Document Type

Article

Publication Date

4-17-2026

Keywords

Research administration, AI literacy, pedagogical framework, critical engagement, generative AI, higher education, critical AI evaluation skills, AI information literacy, needs assessment

Abstract

This needs assessment plan establishes a framework for developing an undergraduate Research Administration (RA) curriculum that integrates artificial intelligence (AI) competencies across all program components. The plan synthesizes findings from research literature across workforce competency studies, national RA curricula frameworks, e-mentoring models in undergraduate programs, and AI pedagogy in professional education. Key findings reveal that entry-level research administrators require core competencies, including critical thinking, interpersonal skills, written communication, and knowledge of the research enterprise, which are valued by hiring managers (Parker, 2024; Signorelli et al., 2025). The evolving research administration landscape increasingly demands AI literacy, including the ability to interact with AI tools, critically evaluate AI-generated outputs, and apply ethical principles to the use of AI in research support contexts (Tadimalla et al., 2025; Kennedy et al., 2025). The curriculum framework proposed includes five foundational courses aligned with seven program learning outcomes (PLOs), including a new PLO 7 focused on AI competency. Each course integrates AI-specific student learning outcomes (SLOs) and embedded AI activities that complement traditional research administration content. The RA undergraduate program incorporates an e-mentoring model designed to support student learning, persistence, and career awareness while maintaining curriculum-driven outcomes. The e-mentoring structure complements AI integration by providing scaffolded support as students develop both traditional RA competencies and emerging AI skills through professional mentors and faculty guidance. The RA Undergraduate program evaluation design emphasizes mixed-methods approaches, competency-based outcome measurement, and evaluation strategies that isolate mentoring contributions from instructional quality while measuring AI competency development. This plan provides actionable recommendations for curriculum development, implementation strategies, and continuous improvement mechanisms to strengthen the research administration workforce in higher education for an AI-augmented future.

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