تلفیق مدیریت دانش و هوش مصنوعی جهت بهبود عملکرد نیروی انسانی (مورد مطالعه: دستگاه‌های اجرایی استان مرکزی)

هدف: تلفیق هوش مصنوعی و مدیریت‌ دانش‌ به‌ استفاده راهبردی از فناوری‌ برای‌ افزایش‌ ایجاد، اشتراک و استفاده‌از دانش‌ در یک‌ سازمان اشاره دارد. پیاده‌سازی هوش مصنوعی در مدیریت دانش، چالش‌ها و محدودیت‌هایی برای سازمان‌ها به‌ویژه دستگاه‌های اجرایی که وظیفه خدمت‌رسانی به مردم را دارند به‌همراه دارد. برای درک ارتباط بین مدیریت دانش و هوش مصنوعی، باید نحوه تاثیرگذاری آن بر متغیرهای مهم نیروی انسانی سازمان‌ها ازجمله «عملکرد» به‌دقت مورد بررسی قرار گیرد. هدف این مطالعه بررسی تاثیر هم‌زمان مدیریت دانش و هوش مصنوعی بر عملکرد نیروی انسانی دستگاه‌های اجرایی است.روش پژوهش: پژوهش حاضر ازنظر جهت‌گیری، کاربردی و ازنظر روش‌شناسی، کمی است که با استفاده‌از راهبرد توصیفی-همبستگی انجام شده‌است. بازه زمانی مطالعه زمستان 1403 و بهار 1404 است. جامعه‌آماری پژوهش کارکنان دستگاه‌های اجرایی استان مرکزی است که 150 نفر از مدیران و کارشناسان به‌روش نمونه‌گیری گلوله‌برفی، انتخاب شدند. برای جمع‌آوری داده‌ها از ابزار پرسش‌نامه استفاده‌شد که پایایی پرسش‌نامه براساس معیار‌های آلفای کرونباخ و پایایی ترکیبی و روایی آن براساس شاخص‌های aveو بارعاملی متقابل موردتایید قرار گرفت. جهت تجزیه‌وتحلیل داده‌ها از روش مدل‌سازی معادلات ساختاری و برای انجام محاسبات آن از نرم‌افزار smart pls 3.0 استفاده‌شد.یافته‌ها: تجزیه‌وتحلیل داده‌ها نشان داد کاربردهای مختلف هوش مصنوعی رابطه مستقیمی با فرآیندهای مدیریت دانش دارد. مستندسازی دانش ضمنی بر تبدیل و انتقال و به‌کارگیری دانش؛ شخصی‌سازی دسترسی‌به دانش بر حفظ و نگهداری دانش؛ پیش‌بینی و تصمیم‌گیری هوشمند بر ایجاد و به‌کارگیری دانش، تاثیر مثبت و معناداری دارند. همچنین تلفیق هوش مصنوعی و مدیریت دانش رابطه مستقیمی با عملکرد نیروی انسانی دستگاه‌های اجرایی دارد. ایجاد دانش بر مشارکت؛ حفظ و نگهداری دانش بر حفظ و نگهداشت و رضایت؛ تبدیل و انتقال دانش بر مشارکت؛ به‌کارگیری دانش بر حفظ و نگهداشت، رضایت و هزینه‌های آموزش، تاثیر مثبت و معناداری دارند. بیشترین ضریب مسیر (641/0) مربوط‌به تاثیر به‌کارگیری دانش بر رضایت، و کمترین ضریب مسیر (174/0) مربوط‌به تاثیر پیش‌بینی و تصمیم‌گیری هوشمند بر به‌کارگیری دانش، می‌باشد.نتیجه‌گیری: برنامه‌های آموزشی جامع برای تقویت مهارت‌ها و آگاهی کارکنان در زمینه هوش مصنوعی و مدیریت دانش راه‌اندازی شود. تشکیل تیم‌های چندتخصصی که ترکیبی از کارکنان بخش‌های مختلف سازمان هستند، می‌تواند در ایجاد فرهنگ هوش مصنوعی و مدیریت دانش در سازمان کمک کند. فرآیندهای استانداردی برای جمع‌آوری، سازماندهی و تبدیل داده‌ها به فرمت قابل‌استفاده وجود داشته باشد. اقدامات امنیتی شامل استفاده‌از الگوریتم‌های رمزنگاری قوی برای حفاظت‌از داده‌ها، اعمال سیاست‌ها و محدودیت‌های دسترسی، بررسی و شناسایی تهدیدات امنیتی، درنظر گرفته‌شوند. سازمان‌ها سازوکارهای نظارتی مناسبی برای تضمین کیفیت داده‌های به‌اشتراک گذاشته‌شده طراحی نمایند.اصالت/ارزش: مدیریت دانش شرایطی را فراهم می‌کند تا درک دانش رخ دهد، در حالی که هوش مصنوعی قابلیت‌های گسترش، استفاده و ایجاد دانش را به روش‌هایی نوین و کارآمد فراهم می‌کند. با تلفیق موثر فناوری‌ با شیوه‌های‌ مدیریت‌ دانش‌، سازمان‌ها می‌توانند تصمیم‌‌گیری‌، نوآوری‌ و عملکرد کلی‌ سازمان را بهبود بخشند. آموزش و توسعه منابع انسانی متخصص در حوزه هوش مصنوعی و مدیریت دانش برای پیاده‌سازی موفق این فناوری بسیار حیاتی است.

