Evidence map›Paper›PMID 40664983›Full record

ArticleScientific reports2025

Artificial intelligence in educational technology and transformative approaches to English language using fuzzy framework with CRITIC-TOPSIS method.

Jingdan Liu, Xujie Bao, Liji Chen

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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.

Jingdan LiuSchool of General Education, Hunan University of Information Technology, Changsha, 410000, Hunan, China.
Xujie BaoSchool of General Education, Hunan University of Information Technology, Changsha, 410000, Hunan, China. xujiebao2022@163.com.
Liji ChenSchool of General Education, Hunan University of Information Technology, Changsha, 410000, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming educational technology by enabling personalized, adaptive, and data-driven learning experiences. Machine learning algorithms analyze student performance to tailor content delivery, while natural language processing facilitates interactive learning through voice assistants and essay evaluation. This article presents the potential of criteria importance through the inter-criteria correlation (ICCR) method, which is used to determine objective weights under theoretical concepts of standard deviation and coefficient correlation techniques. Furthermore, another approach using the tool for order preference by similarity to the ideal solution (TOPSIS) method is discussed to investigate the ranking of preferences under various criteria and experts' opinions within the system of the q-rung orthopair fuzzy framework. To reveal the validation and superiority, a numerical example is discussed to evaluate an effective AI approach to improve English language and psychology pedagogy under different criteria. Furthermore, a comprehensive comparative study is conducted to assess the compatibility of the proposed optimization techniques in the CRITIC-TOPSIS method with existing optimization approaches. Finally, the findings and contributions, along with future directions, are discussed in the conclusion. Additionally, we can apply the discussed decision-making methodologies to resolve complex real-life applications, such as renewable energy, medical diagnosis, multi-robotic systems, social selections, and computational and environmental sciences.

Indexed as

Advanced educational technologiesAggregation operatorsCRITIC-TOPSIS for decision-making problemsq-rung orthopair fuzzy information

Identifiers

PMID40664983
PMCPMC12264288

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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.