نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 دانشگاه آزاد اسلامی
2 هیات تحریریه
3 مرکزعلمی کاربردی فرهنگ و هنر واحد ۴۱ تهران؛ دانشگاه جامع علمی کاربردی. تهران. ایران
چکیده
با حرکت رسانهها بهسوی تولید محتوای هوشمند، چالشهای نوینی در زمینه مدیریت و راهبری محتوا پدید آمده است. این ابزارهای هوشمند با تولید محتوا متناسب با سلایق مخاطبان، گاه زمینهساز انحراف در افکار عمومی میشوند. پژوهش حاضر با هدف شناسایی و ارائه راهکارهایی برای مقابله با چالشهای هوش مصنوعی در تولید محتوای رسانهای انجام گرفته و از نظر هدف کاربردی و از نظر ماهیت، توصیفی–تحلیلی و آمیخته (کیفی–کمی) است.
در بخش کیفی، با استفاده از مصاحبههای نیمهساختاریافته با ۱۳ نفر از خبرگان دانشگاهی در حوزه مدیریت رسانه و فناوری اطلاعات (نمونهگیری گلولهبرفی تا رسیدن به اشباع نظری)، دادهها جمعآوری و با روش نظریه دادهبنیاد تحلیل شدند. نتایج این بخش در قالب دو مقوله عوامل علّی و راهبردی دستهبندی شدند. عوامل علّی شامل «محدودیتها و چالشهای فنی و الگوریتمی، مسائل دادهای و اطلاعاتی، مسائل اخلاقی و اجتماعی، و تأثیرات اقتصادی و اجتماعی» بودند. راهبردهای استخراجشده نیز در چهار محور «توسعه چارچوبها و قوانین، توسعه فناوری و بهبود فرآیندها، مدیریت منابع انسانی و توانمندسازی، و مدیریت مالی و بهینهسازی منابع» جای گرفتند.
در بخش کمی پژوهش، از پرسشنامهای محققساخته بر مبنای یافتههای کیفی استفاده شد که پس از تأیید روایی و پایایی، در میان ۲۱۳ نفر از کارشناسان رسانه به روش نمونهگیری تصادفی توزیع گردید. تحلیل دادهها با استفاده از مدلسازی معادلات ساختاری (SEM) و تحلیل عاملی تأییدی (CFA) انجام گرفت. نتایج نشان داد که روابط علی میان سازههای مدل مفهومی از نظر آماری معنادار بوده و مدل پیشنهادی از برازش مناسبی برخوردار است.
کلیدواژهها
موضوعات
عنوان مقاله [English]
Presenting a causal-strategic model for solving artificial intelligence challenges in media content production
نویسندگان [English]
- Farid Heydari 1
- mohamad soltanifar 2
- Shahnaz Hashemi 3
1 Azad University
2
3 Ap plied Scientific Education Center of Culture and Art Unit 41, Tehran; Comprehensive University of Applied Science, Tehran. Iran,
چکیده [English]
The integration of artificial intelligence (AI) into the field of media and journalism has generated both unprecedented opportunities and considerable challenges. As intelligent systems increasingly take on the roles of content generation, curation, and dissemination, the ethical, social, technical, and strategic complexities surrounding their deployment require systematic investigation. This study proposes and validates a causal-strategic model that addresses the multifaceted challenges posed by AI in media content production. Employing a mixed-methods approach (qualitative-quantitative), the research aims to explore both the underlying causes and viable strategic responses to these challenges.
The study is applied in its purpose and descriptive-analytical in nature, comprising two primary phases. The qualitative phase involved semi-structured interviews with 13 experts in media management and information technology. Participants were selected through snowball sampling until theoretical saturation was achieved. Data were analyzed using grounded theory techniques, including open, axial, and selective coding. The findings were categorized into two overarching themes: causal factors and strategic responses.
The causal factors were divided into four primary categories: (1) Technical and algorithmic limitations, (2) Data and information-related challenges, (3) Ethical and social concerns, and (4) Economic and social impacts. Within the technical realm, issues such as low generalizability of AI models, inability to understand content context, algorithmic bias, and computational costs were prominent. In the domain of data, participants identified the scarcity of diverse, high-quality data, copyright infringement, and biased training datasets as critical issues. Ethical and social concerns encompassed violations of privacy, lack of accountability, misinformation, and the creation of deepfake content. From an economic standpoint, the deployment of AI in media raises concerns about job displacement, especially in creative industries, and unequal access to technological infrastructure.
In response to these challenges, the study identified four strategic domains for mitigation and adaptation: (1) Development of legal and ethical frameworks, (2) Technological advancement and process optimization, (3) Human resource management and empowerment, and (4) Financial management and resource allocation. These strategies aim to provide actionable pathways for media organizations to responsibly and effectively integrate AI technologies into their content production pipelines.
The quantitative phase of the study utilized a researcher-made questionnaire developed based on the qualitative findings. The questionnaire, consisting of items rated on a five-point Likert scale, was distributed among 213 media professionals selected via stratified random sampling. Validity was confirmed through expert review and content analysis, while reliability was established using Cronbach’s alpha (values exceeding 0.7 for all constructs).
Data analysis was conducted using Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA), supported by SPSS and AMOS software. The analysis confirmed that the causal and strategic variables proposed in the conceptual model were statistically significant. Indicators such as CMIN/DF, RMSEA, CFI, and NFI met standard thresholds, confirming model fit. Specifically, the causal relationship between AI-related challenges and strategic responses was substantiated, indicating that the proposed framework effectively captures the dynamics involved in media organizations’ adaptation to AI.
The model validation results demonstrated a robust fit with observed data, with RMSEA values of 0.045 and 0.055 for causal and strategic factors, respectively—both within acceptable limits. Furthermore, the critical ratios (CR) for paths in the structural model exceeded 1.96, confirming their statistical significance at the 95% confidence level. These outcomes support the theoretical model’s capability to explain and predict effective strategies for handling AI-driven transformations in media content production.
The discussion section highlights the importance of a systemic perspective in addressing the disruptive impact of AI. While AI offers tools to enhance productivity and audience engagement, it also poses threats to content authenticity, employment, and media ethics. The dual-use nature of AI—where the same technology can serve both constructive and harmful purposes—necessitates a nuanced, context-sensitive approach to governance and deployment. For example, personalization algorithms can improve user experience but may also reinforce echo chambers and ideological polarization.
Furthermore, the study emphasizes the importance of interdisciplinary collaboration between policymakers, technologists, media professionals, and ethicists in formulating guidelines and frameworks. Capacity building through education and training is critical to ensure that professionals across the media value chain are equipped to engage with AI technologies responsibly.
From a policy standpoint, the research calls for the establishment of transparent regulatory bodies to oversee AI usage in media. This includes enforcing data protection standards, ensuring the traceability of AI-generated content, and instituting legal accountability mechanisms. At the organizational level, firms are encouraged to invest in hybrid systems where human oversight complements machine efficiency, thereby mitigating risks while capitalizing on AI’s strengths.
In conclusion, the causal-strategic model proposed in this study offers a comprehensive roadmap for understanding and addressing the challenges of AI in media content production. The integration of grounded theory and structural modeling provides both depth and rigor, making the findings highly applicable for academic researchers, media practitioners, and policymakers alike. The model not only elucidates the current barriers but also provides a structured approach to strategic intervention. It is recommended that future studies test this model across different cultural and organizational contexts to refine its generalizability and operational effectiveness.
کلیدواژهها [English]
- Causal-Strategic
- Artificial Intelligence Challenges
- Media Content
- Media Management
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