What Is Automated Writing Feedback and Scoring?

Automated feedback on writing has moved beyond just spellcheck. Some platforms now offer AI‑driven “writing coaches” that can highlight issues with clarity, tone, structure, and argument, and some can go further by assigning holistic scores intended to mimic human markers. These systems can be used in school platforms, university writing centres, language exams, and commercial tools marketed directly to students and teachers.​

Most rely on large language models or machine‑learning algorithms trained on thousands (sometimes millions) of previously marked scripts. The model looks for patterns: how sentence length, vocabulary variety, paragraphing, and certain discourse markers correlate with higher or lower grades. Some tools can be fine‑tuned on local rubrics, so they can give task‑specific feedback like “you need a clearer thesis” or “you’re not addressing counter‑arguments”. Others focus on micro‑level feedback: suggesting alternative wordings, flagging repetition, or identifying sentences that may need dividing.​

From a student’s perspective, the appeal is immediate: instant feedback, no waiting days for a busy teacher; the chance to revise multiple times without over‑using a human marker’s time; and a sense of control over improvement. For institutions, automated scoring promises consistency across large cohorts and potentially lower marking costs for standardised tasks. But beneath this promise lies a tension we need to grapple with between fluency and depth, between what is easy for algorithms to measure and what actually matters in complex writing and the back and forth processing of editing for formulating and improving critical thought..​

Where AI Feedback Helps – and Where It Gets Dangerous

Used well, AI writing feedback can be a powerful practice tool. It can:

• Help students notice recurring sentence‑level issues (run‑ons, vague pronouns, overuse of filler words) which human markers may not have time to catalogue each example.​
• Encourage iterative drafting: students can experiment with reorganising a paragraph or sharpening topic sentences and immediately see whether the comments shift.​
• Support multilingual writers by catching basic grammatical slips and suggesting more natural phrasing, freeing teachers to focus on argument and structure.​

The problems arise when automated scores are treated as authoritative judgements rather than rough indicators. Studies have shown that some scoring engines can be “gamed” by formulaic, verbose writing that looks sophisticated but says little; others may penalise unconventional structures, creative risks, or certain dialect features, effectively rewarding conformity over voice. Importantly, there are also equity concerns: if models are trained mostly on essays from certain linguistic or cultural backgrounds, students who write differently – including many neurodivergent and multilingual learners – may be misjudged.​

For high‑stakes decisions (grades which affect progression, scholarships, or visas), over‑reliance on automated scores can create unfairness. Even when humans “double‑mark” with AI, there can be subtle pressure to align with the machine, especially under time constraints. When students internalise the system’s preferences, they may begin to write to the algorithm – padding word counts, avoiding complex ideas, or smoothing out their own voice to please a statistical model.​

A healthier stance is to position AI feedback as a mirror, not a judge. It can show patterns worth reflecting on, but it cannot decide what good writing should be in a given context, for a given audience, at a given moment in a learner’s development. That work belongs to humans – ideally in dialogue with the students learning it.

Note: the first draft of this article was done by AI Chatbot Claude with the support of Max Capacity. The text was then edited and adapted by Jaye Sergeant of Turtle & elephant, who is responsible for the published version.