integrating knowledge management and artificial intelligence to improve human resource performance (case study: government offices of markazi province)

purpose: the integration of ai and knowledge management refers to the strategic use of technology to increase the creation, sharing, and use of knowledge in an organization. implementing artificial intelligence in knowledge management poses challenges and limitations for organizations, especially government departments that are tasked with providing services to the public. to understand the relationship between knowledge management and ai, it is necessary to carefully examine how it affects important human resource variables of organizations, including &performance&. the aim of this study is to investigate the simultaneous impact of knowledge management and ai on the performance of human resources in the public sector.methodology: the present study is applied in terms of orientation and quantitative in terms of methodology, which was conducted using a descriptive-correlation strategy. the study period is winter 2024 and spring 2025. the research population is the employees of the public sector in markazi province, from which 150 managers and experts were selected using snowball sampling. a questionnaire was used to collect data, and its reliability was confirmed based on cronbach’s alpha and combined reliability criteria, and validity were confirmed based on ave and cross-factor loading indices. structural equation modeling was used to analyze the data, and smart pls 3.0 software was used to perform its calculations.findings: data analysis showed that various applications of artificial intelligence have a direct relationship with knowledge management processes. documenting tacit knowledge has a positive and significant effect on knowledge conversion, transfer, and application; personalizing access to knowledge has a positive and significant effect on knowledge retention and maintenance; and intelligent prediction and decision-making has a positive and significant effect on knowledge creation and application. also, the integration of artificial intelligence and knowledge management has a direct relationship with the performance of human resources in the public sector. knowledge creation has a positive and significant effect on participation; knowledge retention and maintenance has a positive and significant effect on satisfaction; knowledge conversion and transfer has a positive and significant effect on participation; and knowledge application has a positive and significant effect on retention and maintenance, satisfaction, and training costs. the highest path coefficient (0.641) is related to the effect of knowledge application on satisfaction, and the lowest path coefficient (0.174) is related to the effect of prediction and intelligent decision-making on knowledge application.research limitations/implications: the following are some of the limitations of the present study:1. limited use of artificial intelligence tools in the country’s public sector.2. limited access to service sector employees who use artificial intelligence tools, resulting in a limited sample size.3. limited data analysis methods and tools.the following suggestions are made for future research:1. the challenges and obstacles to implementing artificial intelligence and knowledge management in executive agencies should be studied.2. the impact of using new technologies on various organizational performance measures should be examined.3. the scope of the study should be expanded at the national level and the comparative study should be expanded at the international level.4. qualitative methodology should be used to find the implementation pattern of artificial intelligence and knowledge management in the public sector.practical implications: comprehensive training programs should be launched to strengthen the skills and awareness of employees in the field of artificial intelligence and knowledge management. the formation of multidisciplinary teams that are a combination of employees from different departments of the organization can help in creating a culture of artificial intelligence and knowledge management in the organization. there should be standard processes for collecting, organizing and converting data into a usable format. security measures should be considered, including the use of strong encryption algorithms to protect data, applying access policies and restrictions, and reviewing and identifying security threats. organizations should design appropriate monitoring mechanisms to ensure the quality of shared data.one of the application areas where artificial intelligence has many capabilities is knowledge management. using artificial intelligence tools and techniques such as machine learning, artificial neural networks, natural language processing and recommender systems, knowledge can be collected, organized, extracted and shared in a structured way. this improves access to knowledge, increases the efficiency and speed of knowledge management processes, and makes better organizational decisions. however, to successfully implement artificial intelligence in knowledge management, we need to face related challenges. one of the benefits of artificial intelligence for knowledge management is the power of prediction and analysis. using machine learning algorithms and artificial neural networks, it is possible to recognize patterns and trends in data and provide more accurate predictions about future behaviors and changes. this capability is of great importance for organizations, because they can make better decisions for the future and achieve success and sustainable growth. artificial intelligence can also be used to aggregate knowledge. by using hybrid algorithms and intelligent decision-making systems, information and knowledge in an organization can be collected from various sources and a comprehensive picture of organizational knowledge can be obtained. this helps the organization to improve its strategies and decisions based on existing knowledge and achieve better results. in addition, artificial intelligence can play a role in increasing cooperation and interaction between people in different departments of the organization. by using recommender systems and data analysis, more effective communication and collaboration can be established between members of the organization. these systems can give individuals suggestions that increase interaction and cooperation between team members and improve the performance and creativity of groups. another benefit of artificial intelligence for knowledge management is improving access to knowledge. using machine learning algorithms, systems can be developed that are capable of searching and extracting knowledge from various sources, which allows for quick and easy access to the required knowledge at any time and place. artificial intelligence can also play an important role in knowledge sharing. ai-based recommender systems can help employees in an organization share their knowledge and experiences with colleagues. these systems can recommend appropriate materials and resources to employees based on past experiences and individual profiles, and accelerate the knowledge sharing process. using natural language processing, artificial intelligence can be used to analyze texts and information available in an organization. this technology can help identify topics, extract useful information, and summarize texts. natural language processing systems can also be used in knowledge management automation processes. for example, automation systems can automatically categorize and assign relevant tags to related content. this speeds up the process of categorizing and organizing knowledge, improving its accessibility and usability.originality/value: knowledge management provides the conditions for knowledge understanding to occur, while ai provides the capabilities to expand, use, and create knowledge in new and efficient ways. by effectively integrating technology with knowledge management practices, organizations can improve decision-making, innovation, and overall organizational performance. training and development of human resources specialized in ai and knowledge management is critical to the successful implementation of this technology.