Informing for Equity: A Systemic Framework for GenAI Assessment in Resource-Constrained Higher Education

Abstract

Aim/Purpose To examine how Generative AI (GenAI) creates systemic informing failures in resource-constrained higher education institutions, producing cycles of inequity that fragmented interventions cannot resolve. Background This study introduces a Systemic Framework for Equitable GenAI Assessment that addresses breakdowns in information sharing across students, faculty, and administrators through three interlocking principles: infrastructure-based equity, transparent collaboration, and institutional scaffolding. Methodology A systematic literature synthesis of 47 sources, informed by critical pedagogy, digital equity theory, and institutional theory, with Philippine higher education as an illustrative case of informing system failure. Contribution The framework provides a novel informing systems model that explains how GenAI’s equity challenges are causally interdependent and offers a phased implementation roadmap for improving institutional informing in resource-constrained contexts. Findings Infrastructural exclusion, pedagogical misalignment, and ethical displacement form a self-reinforcing informing failure system. Isolated interventions fail due to three causal mechanisms: Pedagogical Coherence Constraint, Institutional Legitimacy Spiral, and Resource Allocation Trap. Recommendations for Practitioners Institutions should adopt phased, integrated implementation beginning with institutional scaffolding, then small-scale pilots that coordinate informing across stakeholder groups. Recommendations for Researchers Priority areas include longitudinal implementation studies, comparative validation across Global South regions, and research into how the framework applies to indigenous knowledge systems and non-Western epistemologies. Impact on Society The framework prevents GenAI from reinforcing digital coloniality by transforming institutional informing systems to support educational justice and equitable access. Future Research Empirical validation through participatory action research, disciplinary variation studies, and indigenous-led inquiry into GenAI’s compatibility with oral and relational knowledge transmission.

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