Abstract
This chapter serves as a comprehensive guide for conducting systematic reviews (SRs) in applied linguistics. SRs play a crucial role in this field, allowing researchers to analyze and synthesize existing research, facilitating evidence-based decisions and advancements. The chapter provides detailed guidance on conducting an SR, including formulating clear research questions, developing effective search strategies, screening and selecting studies based on predefined criteria, extracting and synthesizing data, appraising the quality of included studies, and reporting findings clearly. A checklist tailored for appraising studies within applied linguistics ensures comprehensive evaluation of quality and relevance. Additionally, the chapter explores using generative artificial intelligence (GenAI) tools to streamline and enhance the SR process, discussing applications, opportunities, and challenges. In summary, this chapter serves as a comprehensive roadmap, equipping researchers with the knowledge and tools to conduct rigorous and innovative SRs in applied linguistics.
TopTerminologies And Categorization Of Literature Reviews
Before delving into SRs and their applications, it is essential to establish a solid understanding of the related terminologies and categorizations, which serve to position SRs within the broader spectrum of literature reviews.
Key Terms in this Chapter
Systematic Review: A meticulous and transparent research method that involves identifying, evaluating, and synthesizing all relevant studies on a specific question, minimizing bias to provide a comprehensive picture of the existing evidence.
English as a Foreign Language (EFL): The use of English by individuals for whom it’s not their native tongue.
Risk of Bias: The inherent threat in any research study that its design, conduct, analysis, or reporting could systematically skew the results in a particular direction, leading to misleading conclusions.
Generative Artificial Intelligence (GenAI): A cutting-edge field of AI where machines learn from vast amounts of data to create entirely new and original content, potentially assisting with tasks like literature search and summarizing findings in systematic reviews.
Quality Appraisal: The process of dissecting research studies to judge their methodological rigor, trustworthiness, and potential for bias, ensuring the findings are reliable and contribute meaningfully to the body of knowledge.