The PAGE framework paper presents an intriguing application of AI to comics scholarship, proposing a system that parses comic book scans into panels, text, and bibliographic records, then annotates pages using a vision-language model following a human-like codebook. Across three studies using 3,929 American comic books (1938–1956) and 87 manga volumes, the authors demonstrate how the framework can recover portrayals of political power, identify individual artists by visual style, and track speaker patterns across traditions—always with human annotation as ground truth.
Beyond research, the framework offers tangible benefits: making archives searchable by visual content, enabling accessibility for blind and low-vision readers, preserving fragile collections, and crediting previously uncredited artists. The research agenda outlined suggests AI could strengthen comics study, preservation, and reach in multiple dimensions.
However, this remains a specialized humanities computing project rather than a general technology milestone. While well-executed and socially valuable in its domain, it doesn't represent a major capability jump, widespread adoption, or breakthrough that would typically earn placement in a general tech news feed. The work advances comics scholarship specifically, not computing broadly.