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Key Takeaways

Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education This paper investigates how university stude...
The integration of GenAI tools into higher education assessment raises important questions about how students understand, interpret, and respond to AI-mediated evaluation.
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.

Paper AbstractExpand

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...

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

This paper investigates how university students perceive and interpret writing assessments when they are explicitly told that the feedback and grades were generated by an AI rather than a human instructor. As educational institutions increasingly adopt generative AI tools, this study explores the nuanced relationship students form with automated evaluation, specifically focusing on the distinction between finding feedback helpful and accepting an AI as a legitimate authority for grading.

The Study Approach

The research involved a qualitative inquiry with thirteen male undergraduate computing students at a Saudi public university. Participants completed a handwritten writing task for a technical communication course. Their submissions were scanned and evaluated by ChatGPT using a rubric-based prompt designed to align with the course objectives. After receiving the AI-generated scores and feedback, the students were informed of the source and asked to provide written reflections on the experience. The openai story also surfaces in OpenAI Unveils GPT-Red an Automated Model..., adding another angle.

Key Findings on AI Feedback

The study identified four primary themes in student reflections: the perceived usefulness of the feedback, an awareness of the AI’s pedagogical limitations, the development of conditional trust, and the importance of the human instructor’s role.
The results suggest that students view AI feedback as a useful tool for surface-level revisions, such as grammar or structure. However, they remain skeptical of the AI's ability to provide deep, contextual, or pedagogical insight. Most importantly, the participants consistently maintained that the human instructor should remain the ultimate authority for final grading decisions. The openai story also surfaces in OpenAI’s Opaque Reasoning Technique Raises Alarm..., adding another angle.

Feedback Utility vs. Evaluative Authority

A central contribution of this research is the identification of a clear divide between "feedback utility" and "evaluative authority." The study found that students do not view these two concepts as opposite ends of a single spectrum. Instead, they treat them as separate judgments. A student may find an AI's comments helpful for improving a draft (utility) while simultaneously rejecting the AI's right to assign a final grade (authority). This distinction highlights that even if AI becomes technically proficient at grading, students may still require the human element to feel that an assessment is legitimate and fair. To see openai in practice, The Ultimate Vibe Coding Guide (2026... walks through a concrete example. as detailed in the full paper on Arxiv

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