Artificial Intelligence Applications in College Academic Writing and Composition: A Systematic Review
Bibliographic record
Abstract
Proofreading and editing are essential to enhance the quality of texts. While literature is abundant on technological tools for identifying semantic and lexical-grammatical errors, evidence on the actual effectiveness of artificial intelligence (ai) in this process remains limited, with studies varying in scope and rigor. This study examines whether existing evidence supports or contradicts the hypothesis that a ai-based applications help edit and proofread texts in higher education. A review was conducted in Scopus and Web of Science databases, covering scientific articles in English and Spanish, published between 2019 and 2024, related to university writing and the use of ai in text correction. Most studies were exploratory and descriptive. A notable increase in publications related to ai and academic writing was observed between 2022 and 2024, with the United States, China, Australia, and Canada leading in this area. Findings suggest that ai improves linguistic quality and feedback in the writing process. It also highlights issues related to academic integrity, data privacy, and ai’s inability to manage complex writing errors. More explicit connections between ai and university instruction are necessary to complement traditional pedagogical strategies. The need for more research in this area is urgent, as issues related to equitable access and responsible integration are essential to the use ai to support academic writing development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".