Student Evaluation of Repeated AI Feedback Across a Semester of Writing

When AI-Mediated Feedback Writing Works and When It Does Not: Lessons Learned from Two Classroom Cases

The COM Essay Assessor

The development and calibration of the COM Essay Assessor is presented, a rubric-based generative artificial intelligence (GenAI) tool designed to support formative feedback while retaining instructor oversight and reflects on the opportunities and challenges of integrating GenAI into large writing programs.

Teacher Strategies for Reducing AI Generated Content in English Major Student Writing

Generative AI can produce fluent prose before students have worked through the linguistic choices that writing courses are intended to teach. This study examined how one university instructor responded to that problem in a second-year English-major writing course. Forty-two students (18 men and 24 women; mean age = 20.3 years) participated in a 16-week concurrent embedded mixed-methods study. The instructor compared AI-probability estimates from GPTZero, Originality.AI, and Turnitin, returned passage-level feedback, and taught four sessions on making context-sensitive lexical choices without asking AI to rewrite students' work. Questionnaires, interviews with six students, three sets of writing samples, and course scores supplied the data. Across Weeks 5, 10, and 15, the mean estimated share of AI-generated text fell from 51.2% to 35.8% and then to 23.6%. Overall AI-tool use also declined, while mean writing scores rose from 72.4 to 85.9. Students most often associated AI use with difficulty expressing ideas accurately, limited time, and pressure to obtain higher grades. By the end of the semester, more students distinguished grammar assistance from having AI compose a paper. Because the study involved one class and no control group, these changes should be read as associations rather than proof of a causal effect. Even so, the findings illustrate how transparent checking, individualized revision, and lexical instruction can be combined in a classroom response to AI-dependent writing.

Affordances and Constraints of AI-Generated Writing Feedback

The findings suggest that AI-generated feedback supported targeted revision when it is accessible, interpretable, and aligned with classroom assessment criteria.

Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

The integration of GenAI tools into higher education assessment raises important questions about how students understand, interpret, and respond to AI-mediated evaluation. As instructors increasingly explore AI tools for providing feedback, prior research has examined whether GenAI-generated feedback improves writing performance and how students perceive its usefulness; comparatively little is known, however, about how students interpret such evaluation when they are explicitly informed that an AI system, rather than a human instructor, produced the feedback and the score. This study reports findings from a qualitative pedagogical inquiry conducted in an undergraduate technical communication course for computing students at a Saudi public university. Thirteen male undergraduate computing students completed an in-class handwritten writing task; the scanned submissions were evaluated by ChatGPT using a rubric-based prompt aligned with the task objectives. Students were then explicitly informed that ChatGPT had generated the score and feedback and were invited to reflect on the evaluation in writing. Inductive thematic analysis of these reflections identified four themes: perceived usefulness of feedback; awareness of AI's contextual and pedagogical limitations; conditional trust, distinguishing feedback utility from evaluative authority; and reflection on the institutional and pedagogical role of the human instructor. Participants accepted GenAI feedback as useful for surface-level revision but consistently positioned the human instructor as the appropriate authority over grading decisions. The study identifies this as a distinction between feedback utility and evaluative authority, two judgments that students treat as analytically separate rather than as opposite ends of a single approval scale...

EFL Students’ Perceptions on AI-Generated Feedback in Academic Writing

The increasing use of Artificial Intelligence (AI) writing tools has changed the way English as a Foreign Language (EFL) students seek feedback on their academic writing, particularly during the demanding process of preparing a research proposal or thesis. This study aims to explore how final-semester EFL students perceive, evaluate, and respond to AI-generated feedback while writing their proposal or thesis. Using a descriptive qualitative design, this study employed a Likert-scale screening questionnaire distributed to 24 respondents, followed by semi-structured interviews with six key informants selected through purposive sampling. The findings of the questionnaire indicate that most participants perceived AI-generated feedback favorably for surface-level writing issues, with 87.5% agreeing that it helped them identify possible errors before consulting their supervisor, while only 62.5% reported cross-checking content-level AI suggestions before applying them. From a qualitative perspective, thematic analysis organized these perceptions into four components: cognitive (understanding of how AI feedback is produced and how it differs from supervisor feedback), affective (confidence balanced with doubt about accuracy), conative (direct application of surface-level suggestions versus cross-checking of content-level suggestions), and external/internal cues (trust shaped by feedback consistency and by students’ own digital literacy). When examined through Rosenberg and Hovland’s Tripartite Attitude Model, Robbins and Judge’s theory of perception, and the concept of AI Literacy, the findings show that students who engaged more critically and selectively with AI-generated feedback demonstrated higher AI literacy, while those who applied suggestions uncritically demonstrated lower AI literacy regardless of the amount of AI feedback they received. This study offers practical recommendations for students and lecturers on the wise and critical use of AI-generated feedback in the proposal and thesis writing process.