Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge

Many people now use AI chatbots to obtain summaries of complex topics, yet we know little about how this affects knowledge acquisition, including how the effects might vary across different groups of people. We conducted two experiments comparing how well people recalled factual information after reading AI-generated or human-written historical summaries. Participants who read AI-generated summaries scored significantly higher on knowledge tests than those who read expert-written blog posts (Study 1) or Wikipedia articles (Study 2). These improvements were present regardless of whether readers knew the content was AI-generated or if the AI summaries were politically biased. Moreover, AI summaries improved recall across various demographic groups, including gender, race, income, education, and digital literacy levels. This suggets that using AI tools for everyday factual queries does not create new knowledge inequalities but could still amplify existing ones through differential access. Our findings indicate that the increasingly routine use of AI for information-seeking could enhance factual learning, with implications for education policy and addressing inequality.

Authors

Institutions

Publication Details

Journal
Social Science Computer Review
Published
2025-12-24
DOI
https://doi.org/10.1177/08944393251409744
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge

Matthew Shu, Keitaro Okura, Thomas Davidson, Daniel Karell
Social Science Computer Review
Artificial Intelligence in Healthcare and Education
article

Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge

Matthew Shu, Keitaro Okura, Thomas Davidson, Daniel Karell
article en

Abstract

Many people now use AI chatbots to obtain summaries of complex topics, yet we know little about how this affects knowledge acquisition, including how the effects might vary across different groups of people. We conducted two experiments comparing how well people recalled factual information after reading AI-generated or human-written historical summaries. Participants who read AI-generated summaries scored significantly higher on knowledge tests than those who read expert-written blog posts (Study 1) or Wikipedia articles (Study 2). These improvements were present regardless of whether readers knew the content was AI-generated or if the AI summaries were politically biased. Moreover, AI summaries improved recall across various demographic groups, including gender, race, income, education, and digital literacy levels. This suggets that using AI tools for everyday factual queries does not create new knowledge inequalities but could still amplify existing ones through differential access. Our findings indicate that the increasingly routine use of AI for information-seeking could enhance factual learning, with implications for education policy and addressing inequality.

Social Science Computer Review
Rutgers, The State University of New Jersey (US), Yale University (US), Rutgers Sexual and Reproductive Health and Rights (NL), Rütgers (Germany) (DE)
Quality Education
Openalex Percentile: Top 40%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Generating the Past: How Artificial Intelligence Summaries of Historical Events Affect Knowledge — Matthew Shu, Keitaro Okura, et al. · Social Science Computer Review (2025) | TGRS Research Map | TGRS