A fascinating new study asks: are Large Language Models (LLMs) better at explaining complex topics than human experts? Researchers put this to the test using OpenAI's GPT-4, pitting it against human explainers from WIRED magazine’s popular “5 Levels” series. The study examined how GPT-4 would perform in conversations with college students, given different prompting strategies. One strategy simply provided GPT-4 with the conversation history, while another gave the LLM specific explanatory tactics to use. The surprising result? GPT-4’s explanations were generally preferred over those of human experts! This highlights the struggle many experts face in effectively communicating complex information to broader audiences. However, adding specific explanatory tactics to GPT-4’s prompts didn't always improve the outcome. Often, the more concise explanations from simpler prompts were rated higher, showing that less can be more. The study revealed that while length matters, engagement matters too. When GPT-4 was prompted to ask thought-provoking questions, it created more engaging and enriching learning experiences. This research doesn't suggest that LLMs will replace human experts anytime soon. Instead, it points to a powerful new way that LLMs can support and enhance human communication, creating more accessible and engaging learning opportunities for everyone. The research also shows how important personalization is in effective communication and learning, suggesting exciting future possibilities for AI-driven tools that can adapt to individual learning styles and preferences. Perhaps the most compelling takeaway is the potential for LLMs to not only convey information, but to actively guide and deepen the learning process, creating truly personalized and interactive educational experiences.