Evidence map›Paper›PMID 42384626›Full record

ArticlePloS one2026

Motivational and psychological variables related to unethical uses of artificial intelligence across multiple life domains (academics and online interactions).

Kyle J Messick, Ash Bass, Matthew Fulmer, Justine Williams, Jade Anderson, Mattie Wolff, Hope Taylor, Eli Wright, Graceyn Yonce, Blanca Aranda

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Kyle J MessickUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.ORCID https://orcid.org/0000-0002-0452-0922
Ash BassUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Matthew FulmerUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Justine WilliamsUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Jade AndersonUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Mattie WolffUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Hope TaylorUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Eli WrightUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.ORCID https://orcid.org/0009-0007-3743-6181
Graceyn YonceUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Blanca ArandaUSCB Social and Cultural Psychology Laboratory, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Past studies about unethical uses of generative artificial intelligence (AI) have focused within a specific life domain (e.g., how AI is used for cheating in academics). An online survey was conducted to investigate how AI users utilized AI across multiple domains, how unethical behaviors in one domain related to behaviors in others, and which motivational and psychological variables corresponded with those uses. The goal was to help create a foundational understanding of how people use AI more broadly, including what the largest motivations for unethical use are and if unethical use had psychological consequences for the user. Creating this foundation can help develop successful interventions for unethical AI use. Findings supported that performing potentially unethical behaviors in one area of life (e.g., academics) were positively related to unethical behaviors in another area of life (e.g., social media interactions). Using AI unethically was positively related to knowing that the use was unethical. Unethical AI use was not related to intrinsic or extrinsic motivation, but was positively related to results pressure (prioritizing outcomes), external pressure (unethical use is normative and unlikely to be penalized), and time pressure (saving time by completing a task more quickly). Unethical AI use was also positively related to a desire for social media popularity and narcissism. Unethical uses of AI were not related to self-esteem, self-efficacy, or loneliness, but were positively related to satisfaction with life, making social comparisons, internalizing the perspective of others about their own body, prioritizing physical appearance over other attributes, and comparing one's physical appearance with others. This study provided evidence that despite ethical misuse, generative AI is being used across multiple domains of life and is largely associated with positive consequences for the user. Unethical AI use was applied across multiple areas of life and was associated with variables related to elevating status, achieving good outcomes, behaving within broader cultural norms, and saving time. Future applications could explore if a more wholistic approach that targets motivations is more effective than a domain-specific approach when attempting to create interventions to curb unethical AI use.

Indexed as

Artificial IntelligenceMotivationGenerative Artificial IntelligenceHumansSurveys and Questionnaires

Identifiers

PMID42384626
PMCPMC13322500

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.