Can we trust AI-assisted technical writing in electromagnetics? characteristic mode analysis for metasurface design as a stress test

This article is intentionally generated by an artificial intelligence (AI) system and is framed as a practical stress test: can modern AI reliably draft an engineering tutorial/review on a technically nuanced topic without inventing citations and without producing internally inconsistent technical explanations? The technical topic selected for this test and reported here is the applications of CMA for designing metasurface. Specifically, CMA provides an eigenmode-based decomposition of currents and fields that can expose the dominant mechanisms governing metasurface antennas and related engineered surfaces. This tutorial-style review synthesizes CMA formulations relevant to metasurfaces spanning finite apertures and periodic frequency-selective surfaces, and maps commonly reported modal quantities (eigenvalues, modal significance, characteristic angle, and excitation-dependent modal weights) to practical design tasks such as bandwidth enhancement, mode suppression, polarization conversion, and reconfigurable phase control. Practical guidance is provided on mode tracking, normalization conventions, and validation against full-wave simulations or measurements, with the aim of improving transparency and reproducibility in CMA-guided metasurface studies. As this manuscript was drafted with AI assistance, we additionally report an explicit citation-verification and self-consistency audit to illustrate a concrete workflow for maintaining scientific reliability in AI-assisted technical writing. This manuscript is intentionally positioned as a tutorial/review and an AI-audit case study rather than as a primary research paper; accordingly, the figures are illustrative schematics and synthetic plots used to explain modal reasoning, not to report new electromagnetic simulations or measurements.