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Top1. Introduction
The rapid advancements in generative artificial intelligence (GAI) are creating a transformative shift in language education, particularly by offering new horizons for Project-Based Language Learning (PjBLL). With its capacity to produce human-like text, simulate dialogue, provide personalized feedback, and generate tailored materials, GAI can significantly enhance PjBLL by fostering deeper learner engagement and addressing diverse needs within dynamic, real-world-like projects (Bahroun et al., 2023; Chan & Hu, 2023; Huang et al., 2022; Kohnke et al., 2023; Ogunleye et al., 2024; Pack & Maloney, 2023; Samala et al., 2024). PjBLL itself, grounded in constructivist principles such as Piaget's cognitive developmental theory and Vygotsky's sociocultural framework, emphasizes authentic problem-solving and critical thinking, where GAI can further amplify learner autonomy and collaborative knowledge construction by personalizing tasks and scaffolding project activities (Grubaugh, 2023; Kim & Adlof, 2024; Rahman et al., 2024; Shi et al., 2024; Zhang & Ma, 2023).
However, the integration of GAI into PjBLL is not without its complexities and potential perils. Effectively balancing the profound promise of GAI against its risks—including ethical issues like biases in AI-generated content, data privacy vulnerabilities, and threats to student-teacher trust—requires careful consideration and systematic approaches (Galaczi, 2023; Pack & Maloney, 2023). While the potential benefits are clear, there is a discernible gap in understanding the specific configurations of pedagogical strategies and contextual factors that lead to successful and responsible GAI implementation in PjBLL environments (Alshumaimeri & Alshememry, 2023; Kim & Adlof, 2024; Kim & Su, 2024; Liu & Ma, 2024). Much of the existing research has yet to adopt a holistic, configurational lens to unravel these multifaceted interactions.
To address this gap, the present study aims to explore effective pathways for integrating GAI in PjBLL within the context of English as a Foreign Language (EFL). We employ fuzzy-set Qualitative Comparative Analysis (fsQCA), a methodological approach well-suited for examining how different combinations of conditions lead to specific outcomes, moving beyond simplistic linear relationships. This study investigates how various configurations of key conditions—namely learner Autonomy, Collaborative Learning, and the nature of GAI integration, all operating within the overarching PjBL context—combine to influence learner success. A concise preview of the findings of this present study indicates that no single condition is solely responsible for optimal outcomes; rather, several distinct configurational pathways emerge. These pathways, characterized by different alignments of the aforementioned conditions, are shown to foster high EFL proficiency and robust learner engagement. Understanding these configurations offers a clearer roadmap for educators seeking to design and implement effective GAI-assisted PjBLL.