Evidence map›Paper›PMID 42769238›Full record

ArticleFrontiers in psychology2026

AI-driven educational reform: enhancing talent cultivation in computer-related majors for the digital era.

Jun Wang, Pengfei Li

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.

Jun WangCollege of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China.
Pengfei LiCollege of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of artificial intelligence (AI) has driven the emergence of new quality productive forces, reshaping talent demands in the digital era. In response to this transformation, this study explores a comprehensive reform of the talent cultivation model in computer-related majors, deeply integrating AI technologies into the curriculum, teaching methods, and practical training. Grounded in the "new engineering" educational philosophy, the proposed model emphasizes interdisciplinary integration, personalized learning, and collaborative education, aligning with the education, science, and talent strategy outlined in the 20th National Congress of the Communist Party of China. This strategy highlights the importance of using education to drive technological innovation and cultivate high-quality talent with innovative thinking and practical skills. The reform framework includes course restructuring, smart teaching integration, interdisciplinary collaboration, practice-oriented learning enhancement, and a comprehensive evaluation system. This model uses the comprehensive evaluation system to systematically assess students' technical proficiency, innovation awareness, and practical problem-solving skills, providing data-driven insights to support educational reform and optimize teaching strategies. The reform framework has been phased in across four undergraduate programs, aiming to establish a replicable and scalable talent cultivation model under the paradigm of AI-driven educational transformation. Preliminary results show significant improvements in students' technical capabilities, innovation capacity, and readiness to solve real-world challenges.

Indexed as

AI-driven educationeducational reformintelligent assessmentinterdisciplinary integrationtalent cultivation

Identifiers

PMID42769238
PMCPMC13590548

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.