From process to product: writing engagement and performance of EFL learners under computer-generated feedback instruction.

Artificial intelligence (AI) technology is gradually penetrating the domain of education, opening up many possibilities for teaching and learning. Many educators, faced with the burden of commenting on substantial student essays, have introduced automated writing evaluation (AWE) into second language (L2) writing considering its affordance of immediate scores and diagnostic information. However, students' processing strategies and perceptions of such computer-generated feedback and its impact on student writing quality, particularly as mediated by language proficiency, remain under-explored. This study examines the impact of Pigai, a Chinese AWE system, on revision processes and writing products of 42 English as a foreign language (EFL) learners with varying language levels by analyzing feedback points, feedback uptake, text quality in complexity, accuracy, and fluency (CAF), and perceptions. The findings are as follows. When confronted with AWE instruction, the majority of student work focuses on correcting errors, but higher-level students exhibit an emphasis on language improvement beyond the surface level compared to lower-level students. According to CAF measures, automated feedback exerts greater effects on accuracy for unskilled learners and lexical complexity for skilled learners in the development of interlanguage. Learner profiles and perceptions of students at different levels are explored along four dimensions: writing quality, cognitive engagement, behavioral engagement, and affective engagement. Finally, the potential issues of such technology-based writing instruction are pointed out.

A corpus-based comparison of syntactic complexity in academic writing of L1 and L2 English students across years and disciplines.

Syntactic complexity, widely acknowledged as a key predictor of writing quality, has gained increasing attention in the realm of academic writing. A notable line of inquiry has centered on the factors that potentially influence syntactic complexity in academic writing. Instead of attending to one factor, the study focuses on multiple factors to examine how language background affects syntactic complexity across years and disciplines. Specifically, it compares the syntactic complexity in English academic writing between L1 English and L1 Chinese university students across four years and/or three disciplines. Fine-grained indices, which include five clausal indices and eight phrasal indices, are utilized to capture specific syntactic features for a full understanding and description of the syntactic preferences shown by L1 English and L1 Chinese students in academic discourse. The results revealed that L1 English students tended to produce more noun phrases in academic writing than L1 Chinese students with the increase of years. Additionally, both cohorts of students followed similar syntactic patterns in cross-disciplinary contexts, with highly frequent use of complex nominals in the Physical Sciences and clausal structures in the Social Sciences. In instances where language background, year, and discipline interact, texts produced by Chinese English learners exhibited reliance on the syntactic features of adverbial clauses, attributive adjectives, and pre-modifying nouns across all disciplines in the early years of their academic journey. Ultimately, some pedagogical implications for academic writing courses are put forward in an attempt to provide valuable insights for enhancing academic literacy among both L1 and L2 students.