Evidence map›Paper›PMID 41245344›Full record

SynthesisFrontiers in psychology2025

Unlocking the potential of artificial intelligence in improving learning achievement in blended learning: a meta-analysis.

Jiajun Wu, Ahmed Tlili, Soheil Salha, Daria Mizza, Mohammed Saqr, Sonsoles López-Pernas, Ronghuai Huang

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in psychology, 2025. 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

7 authors.

Jiajun WuFaculty of Education, City University of Macau, Taipa, Macau SAR, China.
Ahmed TliliSmart Learning Institute of Beijing Normal University, Beijing, China.
Soheil SalhaAn-Najah National University, Nablus, Palestine.
Daria MizzaDepartment of Educational Studies, American University in Cairo, New Cairo, Egypt.
Mohammed SaqrSchool of Computing, University of Eastern Finland, Kuopio, Finland.
Sonsoles López-PernasSchool of Computing, University of Eastern Finland, Kuopio, Finland.
Ronghuai HuangSmart Learning Institute of Beijing Normal University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: While several studies investigated the effect of blended learning on students' learning achievement, scant information exists on whether using artificial intelligence (AI) in blended learning could further contribute to the obtained effect. Methods: To effectively address the challenges and opportunities presented by blended learning and AI, the present study conducts a meta-analysis to systematically examine the impact of AI-enhanced blended learning on students' learning achievement, considering the significant role of multiple variables in shaping this achievement, including the type of AI technology, instruction duration, research design, and sample size as well as across different educational levels and subject areas. Specifically, 21 studies ( Results: The obtained results revealed that AI has a medium effect ( Discussion: The findings of this study can help researchers and practitioners better understand the effects of AI in blended learning, thereby contributing to a better design of teaching and learning experiences accordingly.

Indexed as

artificial intelligenceblended learninghybrid learninglearning achievementlearning performancemeta-analysisquantitative evidencesmart learning

Identifiers

PMID41245344
PMCPMC12616485

What Socratic 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 Socratic graph.