In the stage of pervasive digital communication, the rapid dissemination of fake news via social media platforms poses significant challenges to public discourse, societal trust, and institutional integrity. Fake news, characterized by fabricated or misleading content, spreads rapidly across platforms like Facebook, Twitter, WhatsApp, influencing political outcomes and public perceptions. Traditional methods of manual fact-checking cannot keep pace with the scale of misinformation, necessitating the use of advanced technologies such as Natural Language Processing (NLP) and Artificial Intelligence (AI). NLP enables machines to interpret and analyze textual data, identifying linguistic patterns, sentiment, and deceptive cues associated with fake news. Meanwhile, AI, particularly through machine learning and deep learning techniques, improves the accuracy and scalability of detection systems. These technologies integrate textual analysis with social network dynamics to detect misinformation and forecast its propagation. However, challenges remain, including limited datasets with cultural and linguistic diversity, evolving tactics by fake news creators, and ethical concerns such as algorithmic bias and transparency. This research emphasizes the need for comprehensive solutions that combine robust datasets, multimodal data analysis, and predictive modeling. Integrating ethical frameworks ensures that AI-driven systems remain fair and accountable. By advancing real-time detection and forecasting mechanisms, NLP and AI provide a proactive approach to mitigating the societal impact of misinformation, fostering a healthier and more informed digital ecosystem

The integration of artificial intelligence (AI) into Agile IT project management has emerged as a transformative strategy for enhancing risk assessment and decision-making processes. Despite the increasing interest, a fragmented understanding persists regarding the application, effectiveness, and limitations of AI-driven tools in Agile environments. This thematic review systematically analyses 20 peer-reviewed studies published between 2010 and 2025 to synthesise current knowledge and identify research gaps. The literature reveals that machine learning, predictive analytics, natural language processing, and reinforcement learning offer substantial benefits, including improved sprint planning, early risk detection, optimised resource allocation, and greater responsiveness to dynamic project demands. The study categorises findings into four core themes: AI integration in Agile workflows, AI-driven risk assessment methodologies, AI-enhanced decision support systems, and challenges to adoption. Barriers such as data quality issues, model interpretability, integration complexities, organisational resistance, and high implementation costs are critically examined. A conceptual framework, the Dynamic structuration framework (DSF), which leverages the adaptive structuration theory and Dynamic capability theory, is proposed to guide the structured integration of AI within Agile project lifecycles, emphasising the need for recursive co-evolution, iterative feedback loops, augmented capabilities as well as a governable human–AI collaboration. The review highlights the superiority of AI-driven approaches over traditional methods in terms of predictive accuracy and operational agility, while also calling for further empirical validation and development of explainable AI models. The findings offer valuable insights for researchers and practitioners seeking to advance AI-enabled Agile project management.