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

Abstract

Generative artificial intelligence (GenAI) is reshaping higher education, yet its educational value, governance, inclusion and responsible implementation remain contested. This structured integrative literature review used a PRISMA-informed search and selection process and thematic synthesis to examine pedagogical opportunities and risks, student–staff partnership, assessment and academic integrity, institutional governance, privacy, accessibility and digital equity, with particular attention to UK higher education. The detailed synthesis drew on a 27-report focused corpus formed through an investigator-dependent purposive selection process from 1011 broadly eligible reports; it is therefore neither exhaustive nor statistically or descriptively representative of the broad eligible register. The evidence suggests that GenAI may support explanation, writing, planning, language assistance and flexible academic engagement, but these benefits remain conditional on disciplinary context, user capability, verification and continued human support. The evidence base is stronger for reported use, perceptions and implementation concerns than for causal improvements in learning outcomes. Evidence of student–staff partnership was clearest where participation influenced defining curriculum, assessment or policy decisions; consultation alone should not be presented as co-creation. Responsible adoption also requires programme-level assessment review, clear permitted-use guidance, accessible alternatives, equitable access, data protection, meaningful human oversight and institutional accountability. Drawing these findings together, the review develops an evidence-informed TPACK–Responsible AI–Sociotechnical framework based on pedagogical and capability alignment, Responsible AI safeguards and sociotechnical readiness. The framework supports transparent decisions to approve, approve with controls, pilot with monitoring, redesign or reject proposed uses. Responsible GenAI integration should therefore be use-specific, participatory, proportionate and subject to continued review.