What does this research mean for the field?

Responsible integration of Generative AI in higher education requires use-specific, participatory, and proportionate approaches supported by a sociotechnical framework, as its pedagogical benefits remain conditional on disciplinary context, user capability, and human oversight. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate pedagogical opportunities, risks, student–staff partnership models, and governance requirements for integrating generative AI into higher education.

September 5, 2026InformationOpen Access

Generative AI in Higher Education: A Structured Integrative Literature Review of Student–Staff Partnership, Responsible Use, and Inclusive Learning

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Authors

ASAntesar Shabut VSVishvapriya Sangvikar XLX. Lucas Lu

Discussion

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Overview

Integrative review reveals conditional benefits and implementation risks of generative AI in higher education, highlighting the need for participatory governance and ethical safeguards.

Key Points

To evaluate pedagogical opportunities, risks, student–staff partnership models, and governance requirements for integrating generative AI into higher education.
Conducted a PRISMA-informed structured integrative literature review and thematic synthesis.
Purposively selected a focused corpus of 27 reports from 1,011 broadly eligible publications, with a focus on UK higher education.
Generative AI supported writing, planning, explanation, and language assistance, but evidence was stronger for user perceptions than for verified causal improvements in learning outcomes.
Student–staff partnership showed meaningful impact primarily when direct collaboration shaped curriculum, assessment, or institutional policy rather than tokenistic consultation.
Synthesized findings into a TPACK–Responsible AI–Sociotechnical framework to guide proportionate institutional adoption, permitted-use policies, assessment redesign, and digital equity.

Cite This Study

Shabut et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4436b95aff0620ebbb9 https://doi.org/10.3390/info17090853

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September 5, 2026InformationOpen Access

Generative AI in Higher Education: A Structured Integrative Literature Review of Student–Staff Partnership, Responsible Use, and Inclusive Learning

View Full Paper

Ask AI

Bookmark

Authors

ASAntesar Shabut VSVishvapriya Sangvikar XLX. Lucas Lu

Discussion

Member takes

Overview

Integrative review reveals conditional benefits and implementation risks of generative AI in higher education, highlighting the need for participatory governance and ethical safeguards.

Key Points

To evaluate pedagogical opportunities, risks, student–staff partnership models, and governance requirements for integrating generative AI into higher education.
Conducted a PRISMA-informed structured integrative literature review and thematic synthesis.
Purposively selected a focused corpus of 27 reports from 1,011 broadly eligible publications, with a focus on UK higher education.
Generative AI supported writing, planning, explanation, and language assistance, but evidence was stronger for user perceptions than for verified causal improvements in learning outcomes.
Student–staff partnership showed meaningful impact primarily when direct collaboration shaped curriculum, assessment, or institutional policy rather than tokenistic consultation.
Synthesized findings into a TPACK–Responsible AI–Sociotechnical framework to guide proportionate institutional adoption, permitted-use policies, assessment redesign, and digital equity.

Cite This Study

Shabut et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4436b95aff0620ebbb9 https://doi.org/10.3390/info17090853

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