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DOI:doi.org/10.65613/743929 Sha HeSchool of Marxism, Wuhan Donghu University, Wuhan 430212, Hubei Province, Chinahanna0098@163.com Abstract The rapid digital transformation of the global education sector has profoundly reshaped occupational workflows, institutional operational paradigms, and workplace environments for educational practitioners. Driven by intelligent education reform and post-pandemic workplace restructuring, the integrated deployment of artificial intelligence (AI) tools and standardized flexible work policies (FWP) has emerged as two core driving forces for optimizing institutional operations and boosting employee work efficiency in educational contexts. This study systematically examines the independent and synergistic impacts of AI tool integration and flexible work policies on multi-dimensional employee productivity across teaching and administrative staff in educational institutions. Adopting a quantitative explanatory research design, this paper aims to clarify the causal relationships between technological empowerment, institutional policy optimization, and employee productivity growth. The AI tools investigated in this research cover five mainstream intelligent educational and auxiliary office platforms, including ChatGPT, Gamma, Grammarly, Scholarcy, and Turnitin, which are widely deployed for instructional design, academic research, textual optimization, academic norm verification, and administrative document processing. Flexible work policies in this study primarily encompass flexible working hour arrangements and hybrid work models that integrate offline on-site duties and online remote work. Based on valid empirical data collected from 426 frontline teachers and administrative personnel across primary, secondary, and tertiary educational institutions, this research constructs a multi-dimensional productivity evaluation framework covering teaching methodology innovation, administrative operational efficiency, academic research output, and long-term work sustainability. Multiple regression analysis and mediation effect testing are employed to elucidate the independent influence pathways of AI tools and flexible work policies on educational employee productivity, as well as their interactive synergistic mechanisms. The empirical results demonstrate that both AI tool application and standardized FWP implementation exert significant positive predictive effects on overall employee productivity in the education sector. AI tools enhance work productivity predominantly by automating repetitive procedural tasks, streamlining teaching and research workflows, and improving work accuracy and standardization, whereas flexible work policies facilitate efficiency gains through rational time allocation optimization, reduction of invalid commuting costs, and improvement of work-life balance. Furthermore, flexible work policies exert a significant positive partial mediating effect on the relationship between AI tool application and employee productivity, enabling educational practitioners to fully leverage the intelligent auxiliary value of AI tools in diversified work scenarios. Meanwhile, this study identifies several practical constraints hindering synergistic empowerment, including uneven proficiency in AI tool adoption among employees, imperfect institutional governance mechanisms for flexible work, and potential productivity differentiation risks induced by excessive work flexibility. Methodologically and theoretically, this study innovatively integrates technological empowerment and institutional optimization perspectives to explore the productivity improvement mechanisms of educational employees, filling the prominent research gap regarding synergistic analysis of AI technology and flexible work policies in educational productivity research. The findings provide empirical evidence and actionable optimization strategies for educational institutions to promote standardized intelligent technology application, formulate scientific flexible work management systems, and achieve high-quality improvement in comprehensive employee productivity. Keywords: Artificial Intelligence; Flexible Work Policies; Employee Productivity; Educational Sector; Teaching Efficiency; Administrative Operation; Work Synergy 1. Introduction 1.1 Research Background The global education industry is undergoing an unprecedented intelligent and digital transformation fueled by iterative generative artificial intelligence innovations and modern human resource management paradigms. In recent years, the continuous upgrading of generative AI and intelligent office technologies has comprehensively penetrated core educational scenarios, including instructional practice, academic research, and institutional administrative operations, fundamentally revolutionizing traditional working modes and workflow systems for educational employees. Concurrently, the widespread adoption of hybrid work models and the iterative optimization of institutional human resource management systems have rendered flexible work policies a pivotal institutional arrangement for enhancing organizational operational efficiency and employee occupational well-being. The dual structural changes of technological iteration and institutional reform have profoundly reshaped the productivity level and occupational development status of teaching and administrative staff, emerging as a critical research agenda in the fields of modern educational management and educational technology. Employee productivity in the education sector exhibits distinct multi-dimensional, procedural, and intellectual attributes that differentiate it from conventional industrial and commercial productivity metrics. The occupational duties of educational practitioners integrate innovative instructional design, standardized student cultivation, professional academic inquiry, and repetitive daily administrative operations, requiring both high-level intellectual output and rigorous procedural execution. For decades, productivity stagnation among educational employees has been constrained by two core bottlenecks. First, educational work entails substantial repetitive and standardized tasks, including textual revision, academic authenticity detection, document sorting, and teaching resource development, which consume massive working time and energy and hinder high-value intellectual work such as pedagogical innovation and academic research. Second, the traditional fixed-time and fixed-location working regime adopted by most educational institutions leads to rigid time allocation, excessive invalid commuting costs, and imbalanced work-life boundaries, thereby inducing occupational burnout, reducing work motivation, and restricting the sustainable improvement of comprehensive work productivity. With the maturity and popularization of generative AI technology, intelligent tools represented by ChatGPT, Gamma, Grammarly, Scholarcy, and Turnitin have become prevalent in the daily work of educational practitioners. ChatGPT provides intelligent assistance for instructional scheme design, lesson planning optimization, and academic problem analysis; Gamma enables rapid generation and intelligent optimization of teaching courseware and academic presentation slides; Grammarly delivers high-precision grammatical detection and academic writing standardization; Scholarcy facilitates efficient literature screening, academic content extraction, and research framework refinement; Turnitin realizes standardized academic plagiarism detection and normative inspection. These AI tools effectively compensate for the inherent inefficiencies of traditional manual workflows, automate repetitive educational procedural tasks, and provide robust technical support for employee productivity enhancement. At the institutional management level, an increasing number of educational institutions have implemented flexible work policies covering flexible working hours and hybrid work arrangements, breaking the institutional shackles of traditional fixed work regimes. Flexible work mechanisms empower employees to independently allocate working time and select work scenarios based on task characteristics and personal conditions, effectively reducing invalid time consumption, optimizing work rhythms, and improving occupational autonomy and flexibility. Although existing literature has independently verified the positive effects of AI technology application and flexible

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