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طراحی چارچوب شبیهساز شناختی برای ارتقای یادگیری مدیریت دانش در برنامههای آموزشی سازمانی | ||
| مدیریت راهبردی دانش سازمانی | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 تیر 1405 | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| شناسه دیجیتال (DOI): 10.47176/SMOK.2026.1984 | ||
| نویسندگان | ||
| سید عبدالله صالح نژاد* 1؛ امین امینی2؛ محمدعلی فرشچیان3 | ||
| 1استادیار گروه مدیریت دانش، دانشکده هوش مصنوعی و علوم شناختی، دانشگاه جامع امام حسین علیه السلام، تهران، ایران. | ||
| 2دانشکده و پژوهشکده هوش مصنوعی و علوم شناختی، دانشگاه جامع امام حسین (ع)، تهران، ایران. | ||
| 3گروه مدیریت دانش، دانشکده و پژوهشکده هوش مصنوعی و علوم شناختی، دانشگاه جامع امام حسین علیه السلام، تهران، ایران | ||
| تاریخ دریافت: 27 آذر 1404، تاریخ بازنگری: 25 خرداد 1405، تاریخ پذیرش: 06 تیر 1405 | ||
| چکیده | ||
| هدف: در محیطهای پیچیده و پویا که مدیریت دانش به عنوان یکی از مؤلفههای حیاتی مزیت رقابتی شناخته میشود، ضعف در یادگیری و انتقال مؤثر دانش، عملکرد سازمانها را با چالش مواجه میسازد. یکی از نیازهای اساسی، طراحی ابزارهایی است که بتوانند با بهرهگیری از اصول علوم شناختی، یادگیری تعاملی و تجربهمحور را تسهیل کنند. بر این اساس، هدف پژوهش حاضر طراحی چارچوب یک شبیهساز شناختی برای یادگیری مدیریت دانش در برنامههای آموزشی، بهویژه در محیطهای سازمانی و نظامی، است. روش پژوهش: این مطالعه از نوع تحقیق کیفی اکتشافی–توصیفی است که با رویکرد تحلیل مضمون انجام شده است. جامعه پژوهش شامل خبرگان حوزههای مدیریت دانش، علوم شناختی، آموزش و فناوری بوده و نمونهگیری به صورت هدفمند و با فن گلولهبرفی صورت گرفته است. دادهها از طریق مصاحبههای نیمهساختاریافته گردآوری شد. برای اطمینان از روایی، از بازبینی مشارکتکنندگان و تطبیق با منابع نظری استفاده شد و پایایی از طریق توافق در کدگذاری و محاسبه ضریب توافق، با میانگین ۸۲ درصد تأیید گردید. تحلیل دادهها بر اساس مراحل ولکات و با بهرهگیری از نرمافزار MAXQDA انجام شد. یافتهها: تحلیل مصاحبهها به شناسایی هشت مؤلفه اصلی در طراحی شبیهساز شناختی منجر شد: «معرفی و هدف، بازیگران، طراحی و معماری، محیط، محتوای آموزشی، روشهای ارزیابی، توسعه و بهروزرسانی، عملکرد و کارایی». بر اساس دادههای استخراجشده، بیش از ۱۲۰ کد اولیه شناسایی شد که پس از پالایش در قالب ۳۲ مضمون میانی و نهایتاً هشت مضمون اصلی تجمیع گردید. یافتهها نشان داد که یادگیری مؤثر در شبیهسازهای شناختی مستلزم طراحی فعالیتهای تعاملی، تعریف روشن نقشها، تسهیل اشتراکگذاری دانش، بازخورد مستمر، همراستاسازی اهداف یادگیری و ایجاد محیطی شبیهسازیشده با عناصر ریسک، عدم قطعیت و تصمیمگیری چندسطحی است. نتیجهگیری: چارچوب ارائهشده میتواند به عنوان مدلی ساختاریافته برای طراحی شبیهسازهای شناختی در یادگیری مدیریت دانش مورد استفاده قرار گیرد و با افزایش تعامل، مشارکت و تجربهورزی، کیفیت آموزش و توانمندیهای دانشی یادگیرندگان را به شکل معناداری ارتقا دهد. این چارچوب به سازمانها کمک میکند تا فرآیندهای یادگیری مدیریت دانش را واقعیسازی، فردمحور و پایدار سازند. اصالت/ارزش: نوآوری این پژوهش در همگرایی سه حوزه مدیریت دانش، علوم شناختی و شبیهسازی آموزشی و ارائه نخستین چارچوب بومیشده برای طراحی یک شبیهساز شناختی در یادگیری مدیریت دانش در کشور است. این تحقیق با گذر از مرزهای مرسوم روششناسی و بهرهگیری از تحلیل عمیق خبرگان، مدلی ارائه میدهد که میتواند مبنایی برای توسعه ابزارهای نوین یادگیری هوشمند در سازمانها و محیطهای نظامی باشد. کلیدواژهها: ابزارهای یادگیری؛ تحلیل مضمون؛ شبیهساز شناختی؛ مدیریت دانش؛ یادگیری سازمانی؛ یادگیری مبتنی بر بازی | ||
| کلیدواژهها | ||
| ابزارهای یادگیری؛ تحلیل مضمون؛ شبیهساز شناختی؛ مدیریت دانش؛ یادگیری سازمانی؛ یادگیری مبتنی بر بازی | ||
| عنوان مقاله [English] | ||
| Designing a Cognitive Simulator Framework to Enhance Knowledge Management Learning in Organizational Training Programs | ||
| نویسندگان [English] | ||
| Abdollah Salehnezhad1؛ Amin Amini2؛ Mohammad Ali Farshchian3 | ||
| 1Assistant Professor, Department of Knowledge Management, Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hossein (AS) University, Tehran, Iran. | ||
| 2Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hossein University, Teheran, Iran. | ||
| 3Department of Knowledge Management, Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hossein (AS) University, Tehran, Iran. | ||
| چکیده [English] | ||
