A systematic review of AI-based automated written feedback research

Abstract

AbstractIn recent years, automated written feedback (AWF) has gained popularity in language learning and teaching as a form of artificial intelligence (AI). The present study aimed at providing a comprehensive state-of-the-art review of AWF. Using Scopus as the main database, we identified 83 SSCI-indexed published articles on AWF (1993–2022). We investigated several main domains consisting of research contexts, AWF systems, feedback focus, ways of utilizing AWF, research design, foci of investigation, and results. Our results showed that although AWF was primarily studied in language and writing classes at the tertiary level, with a focus on English as the target language, the scope of AWF research has been steadily broadening to include diverse language environments and ecological settings. This heterogeneity was also demonstrated by the wide range of AWF systems employed (n = 31), ways of integrating AWF (n = 14), different types of AWF examined (n = 3), as well as varied research designs. In addition, three main foci of investigation were delineated: (1) the performance of AWF; (2) perceptions, uses, engagement with AWF, and influencing factors; and (3) the impact of AWF. We identified positive, negative, neutral, and mixed results in all three main foci of investigation. Overall, less positive results were found in validating AWF compared to results favoring the other two areas. Lastly, we grounded our findings within the argument-based validity framework and also examined the potential implications.