Evidence map›Paper›PMID 40325105›Full record

ArticleScientific reports2025

Cognitive bias in generative AI influences religious education.

Jing Zhang, Wenlong Song, Yang Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jing ZhangSchool of Government and Public Affairs, Communication University of China, Beijing, 100024, China.
Wenlong SongSchool of International Relations, Beijing International Studies University, Beijing, 100024, China. songwenlong@bisu.edu.cn.
Yang LiuSchool of International Relations, Beijing International Studies University, Beijing, 100024, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the transformative role of generative artificial intelligence (AI) in shaping religious cognition, with particular emphasis on its implications for religious education. By examining the biases inherent in AI-generated content, this research highlights how these biases influence user perceptions and interactions with diverse religious teachings. Through experimental frameworks and pre/post-interaction evaluations, the study reveals that generative AI not only reflects but amplifies cognitive biases, affecting users' understanding of religious doctrines and cultural diversity. The findings underscore the potential of generative AI to act as a double-edged sword in religious education: enhancing personalized learning and cross-cultural understanding while risking the reinforcement of prejudice. These insights call for ethical guidelines and oversight mechanisms in deploying generative AI within religious contexts. This research contributes to the growing discourse on AI ethics and its pivotal role in shaping inclusive and unbiased religious education in the digital era.

Indexed as

Artificial IntelligenceCognitionReligionAdultCultural DiversityFemaleHumansMaleAI ethicsCognitive biasExperimental designGenerative AIReligious education

Identifiers

PMID40325105
PMCPMC12053680

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.