| Purpose: In contemporary organizational environments characterized by volatility, complexity, and rapid knowledge evolution, the effectiveness of knowledge management (KM) practices increasingly depends on the ability of individuals and organizations to learn, internalize, share, and apply knowledge in dynamic contexts. Traditional instructional approaches, especially in knowledge-intensive and mission-critical settings such as military and security organizations, often fail to provide learners with the cognitive engagement, experiential exposure, and interactive decision-making opportunities necessary for deep learning. Cognitive simulators—technology-enhanced environments inspired by cognitive science, learning theories, and experiential modeling—represent an emerging solution that can overcome these limitations. Despite increasing interest in cognitive technologies and simulation-based learning, the literature still lacks a coherent, systematic, and empirically grounded framework for designing a cognitive simulator specifically tailored to KM learning. Existing models are fragmented, technologically oriented, or limited to narrow instructional designs and do not address the multidimensional interplay among cognitive processes, decision-making, knowledge flows, user roles, and organizational learning structures. This study aims to fill this gap by designing a comprehensive conceptual framework for a cognitive simulator dedicated to knowledge management learning within educational programs. Anchored in interdisciplinary foundations—knowledge management, cognitive science, learning sciences, artificial intelligence, and simulation design—this framework seeks to structure the essential components, dynamic interactions, and functional processes required to foster deep, adaptive, and collaborative knowledge learning. The purpose is therefore twofold: first, to conceptualize and define the fundamental architecture of a KM cognitive simulator; and second, to reveal the mechanisms through which such a simulator can enhance knowledge acquisition, sharing, internalization, decision-making, and performance outcomes for learners. Design/methodology/approach: The research adopts a qualitative exploratory-descriptive methodology appropriate for conceptual theory building in emerging cross-disciplinary areas. The study employed semi-structured, in-depth interviews with experts from the fields of knowledge management, cognitive science, artificial intelligence, educational technology, and simulation development. Purposive and snowball sampling techniques were used to identify participants with specialized knowledge and practical experience. The study followed the thematic analysis model of Wolcott, which includes systematic processes of data reduction, data display, and drawing conclusions. Interview transcripts were coded using a combination of open, axial, and selective coding. Data trustworthiness was ensured through triangulation with the theoretical literature, member checking with interviewees, and peer debriefing. Intercoder reliability was assessed through independent coding by multiple researchers, resulting in an average reliability coefficient of 0.82, indicating high consistency. Data analysis was supported using the MAXQDA qualitative analysis software, enabling systematic organization of codes, themes, and relationships. More than 120 initial codes were extracted from the interview data. These were subsequently categorized into 32 subthemes and finally consolidated into eight major thematic dimensions that form the proposed simulator framework. The methodological approach also incorporated extensive review and synthesis of theoretical foundations in KM processes, cognitive learning theories (including information processing theory, constructivism, cognitive load theory, and social learning theory), and models of simulation-based learning and cognitive engineering. Through this integrated approach, the research ensured that the resulting framework aligns with both empirical insights from experts and established theoretical foundations. Findings: The analysis produced a multi-layered and comprehensive framework composed of eight core dimensions essential for the design and deployment of a cognitive simulator for KM learning. Introduction and Purpose: This dimension clarifies the mission, scope, and learning objectives of the simulator. It emphasizes alignment between KM competencies, cognitive learning requirements, and real-world organizational scenarios—particularly those involving uncertainty, risk, collaboration, and strategic decision-making. Users and Actors: The simulator must support multiple user groups—including learners, instructors, administrators, and intelligent agents—each with clearly defined roles. Expert insights highlighted the necessity of modeling actors with distinct knowledge levels, decision-making patterns, and interaction behaviors. Design and Architecture: This dimension includes structural components such as the cognitive engine, scenario generator, decision-support mechanisms, interaction modules, and feedback analytics. Findings suggest that a successful simulator requires layered architecture integrating KM processes (knowledge creation, sharing, storage, use) with cognitive functions (attention, memory, reasoning, and problem solving). Environment: A realistic, context-rich environment is critical