Evidence map›Paper›PMID 42783561›Full record

ArticleJournal of Intelligence2026

Comparing Human Ability with Generative AI: Self-Evaluation, Appraisal, and Adaptive Professional Intelligence Among Journalism and Communication Students.

Juan Wang, Zichen Liu, Jiaying Huang

Abstract read
In one paragraph

Article in Journal of Intelligence, 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

3 authors.

Juan WangFaculty of International Media, Communication University of China, Beijing 100024, China.
Zichen LiuSchool of Humanities, Communication University of China, Beijing 100024, China.ORCID 0009-0006-4199-500X
Jiaying HuangFaculty of International Media, Communication University of China, Beijing 100024, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (AI) is reshaping how students evaluate their abilities, occupational prospects, and the continuing value of professional education. This study examined how ability-based comparison with AI is associated with perceived professional value among journalism and communication students and why the same comparison may be linked to both threatening and adaptive responses. An explanatory sequential mixed-methods design was used. Survey data from 507 students in China were analyzed using partial least squares structural equation modeling, with PROCESS analyses as robustness checks, followed by semi-structured interviews with 20 students. Ability-based comparison with AI was positively associated with job replacement anxiety, which was negatively associated with perceived professional value, and with AI learning motivation, which was positively associated with perceived professional value. A positive direct association with perceived professional value also remained. AI hindrance appraisal strengthened the comparison-anxiety association, whereas AI challenge appraisal did not significantly moderate the comparison-motivation association. Interviews indicated that these relationships were task- and criterion-dependent and shaped by occupational interpretation and concrete learning demands. Human-AI comparison may therefore relate to professional value through simultaneous threat- and adaptation-oriented pathways rather than a uniformly positive or negative process.

Indexed as

AI appraisalAI learning motivationhuman–AI comparisonjob replacement anxietyjournalism and communication educationperceived professional value

Identifiers

PMID42783561
PMCPMC13608180

What OpenQuestion holds

Textmetadata
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.