Evidence map›Paper›PMID 40628752›Full record

ArticleNPJ science of learning2025

Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education.

Alexander John Karran, Patrick Charland, Joé Trempe-Martineau, Ana Ortiz de Guinea Lopez de Arana, Anne-Marie Lesage, Sylvain Sénécal, Pierre-Majorique Léger

Abstract read
In one paragraph

Article in NPJ science of learning, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Alexander John KarranHEC Montréal, Montréal, Québec, Canada. alexander-john.karran@hec.ca.ORCID http://orcid.org/0000-0002-5821-9561
Patrick CharlandUniversity of Québec in Montréal, Québec, Canada.
Joé Trempe-MartineauHEC Montréal, Montréal, Québec, Canada.
Ana Ortiz de Guinea Lopez de AranaHEC Montréal, Montréal, Québec, Canada.ORCID http://orcid.org/0000-0002-1946-3457
Anne-Marie LesageHEC Montréal, Montréal, Québec, Canada.
Sylvain SénécalHEC Montréal, Montréal, Québec, Canada.
Pierre-Majorique LégerHEC Montréal, Montréal, Québec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recognising a need to investigate the concerns and barriers to the acceptance of artificial intelligence (AI) in education, this study explores the acceptability of different AI applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI, it addresses concerns related to data privacy, AI agency, transparency, explainability, and ethical deployment of AI. Using a vignette methodology, participants were presented with four scenarios where AI agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if it was available. The data collection, comprising a final sample of 1198 participants, focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI vary significantly across stakeholder groups and AI applications.

Identifiers

PMID40628752
PMCPMC12238224

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.