Leveraging LLMs for interpreting historical source code: a case study of the Apple Lisa through critical code studies

This study evaluates conversational large language models (LLMs) as pedagogical brainstorming tools for historical source code analysis through structured prompt-based approaches adapted from Critical Code Studies (CCS). The research tests whether conversational interfaces like ChatGPT-4o can support initial exploration of complex historical codebases by adapting CCS perspectives into conversational prompt formats. The dual-prompt evaluation separates technical parsing from interpretive reasoning, assessing how effectively conversational interfaces extract structural information while generating preliminary interpretive hypotheses. Using the Apple Lisa source code as a case study, this analysis documents both pedagogical utility and systematic limitations. The findings demonstrate that while conversational LLMs can preserve developer annotations, parse visual artifacts such as ASCII diagrams, and generate educationally valuable insights for approaching unfamiliar programming languages, the conversational user interface significantly constrains systematic analytical capabilities. Through three case studies examining ASCII typography, architectural diagrams, and interface implementations, the analysis illustrates how conversational interfaces function as structured brainstorming partners while necessitating rigorous validation against primary sources. This evaluation contributes to understanding AI’s pedagogical role in digital humanities by positioning conversational LLMs as question-framing tools rather than interpretive authorities, while identifying systematic implementation requirements necessary for rigorous computational approaches to historical software analysis.