A Proof-of-Concept Study Evaluating the Impact of Comic Literacy on Meaning-Making of Microscopy Data in Microbial Systems and the Capability of LLMs as Collaborators

This proof-of-concept study investigates the impact of a comic literacy framework on scientific narrative synthesis, a key component of a larger meaning-making framework for inclusive smart microscopy that I proposed in Gulez (2026). Using NotebookLM (Gemini 1.5 Pro), I evaluated how multimodal narrative logic ( comic literacy logic ) influences a Large Language Model’s (LLM) ability to interpret microscopy data from microbial biofilms and spores. I used an LLM-as-a-Judge approach with Chain-of-Thought prompting to compare three narratives (baseline, framework-guided, and context-enhanced) against ground-truth papers. Results indicate that the comic literacy framework enhances the “Narrative Leap,” enabling the artificial intelligence (AI) to transition from simple data description to high-level scientific insight and relational reasoning. This work provides a foundation for developing AI as a meaning-making collaborator.