Evidence map›Paper›PMID 42491441›Full record

ReviewFrontiers in medicine2026

Reframing faculty development for digital transformation and generative AI in higher education.

Zhitao Hou, Jing Chen, Hao Zhang

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 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

3 authors.

Zhitao HouCollege of Basic Medical and Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Jing ChenCollege of Basic Medical and Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Hao ZhangCollege of Basic Medical and Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As educational digitalization continues to deepen and generative artificial intelligence rapidly enters higher education teaching contexts, faculty professional development is undergoing a profound transformation from tool use to pedagogical redesign and from individual adaptation to organizational change. However, current faculty development practices still tend to substitute standardized training for differentiated support, making it difficult to address the diverse needs of teachers at different career stages in digital competence, curricular innovation, disciplinary integration, and academic leadership. To enhance the transparency of the framework-building process, this study was informed by explicit review questions, database-based literature retrieval, and thematic synthesis. Building on scholarship on teacher digital competence, teacher professional development theory, and the practice of digital transformation in higher education, with Heilongjiang University of Chinese Medicine serving as the institutional context for framework formulation, this paper offers a systematic account of the stage-specific developmental tasks of university faculty in the digital era and advances a multi-level, developmental framework. The framework is organizationally supported by coordination across three institutional levels-university, school, and department-structured around four developmental stages: entry and adaptation, stable development, deep integration, and academic leadership, and implemented through five pathways: blended professional learning, cross-boundary linkage, community-based mutual learning, diagnostic mentoring, and evidence-informed practice. The study contends that faculty development should move beyond a narrow focus on digital tool training and instead emphasize the systematic development of capacities related to pedagogical improvement, disciplinary reconfiguration, digital ethics, and organizational leadership. This framework helps enhance the stage-specific relevance, practical explanatory power, and organizational operability of faculty development, while also providing a theoretical reference for designing faculty development systems in digitally transformed universities.

Indexed as

digital competenceeducational digitalizationfaculty development in higher educationfaculty development modelgenerative artificial intelligence

Identifiers

PMID42491441
PMCPMC13377356

What OpenQuestion holds

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LicenceCC BY
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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.