The Impact of AI-Assisted Secretarial Services Through the Lens of Fluid Reality Theory: Toward Adaptive Administrative Ecosystems in Higher Education

Purpose: This study examines the transformative impact of artificial intelligence integration in university secretarial services through the theoretical framework of Fluid Reality Theory, investigating how AI-assisted administrative systems create adaptive organizational ecosystems that enhance student satisfaction and institutional effectiveness. Objectives: The research aims to evaluate the effectiveness of AI-assisted secretarial services in improving student satisfaction scores, reducing administrative stress levels, and enhancing retention rates while exploring the theoretical implications through Ariel Fuchs’ Fluid Reality Theory framework. Methodology: A mixed-methods approach was employed involving a controlled experimental design conducted over 12 months at Gaia College. Quantitative data were collected from 450 students and 25 secretarial staff members through validated satisfaction surveys, stress assessment scales, and institutional retention metrics. Qualitative insights were gathered through semi-structured interviews and observational studies. Statistical analysis included correlation analysis, t-tests, and regression modeling. Findings: Results demonstrated significant improvements in student satisfaction scores from 3.5 to 4.2 (p < 0.001), reduction in administrative stress levels from 3.9 to 2.7 (p < 0.001), and a 33% decrease in student dropout rates. AI-assisted secretaries handled 32% more daily requests while experiencing reduced stress levels. Correlation analysis revealed positive relationships between AI adoption and student satisfaction (r = 0.73) and negative correlations with secretary stress (r = −0.68). Recommendations: The study recommends systematic implementation of AI-assisted administrative systems guided by Fluid Reality Theory principles, emphasizing adaptive boundary management, human-AI collaborative frameworks, and continuous system optimization. Institutional policies should focus on ethical AI integration while preserving human agency and maintaining student-centered approaches.