Evidence map›Paper›PMID 42865554›Full record

ArticleFrontiers in psychology2026

AI-mediated writing and durable learning: a psychological theory of calibration, bypassing and uptake.

Zihang Lan, Chen Niu

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.

Zihang Lan *Faculty of Languages and Linguistics, University of Malaya, Kuala Lumpur, Malaysia.
Chen Niu *Faculty of Languages and Linguistics, University of Malaya, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI)-mediated writing provides a critical case for educational psychology because it reveals how learners regulate cognition, effort, confidence, motivation, and beliefs about competence when fluent external support is available. In English as a foreign language (EFL) writing, such support can correct grammar, reformulate sentences, suggest organization, and sustain revision dialogs with learners who may not have immediate access to human feedback. These affordances invite a tempting inference: better AI-assisted drafts indicate better learning. This Hypothesis and Theory article qualifies that inference by proposing Calibration-Bypassing-Uptake theory, a psychologically grounded account of when AI-mediated writing support strengthens durable competence and when it mainly improves visible performance. The theory links five processes: perceived feedback affordances, dual calibration of source and self, cognitive-affective regulation, strategic uptake or cognitive bypassing, and delayed transfer. Calibration refers to learners' ability to judge both the reliability of AI feedback and the adequacy of their own understanding. Cognitive bypassing refers to delegating a target learning operation to AI without substantively attempting, evaluating, or reconstructing it during the writing episode; its consequences for learning require separate testing. The article argues that AI supports EFL writing development when learners use feedback to predict, compare, explain, revise, and transfer, rather than merely accept improved text. It derives hypotheses about feedback type, cognitive load, learner proficiency, writing anxiety, self-efficacy, authorship beliefs, hybrid human-AI feedback, alongside a methodological proposition about measuring transfer. The central outcome is therefore not the polish of the assisted draft alone, but the learner's later capacity to plan, justify, revise, and transfer with reduced support.

Indexed as

cognitive bypassingcognitive loadeducational psychologyEnglish as a foreign language writingfeedback uptakegenerative artificial intelligencemetacognitionself-regulated learning

Identifiers

PMID42865554
PMCPMC13655090

What OpenQuestion holds

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Registered trials

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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.