AI-Augmented Performance Management in Higher Education: A Smart-Tech Framework for Faculty Development and Institutional Growth

In HEIs, existing faculty performance management systems are still a union of subjective annual performance review, self-assessment, and manager judgment. These systems do not recognize the autonomy, hybridity, and fluidity of contemporary academic labour. New positions entail new demands e.g., online and hybrid teaching, online mentorship, cross-disciplinary teamwork, community participation, and being results-oriented that are not yet reflected in the legacy metrics (for e.g., academic publication or aggregate student feedback). This literature review article aims to synthesize empirical, conceptual, and theoretical literature on AI for faculty performance measurement and management (FPMM). In the search engines (such as Scopus, WoS, ERIC or from top-tier journals), I identify the primary disadvantages of the traditional methods such as: human-centric bias, opaque methodology, high administrative costs and ineffective accountability.

After outlining the shortcomings of these approaches (human bias, opaqueness, administrative overload and unmet global accountability needs), a proposed solution (the Smart-Tech Framework, Marín, 2020) is introduced. The framework is based on the Enhancement Model of UTAUT2 (Venkatesh et al., 2012) which integrates the latest empirical findings of AI systems, coming up with five constructs (AI system quality, perceived usefulness, AIO trust, faculty engagement and institutional outcomes) and the three main moderating factors (ethical governance, digital capital and cultural/institutional context) to account some of the ongoing problems of algorithmic bias, data privacy fears, perceived surveillance, faculty opposition and digital inequities (Marín, 2025; Acikgul, 2026; EDUCAUSE, 2026). He also hopes the Smart-Tech Framework would enable teachers to broaden their added value on AI in a professional development model based on a lifelong learning philosophy, minimize administrative workloads, decrease inequality perceptions and improve institutional standings, accreditation and stakeholders confidence.

The conceptual model (see Figure 1) illustrated these relationships as a conceptual path diagram. It also considers the practical implications for policy making and for HEI leadership and for human-centred implementation, ethical protections and recommendations for future empirical testing using mixed methods studies. This model offers a progressive and holistic framework for the HEI to utilize the revolutionary potential of AI without sacrificing equity or academic integrity.