AI evidence extraction

Ethical Considerations of Generative AI in Higher Education

Yao Chu, Na Li, Jinjin Lu, Naser Sedghi · 2026

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

7/10

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/10.4324/9781003716518-18

Methodology & findings

Study design

Scoping review of 62 relevant journal articles published between January 2020 and July 2025, examining ethical and moral issues arising from GenAI application in higher education from a global perspective..

Main result

The study reveals that "academic integrity is the most significant concern, followed by the erosion of critical thinking, as well as concerns regarding privacy and transparency" when GenAI tools are applied in higher education. Additionally, the research identifies "a global trend shifting from prohibition towards guidance" in institutional and national policy responses.

Research paradigm

Interpretivist/qualitative synthesis

Author conclusions

The authors conclude that "this chapter concludes by offering targeted recommendations for policymakers, institutions, educators, and students. These aim to foster the better and more responsible integration of GenAI in higher education, thereby providing fresh perspectives for advancing this field."

Risk of bias

Publication bias (only journal articles included; grey literature excluded); Time-period selection bias (2020-2025 publication window may miss earlier foundational work); Geographic representation bias (countries with English-language publications potentially overrepresented); Selection bias in article inclusion criteria (not specified in abstract); Selection bias: Articles published only in English may be included, potentially missing non-English literature; grey literature (reports, conference proceedings) may be excluded; Geographic bias: 'Global perspective' claimed but actual geographic representation of the 62 articles is unstated; Subjective coding risk: No inter-rater reliability metrics reported for ethical theme extraction from 62 articles; Confirmation bias: Authors may have selectively coded articles to support predetermined ethical concerns; Interpretation bias in thematic analysis of ethical issues

Open questions raised

The study addresses the gap of examining "the ethical and moral issues arising from the application of GenAI tools in higher education from a global perspective" and explores institutional and national policy responses to guide responsible integration.
The study identifies the need for better and more responsible integration of GenAI in higher education through targeted recommendations for different stakeholder groups (policymakers, institutions, educators, students), suggesting gaps in coordinated governance and implementation guidance.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 68%

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