Researchers built an experiment where people interacted with an AI trained only on pre-1930 text to see if talking to a "historical mind" would shift their views about the past. It worked—people who chatted with the old-data model reported less of a bias that the past was more moral than today, compared to those using a current AI.

• The experiment reduced the "illusion of moral decline"—a documented bias where people assume past societies were more ethical than they actually were—by having participants interact with a historically-bounded language model instead of a contemporary one.
• Historically-bounded LLMs create an experimentally accessible way to approximate talking to someone from the past without modern knowledge contaminating their perspective, solving a real methodological problem in behavioral research.
• This framework treats AI systems as research instruments that can deliberately manipulate interaction conditions (in this case, temporal knowledge) to study and influence how people perceive, reason, and reflect.