AI-Assisted Dialectical Manuscript Analysis: Toward a Mixed-Method Developmental Editorial Practice

This paper introduces AI-assisted dialectical manuscript analysis as a new mode of editorial engagement for scholarly writing. It argues that conventional peer review, while valuable, is structurally limited by its episodic, specialized, and largely evaluative character. By contrast, the method described here combines sustained human-AI dialogue, multilevel manuscript analysis, and selective quantitative pattern tracking to diagnose problems that often remain invisible across conventional review rounds. These include conceptual conflations, structural imbalances, rhetorical overcompensation, underdeveloped constructs, and unresolved tensions that propagate across sections and revisions. The paper also emphasizes that AI does not replace editorial discernment. Rather, it expands analytical reach while leaving interpretive judgment, diagnostic prioritization, and revision strategy firmly in human hands. In more advanced forms, the method can also involve multi-model triangulation, where convergences and divergences across AI systems become sources of deeper editorial insight. The result is a mixed-method developmental practice that strengthens manuscript quality before, during, and after formal peer review.