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The present study adopted the structural equation modeling approach to examine Chinese university students’ metacognition, critical thinking skills, and academic writing. In particular, this research explored whether awareness in metacognition can foster critical thinking and, thus, lead to enhancement in academic writing. The measure for exploring metacognitive writing strategies covered metacognitive knowledge and regulation in academic writing. The measure for understanding learners’ critical thinking encompassed the following five skills: inference, recognition of assumptions, deduction, interpretations, and evaluation of arguments. The academic writing assessment was based on an internal test. The participants consisted of 644 third-year students from a Chinese university. Three models tested: (1) the role of metacognition in academic writing; (2) the role of metacognition in critical thinking; and (3) correlations between metacognition, critical thinking skills, and academic writing. The results indicated significant relationships between the three variables, and the implications based on these findings were discussed.

In the present study, we developed a chatbot that helps teachers to deliver writing instructions. By working with the chatbot, the post-secondary writers developed a thesis statement for their argumentative essay outlines, and the chatbot helped the writers to refine their peer review feedback. We conducted a preliminary analysis of the effect of a chatbot on these writers' writing achievement. We also collected several student testimonials about their chatbot experiences. Several important pedagogical and research implications for chatbot-guided writing instructions and the use of learning technology have been addressed.

Researches around the world have examined how electronic feedback (e-feedback) assists English (ESL) language learners improve their writing by minimizing errors. This current study examines the use of online written peer feedback (OWPF) and automated written corrective feedback (AWCF), analyzing morphological and syntactical errors in writing. The study used a quantitative method to analyze the data. The study participants included one hundred forty-one first-year senior students. They were divided into three groups: two experimental groups received treatment through OWPF and AWCF, and one control group received through the communicative language teaching approach. Regarding writing improvement and minimizing morphological and syntactic errors, the data show that OWPF is significantly more helpful than AWCF and CLT in maximizing writing effectiveness. The study explores and highlights the implications of the study’s results for pedagogical training and future research.

The new version of ACM and IEEE-CS Computer Science Curricula envisages the preparation of a dozen white papers within more than a third of the Body of Knowledge courses. Lack of experience in conducting research, insufficient ability to articulate thoughts, non-observance of defined recommendations, as well as forgotten verification of what has been done are key challenges in the implementation of these recommendations. Large language models (LLMs) can significantly support the creation of anticipated white papers, encouraging initial directions and inspiration for research, as well as a focused presentation of key elements of the work. Unfortunately, they can also hinder the creation of expected academic texts, promoting the superficiality of research and the presentation of unverified information. In order to facilitate progress in acquiring the necessary writing skills without discriminating against those students who have already acquired them and prefer traditional writing, we have defined a series of strict rules and strategies for preparing the briefing reports with complete, partial, or no reliance on the LLM. They were embedded into the faculty learning management system, implemented, and evaluated twice with more than 150 students during the academic year 2023/24. This paper presents a new approach along with the lessons learned from the first attempt and the changes incorporated into the second. Based on these two carefully designed, conducted and analyzed studies, teachers and students gained a very positive opinion about the impact of LLM on the creation of quality student reports. The effectiveness of the new approach is a guarantee that optimization of the integration of AI-generated content can become a benchmark for the successful preparation of student reports that can be recommended to the wider academic community.

Strategies instruction has improved the writing of high school struggling writers in previous studies, including students with disabilities. This study examined the effectiveness of argumentative writing instruction for the ACT writing exam using the Self-Regulated Strategy Development (SRSD) model with high school students with disabilities or at-risk for writing difficulties. Using a multiple baseline across participants design, four 10th grade students (three males, one female) were taught to analyze ACT prompts, plan, and write an argumentative essay using the SRSD model. Following instruction, students increased quality of their plans, number of argumentative elements, overall ACT essay score, number of words, and number of transition words in their ACT essays. Students were positive about the strategy, learning process, and its effects.

Composing narrative text is complex, requiring development of knowledge of genre specific and general writing knowledge, and the ability to sequence and convey real or imagined experiences, reactions, and an effective ending. Teachers, however, typically do not receive adequate professional learning needed for effective instruction in narrative writing. We collaborated with district-level stakeholders to address the specific writing needs of their 4th grade students and developed two narrative writing strategies aligned with state curriculum and expectations. We also developed two days of practice-based professional development (PBPD) for teachers who implemented Self-Regulated Strategy Development (SRSD) instruction. The PBPD included the genre-based and general writing knowledge needed to use these new strategies. SRSD instruction also included learning to write in a timed condition, as required in the state writing test. Expert support was provided during SRSD instruction. Instruction took place during the teachers’ English language arts block a minimum of 3 times per week. This randomized controlled trial involved 16 teachers and their 4th grade classrooms randomly assigned to the SRSD instruction or control conditions. PBPD resulted in high instructional fidelity for 14 features of SRSD instruction and high social validity for SRSD instruction. Hierarchical linear modeling indicated that student outcomes were moderate to large for narrative elements ( g  = 0.51) and story quality ( g  = 0.48), and small for total words written ( g  = 0.35). This study adds to research on PBPD for SRSD and challenges experienced, and to effective instruction in narrative writing. We discuss issues and future research that can make a difference to teachers, students, and schools.

With dialogic writing assessment, teachers can scaffold students’ writing processes in ways that are flexible and responsive to students’ individual needs. Examples of teachers using this conference‐based method of classroom writing assessment illustrate how to practice assessment that is dynamic and relational rather than static and standardized, by allowing teachers to vary their support for student writers based on students’ unique needs. These examples also suggest that teachers’ epistemologies for writing instruction can influence how they practice dialogic writing assessment. The authors conclude with a discussion of how dynamic and responsive scaffolding can support an equity‐focused model for teaching academic writing and how teachers’ expertise may be a factor in how they apply dialogic writing assessment.

This article presents a constructivist writing placement framework, developed from the study of two pilot iterations of a local writing placement mechanism at a large public research university. Through preliminary analysis of data from these pilots, we present a model of constructivist writing placement and demonstrate how it helps move conceptualizations of student agency as primarily housed within student exercise of choice toward more robust understandings and facilitation of student agency via placement. Extending recent calls to reconsider methodological traditions like directed self-placement to more explicitly account for educational equity issues, our two pilot assessments illustrate how we might reposition student agency within writing placement as emergent from situational interactions with faculty and the institutions they represent, rather than merely authorized by them.

Considering the universality of writing struggles students and teachers face, it is an unexpected observation to find that there are most likely only a dozen systematic reviews on the topic. To this date, no reviews have focused on identifying and discussing English writing instructional models educators use. This paper systematically reviewed original research papers on English writing instructional models published from 2020 to 2024. Using tools such as Publish and Perish, R Studio and NVivo to review the final count of 31 studies, this study identified 24 instructional models for teaching English writing, 8 of them having been newly developed and proposed in the last five years. The names of every identified instruction or teaching model are organised based on seven commonalities that several models share: template-based, genre-specific, discussion-based, AI-based, feedback-based, insight-based, collaboration-based, and focused on lesson plans. A novelty of this systematic review is that it focused on discussing the writing instructional models and presented them with themes that make them easily comparable and easy to use for teachers aiming to change and improve how they teach English writing.

This article investigates the impact of ChatGPT on student writing in the context of Model United Nations position papers, which require extensive research and concise articulation of policies on international issues. By comparing student-written papers and ChatGPT-generated papers, we find that whereas AI-generated papers received higher evaluations overall, award-winning student papers outperformed AI-generated papers. Furthermore, the quality of AI-generated writing is shown to depend on human input, emphasizing the importance of a human-centered approach to AI in education.