Evidence map›Paper›PMID 42688071›Full record

ArticleFrontiers in psychology2026

Student engagement and self-regulated learning across standard and AI-enhanced flipped learning: a within-subjects longitudinal study.

Chamintsetseg Myagmarkhorloo, Bat-Uchral Ganzorigt, Anita Habók

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.

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0citing papers in PubMed
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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

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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.

Chamintsetseg MyagmarkhorlooDoctoral School of Education, University of Szeged, Szeged, Hungary.
Bat-Uchral GanzorigtDepartment of European Studies, National University of Mongolia, Ulaanbaatar, Mongolia.
Anita HabókInstitute of Education, University of Szeged, Szeged, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Although AI is increasingly being integrated into flipped classrooms, little is known about how student engagement and self-regulated learning (SRL) develop across extended implementations, particularly in underrepresented higher education contexts. This study examined developmental trajectories of student engagement and SRL across a sequential implementation comprising a standard flipped phase followed by a progressively scaffolded AI-enhanced phase, representing one of the earliest longitudinal investigations of AI-enhanced flipped learning in a Central Asian higher education context. Methods: Using a within-subjects longitudinal design, 211 undergraduate students at a public university in Mongolia were followed across four measurement occasions (Weeks 1, 6, 11, and 15) within a 16-week course, with quantitative data collected across 15 weeks of instruction. Engagement and SRL were assessed across a standard flipped phase (Weeks 1-6) and a progressively scaffolded AI-enhanced phase (Weeks 7-15). Metacognitive self-regulation was examined as a potential mediator of the longitudinal relationship between agentic engagement and academic achievement using a cross-lagged panel model (Mplus 8.3, ML estimator, 1,000 bootstrap samples). Results: Linear mixed-model analyses revealed significant trajectories for all engagement and SRL subscales (all Discussion: The findings describe distinct longitudinal trajectories of engagement and SRL across sequential instructional phases and support a developmental interpretation in which standard flipped instruction and progressively scaffolded AI integration may play distinct roles in supporting student engagement and SRL. This interpretation warrants evaluation in future controlled longitudinal studies.

Indexed as

flipped classroomgenerative artificial intelligencehigher educationlongitudinal studyMongoliaself-regulated learningstudent engagement

Identifiers

PMID42688071
PMCPMC13533682

What OpenQuestion holds

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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.