Introduction
The quality of feedback during the manuscript review process is vital for ensuring scholarly rigor and clarity in academic publishing. This study evaluates the feedback provided by three distinct methods: human reviewers, AI-generated critiques like those from ChatGPT, and the structured analysis from SCiNiTO. Human reviewers offer contextually grounded comments that reference relevant literature, promoting scientific rigor but sometimes lacking prioritization in their feedback. Conversely, AI tools like ChatGPT typically provide generalized suggestions that are clear yet fail to address intricate methodological issues. SCiNiTO stands out through its structured, criterion-based assessments, linking critiques directly to established best practices while allowing authors to systematically prioritize their revisions.
This article aims to illuminate the strengths and weaknesses of these feedback mechanisms as they relate to clarity, specificity, and scientific rigor.
Human Reviewer: Contextualized Rigor
The human reviewer’s comments were highly specific, referencing external works like the TP-MV model to contextualize the manuscript’s shortcomings1. This specificity ensured that feedback was grounded in the field’s existing literature, a hallmark of rigorous peer review. However, the feedback occasionally lacked clear prioritization, mixing major concerns (e.g., missing SMOTE comparisons) with minor issues (e.g., web server accessibility).
AI-Generated Review: Generalized Suggestions
AI tools like GPT often produce feedback that is clear but overly generic. For instance, they might recommend “improving the abstract’s clarity” without specifying how technical terms like “multi-head self-attention” could be simplified for broader accessibility. While such feedback is useful for surface-level revisions, it does not address the manuscript’s scientific validity or methodological soundness.
SCiNiTO: Structured Precision
SCiNiTO excelled in delivering clear, criterion-based feedback. It explicitly linked critiques to best practices, such as noting the methods section’s omission of software versions and hardware memory details. By categorizing feedback into strengths (e.g., dataset construction details) and weaknesses (e.g., insufficient statistical rigor in t-test reporting), SCiNiTO enabled authors to prioritize revisions systematically. Its assessment of the introduction’s needs for a results preview demonstrated an understanding of narrative flow in scientific writing.