Evidence map›Paper›PMID 42787230›Full record

ArticleFrontiers in psychology2026

Empowerment over enforcement: unpacking the psychological drivers of AI-assisted deep revision in EFL writing.

Huan Li, Wenna Zhang

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

2 authors.

Huan LiSchool of Foreign Languages, Qiongtai Normal University, Haikou, China.
Wenna ZhangSchool of Foreign Languages, Huaibei Normal University, Huaibei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid integration of generative artificial intelligence (AI) in higher education highlights a critical tension between top-down administrative enforcement and students' genuine cognitive investment. Drawing upon Self-Determination Theory (SDT) and Cognitive Load Theory (CLT), this study investigates how AI Prompting Literacy (APL) and External Mandate (EM) differentially predict the Deep Revision Engagement (DRE) of English as a Foreign Language (EFL) learners. Utilizing survey data from 327 Chinese university students and partial least squares structural equation modeling (PLS-SEM), we tested a multiple parallel mediation model incorporating Perceived Competence (PC), Intrinsic Motivation (IM), and Psychological Safety (PS). The empirical results demonstrate that External Mandate yields a negligible direct path, suggesting that compliance-driven requirements may fail to directly stimulate deep cognitive participation. Conversely, AI Prompting Literacy emerges as a significant predictor within this structural model. Higher prompting literacy exhibits a positive indirect association with learners' core psychological needs, thereby predicting an elevated level of Deep Revision Engagement through the parallel pathways of Perceived Competence, Intrinsic Motivation, and Psychological Safety. This research complements traditional technology acceptance frameworks by shifting the analytical lens toward internal psychological needs, suggesting that educational policies are more effective when focusing on cultivating student prompting capabilities alongside necessary institutional guidance.

Indexed as

AI prompting literacycognitive load theorydeep revision engagementEFL writingexternal mandategenerative AIself-determination theory

Identifiers

PMID42787230
PMCPMC13601272

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.