model (TAM). Quantitative data from 317 students were analyzed using factor analysis and ANOVA to identify patterns across gender, academic level, and years of English study. A qualitative phase involving 17 semi-structured interviews provided deeper insights into students' experiences using AI for reading, writing, speaking, and listening tasks. Findings show generally positive attitudes toward AI, especially regarding ease of use and feedback quality, but also raise concerns about over-reliance, content accuracy, and diminished critical thinking. The study offers practical implications for curriculum designers, language instructors, and educational technology developers, particularly in non-Western, digitally evolving EFL contexts. By focusing on learner voices, it contributes to global discussions on ethical and effective AI integration in language education.As generative artificial intelligence (AI) tools become increasingly integrated into educational ecosystems, understanding how learners interact with these technologies is crucial, particularly in underrepresented English as a Foreign Language (EFL) contexts. This dissertation investigates the perceptions and usage patterns of Saudi university students as they interact with generative AI software to support their English language learning. While AI technologies like ChatGPT, Grammarly, and QuillBot are rapidly transforming academic practices, limited research has explored their impact from the learner’s perspective in non-Western, non-native English-speaking environments. This mixed-methods study, grounded in the Technology Acceptance Model (TAM), was conducted at one of the largest educational institutions in Saudi Arabia. The quantitative phase included 317 valid student responses analyzed using JASP. Principal Component Analysis confirmed the TAM-based factor structure, and repeated-measures ANOVA tested for differences across demographic variables such as gender, academic level, and years of English study. Findings indicated generally favorable perceptions of generative AI tools, with perceived ease of use rated higher than perceived usefulness. While no major gender-based differences were observed, modest distinctions emerged across academic levels and language study history. To contextualize these patterns, the qualitative phase involved 17 semi-structured interviews analyzed thematically using Quirkos. The qualitative findings provided deeper insight into how students incorporated AI tools into their learning routines across reading, writing, speaking, and listening tasks. Themes included increased learner autonomy, more personalized and immediate feedback, enhanced motivation, and flexible pacing. However, students also raised concerns about the accuracy of the tool, its insensitivity to context, the lack of critical reflection, and the risk of over-reliance on automated feedback. These nuanced findings were presented using bilingual excerpts (Arabic and English) to preserve cultural and linguistic authenticity. Together, the mixed-methods results suggest that generative AI tools offer both pedagogical opportunities and challenges. While learners recognize the value of these tools for improving task efficiency and engagement, they also navigate tensions between convenience and cognitive growth. Overuse of AI software, particularly for writing, may inhibit deeper learning and reduce confidence in independent language production. This dissertation contributes to the evolving body of research on AI in education by centering learner voices in a global context. It advances the theoretical application of TAM in EFL environments and underscores the need for culturally responsive, pedagogically guided integration of AI tools. The findings hold implications for curriculum designers, instructors, and edtech developers seeking to balance innovation with integrity in technology-enhanced language learning.172en-USGenerative Artificial Intelligence (AI)Artificial Intelligence in EducationAI-Assisted Language LearningTechnology Acceptance Model (TAM)English as a Foreign Language (EFL)Higher Education in Saudi ArabiaStudent PerceptionsLanguage Learning TechnologiesMixed Methods Research in EducationDigital Tools in English Language LearningGrammarlyQuillboAI Writing ToolsChatGPTAutonomous Learning and TechnologySecond Language Acquisition and AIEducational Technology in EFL ContextsCross-Cultural Technology IntegrationSaudi EFLThesis