Predicting the future is hard, especially when it comes to finances. But what if AI could help forecast financial aid, ensuring students get the support they need? New research explores how Large Language Models (LLMs), the same technology behind ChatGPT, can tackle the complex challenge of predicting financial aid distribution. Financial time series forecasting is notoriously difficult, with limited historical data and ever-changing policies making accurate predictions a headache. Traditional methods struggle, but LLMs offer a glimmer of hope. Researchers tested several cutting-edge time series models, including LLMs like GPT-2, against traditional approaches. The key advantage of LLMs? Their ability to learn from limited data, crucial in scenarios with scarce historical information, like financial aid. The study found that LLMs, especially TimeLLM, excelled in 'few-shot' learning scenarios where training data is limited. This opens exciting possibilities for predicting financial aid even with yearly, rather than daily, data. While the study also tested 'zero-shot' learning (no training data), the results were less impressive, suggesting LLMs still need some grounding in the specific data to make accurate predictions. This research highlights the potential of LLMs to revolutionize financial aid forecasting, offering a powerful tool for institutions and policymakers. However, challenges remain, particularly in improving zero-shot performance and incorporating diverse financial data sources. Future research could explore incorporating multiple data modalities, like news sentiment or economic indicators, to enhance prediction accuracy. This study is a promising first step, hinting at a future where AI can empower students and institutions with more accurate and timely financial aid insights.