for effective simulation-based learning. Experts emphasized the importance of modeling organizational dynamics, constraints, missions, cultural elements, and uncertainty factors. The environment must allow learners to experience cause–effect relationships and test decisions safely. Educational Content: Content must be authentic, domain-specific, cognitively aligned, and progressively structured. It should combine declarative, procedural, and tacit knowledge elements and allow learners to transition from simple to complex scenarios, thereby supporting internalization of KM concepts. Evaluation Methods: Assessment must incorporate both process-oriented and performance-oriented metrics. Findings identify the importance of multi-dimensional evaluation, including cognitive load measurement, decision accuracy, learning progression, collaboration effectiveness, usage behavior patterns, and reflective feedback. Development and Updating: The simulator must be dynamic, with continuous updating of scenarios, cognitive models, and KM content. Mechanisms for adaptation, scalability, and integration with new technologies such as AI tutors or adaptive learning engines were considered essential. Performance and Efficiency: The final dimension measures how effectively the simulator enhances learning outcomes. Experts reported that a cognitive simulator can significantly improve KM competencies—especially decision-making under uncertainty, knowledge sharing attitudes, systems thinking, and collaborative problem-solving—when properly designed. Overall, findings indicate that a cognitive simulator structured around these eight dimensions can provide an immersive, interactive, and cognitively aligned environment capable of transforming the way KM is taught and internalized. Research limitations/implications: The qualitative nature of the study, while enabling deep exploration, inherently limits generalizability. The sample size, although sufficient for thematic saturation, was restricted to experts within Iranian academic and military-learning contexts, potentially narrowing cultural and organizational diversity. Furthermore, the framework has not yet been implemented or empirically validated in a fully operational simulator, limiting assessment of its real-world performance and pedagogical impact. Despite these limitations, the implications are significant: the study offers a foundational theoretical model that can guide future system development, enable quantitative validation studies, and inform interdisciplinary research across KM, cognitive science, human–computer interaction, and simulation engineering. Practical implications: The framework provides actionable guidance for organizations aiming to modernize their learning infrastructures. It enables instructional designers, simulation developers, and KM specialists to plan and construct cognitive simulators that enhance knowledge internalization, decision-making accuracy, and learner engagement. Military and mission-driven organizations can use the framework to simulate complex, high-risk KM environments, thereby reducing learning costs and operational risks. Educational institutions can integrate the model into curricula to support hands-on, experiential learning. The framework can also inform the development of AI-driven learning systems, intelligent tutoring modules, and adaptive feedback environments that personalize knowledge acquisition based on cognitive and behavioral data. Originality/value: This research is the first comprehensive, interdisciplinary, and empirically grounded study in Iran to develop a conceptual framework for a KM-specific cognitive simulator. It bridges several traditionally separate fields—knowledge management, simulation-based learning, cognitive psychology, and artificial intelligence—offering a novel integration that expands methodological and conceptual boundaries. The study contributes original value by: • introducing a structured framework for cognitive simulation in KM learning; • synthesizing cognitive theories with KM processes in a unified architecture; • providing empirically validated thematic dimensions derived from expert knowledge; • offering a foundational reference for future simulator development and cross-disciplinary research. Its value extends to scholars, practitioners, system designers, and organizations seeking sophisticated, cognition-enhanced educational technologies. | ||
| کلیدواژهها [English] | ||
| Cognitive simulator, Knowledge management learning, Simulation-based education, Thematic analysis, Cognitive science, Game-based learning, Organizational learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 11 |
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