AI evidence extraction

Toward Responsible Integration: A Review of Applications, Capabilities, and Perceptions of Generative AI in Higher Education

Ying Qian, Nicholas A. Bowman · Education Sciences · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.3390/educsci16020323

Methodology & findings

Study design

Scoping review of 50 empirical articles published from January 2023 to April 2025 on generative AI in higher education, synthesizing applications, capabilities, and perceptions across stakeholder groups..

Sample

N = 50, 2 groups

Primary method

Qualitative synthesis and thematic analysis of 50 empirical articles; no quantitative statistical methods reported in the abstract.

Main result

The review synthesized findings from 50 empirical articles and found that "students hold a more open and positive attitude toward this rising technology, while instructors and researchers hold mixed attitudes toward GenAI usage, and administrators tend to hold an open but cautious attitude toward GenAI implementation." The study also demonstrated "how GenAI has already been applied and present[s] its potential for implementation across teaching and learning, research, and student affairs in higher education."

Reports effect sizes.

Research paradigm

Mixed methods (qualitative synthesis of empirical studies)

Author conclusions

The authors conclude that "Addressing common stakeholder concerns and needs, we outline institutional strategies for responsible GenAI integration, including launching GenAI learning hubs, formalizing license agreements, redefining academic originality, and implementing pilot programs."

Risk of bias

Temporal limitation (January 2023 to April 2025) may introduce recency bias; Language bias if only English-language articles were included; Publication bias toward positive or notable findings about GenAI; Selection bias in choosing which 50 articles from potentially larger pool to review; Not reported in abstract; Publication bias (only published empirical articles included); Time-bound search (January 2023 to April 2025) may miss earlier foundational work; Selection bias in which 50 articles were chosen from the population of relevant studies; Potential geographic bias depending on journal coverage

Open questions raised

The authors identify the need for institutional strategies for responsible GenAI integration and recommend launching learning hubs, formalizing agreements, redefining academic standards, and implementing pilot programs to address stakeholder concerns.
Not explicitly detailed in the abstract
The authors identify a gap in the literature: "While numerous articles discuss applications and perceptions of GenAI in higher education, no comprehensive review has considered commonalities and differences among various educational stakeholder groups and contexts." This review was designed to address that gap.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 59%

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