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

Artificial intelligence (AI) is increasingly transforming faculty performance management in higher education by enabling data-driven evaluation, real-time analytics, personalised feedback, and targeted professional development. However, the effectiveness of AI-augmented performance management depends not only on technological capabilities but also on faculty perceptions of usefulness, trust, engagement, and the institutional conditions under which these systems operate. This study proposes a Smart-Tech Framework to examine how AI System Quality (ASQ) and Smart Features Integration (SFI) shape faculty experiences and contribute to personalised faculty development and institutional outcomes. Drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the framework integrates perceived usefulness, trust, faculty engagement, personalised faculty development, and institutional outcomes. It further conceptualises digital readiness, ethical governance, and cultural and institutional context as human-centred moderating conditions that may influence the relationships among these constructs. Methodologically, the study adopts a post-positivist, deductive, explanatory sequential mixed-methods design. The proposed quantitative phase will employ a multi-institutional survey of full-time faculty members with recent experience of AI-enabled assessment, teaching analytics, research analytics, or faculty development recommendation systems. A target of at least 380 responses is envisaged, with partial least squares structural equation modelling (PLS-SEM) used to assess the measurement and structural models, including direct, indirect, and moderated relationships. The subsequent qualitative phase will involve approximately 15–20 semi-structured interviews with faculty members and academic administrators to contextualise and explain the quantitative findings. As an ongoing research design, the study does not report empirical results; instead, it establishes a theoretically grounded and empirically testable framework for investigating human-centred AI-enabled performance management. The anticipated contribution is to inform institutional decisions concerning AI system design, ethical oversight, digital capability development, and faculty participation, while supporting more transparent, equitable, and development-oriented performance management in higher education. The study aligns with Sustainable Development Goal 4 (Quality Education) and SDG 9 (Industry, Innovation and Infrastructure).