Despite the current widespread use of Automated Writing Evaluation (AWE) feedback, many issues regarding its efficacy still remain unresolved. Recent studies mainly focus on correctly detected errors with a lack of attention on the comprehensiveness of error detection, or error coverage. Error coverage is interesting because little is known about the capacity of AWE systems to fully detect common second language (L2) errors. It is also important to investigate the potential effect of such capacity on student uptake and retention, which are important constructs in fostering L2 writing development. To this end, the present study compared teacher feedback and AWE error coverage in L2 writing classes. The findings suggest that both the AWE system and the teacher demonstrated low error coverage across grammar, usage, and mechanics error categories. However, they indicated differences in the types of errors they identified most frequently. The AWE system flagged more mechanical errors, whereas the teacher provided twice as many corrections for grammar errors, including wrong/missing words, prepositions, and incorrect word forms. While the AWE system performed moderately in flagging articles and comma errors, it struggled with more nuanced grammatical errors, suggesting it may not be a reliable standalone tool for addressing specific needs of L2 learners’ writing challenges. Interestingly, coverage was positively associated with successful uptake, with students utilizing a wider variety of revision acts (i.e., change, add, delete, remove) on AWE errors identified compared to errors not identified. However, error coverage did not correlate with short- or long-term retention of accuracy, implying that retention may result from the interplay of error coverage with other factors. Findings provide implications for writing teachers regarding the employment of AWE systems and for AWE developers regarding the future optimizations of the AWE systems.

This paper explores how Chinese ESL students utilize Generative AI (Gen AI) in their writing processes, focusing on the stages of writing, specific methods of use, and language practices. The study was carried out in an English-medium instruction (EMI) environment at a Sino-American university in China. Data were collected through online surveys with 157 participants and follow-up interviews with nine students. Quantitative analysis of the survey data uncovered general patterns, while analysis of the interview data, using Polio & Freedman’s (2017) coding method to identify themes, provided detailed illustrations. Subsequent analysis indicates that students primarily engage with Gen AI in the early stages of writing for brainstorming to overcome initial hurdles. Information retrieval happens frequently throughout the writing process. However, ethical concerns and academic integrity issues discourage students from directly incorporating AI-generated text into their drafts. Regarding language use, Chinese ESL students make flexible language choices based on task goals, content relevance, and the perceived cultural appropriateness of AI-generated content. Though limited, this paper expounds on how Gen AI is flexibly utilized in ESL writing and aims to inspire future academic research in this emerging area. The assistive role of Gen AI in ESL writing is emphasized, and strategies for its responsible and critical use are proposed.

The study explored six ESL university students’ behavioral, cognitive, and affective engagement with e-rater feedback on local issues and examined any changes in students’ engagement over two weeks. We explored behavioral engagement through the analysis of screencasts of students’ e-rater usage and writing assignments. We measured cognitive and affective engagement by analyzing students’ comments during the think-aloud protocol and reflection surveys. The findings indicated that the students had varying levels of engagement with the feedback. Behaviorally, all students used a range of revision operations to address errors based on the provided feedback. Cognitively, some students were more engaged than others. Affectively, students experienced both positive and negative reactions toward e-rater feedback. While some students’ engagement with feedback did not change over two weeks, others’ engagement grew more negative. We conclude that e-rater feedback could positively impact students’ accuracy in local aspects of writing if students are actively engaged with the feedback.

In writing studies research, automated writing evaluation technology is typically examined for a specific, often narrow purpose: to evaluate a particular writing improvement measure, to mine data for changes in writing performance, or to demonstrate the effectiveness of a single technology and accompanying validity arguments. This article adopts a broader perspective and offers a standpoint theory of action for formative automated writing evaluation (fAWE). Following presentation of the features of our standpoint theory of action, we describe our two study sites, and each instructor documents her experiences using the fAWE application (app), Writing Mentor® (WM). One instructor analyzes experiences using the app with nontraditional adult learners to provide career pathway access through a high school equivalency (HSE) credential awarded by successful completion of the GED® (General Educational Development Test) or of the HiSET® (High School Equivalency Test). A second instructor analyzes WM experiences working with a diverse population of two-year college students enrolled in first-year writing. These instructors’ experiences are used to propose two theory-of-action frameworks based on the instructors’ standpoints, with particular attention to fAWE components, pedagogies, and consequences. To explore the representativeness of these two case studies, we also analyze student feature use and self-reported self-efficacy data from a general sample (N = 5,595) collected through WM user engagement. We conclude by emphasizing the pedagogical potential of writing technologies, the advantages of instructionally situating these technologies, and the value of using standpoint theories of action as a way to anticipate local impact.