Enhancing Financial Education with AI-Driven Learning and Simulations

As financial markets evolve in complexity, artificial intelligence (AI) and multimodal learning present innovative pathways to reshape financial education. This study introduces a creative AI-driven framework that fuses structured financial data, sentiment analysis, and deep learning to enhance portfolio optimization and strategic decision-making. Leveraging natural language processing (NLP), machine learning, and quantitative modeling, the framework enables more accurate forecasting and adaptive investment strategies. A key contribution lies in applying multimodal data fusion-integrating numerical indicators with unstructured textual insights-to improve predictive precision. Advanced deep learning models, including transformers, LSTM networks, and reinforcement learning, are employed to evaluate risk-adjusted returns and optimize portfolio allocations dynamically. Uniquely, this research extends beyond technical modeling to explore the pedagogical impact of AI-powered trading simulations. By incorporating macroeconomic indicators into Deep Reinforcement Learning environments, the simulations offer learners a hands-on, realistic experience in portfolio construction, risk management, and behavioral finance. Empirical results confirm that AI-enhanced sentiment and factor models improve forecasting accuracy and investment performance. Overall, this study advances the field by bridging AI, financial modeling, and education-laying the groundwork for intelligent, interactive learning systems that prepare students and professionals to navigate financial complexity with confidence and strategic insight.