Recently, I’ve been using Unstuck, a tool which has the potential to help students capture key information from lectures more efficiently. I started using this AI platform to get more value out of my lectures by recording them and asking the platform to make notes suitable for the slides. While it ultimately helped me improve my information capture, the initial learning curve was steep, and I found myself struggling to adapt. This led to a decrease in efficiency at the start, which was the opposite of what I was searching for.
This experience parallels what many face when experimenting with Generative AI tools. Like Unstuck, Generative AI has the potential to enhance productivity and learning, but it also requires users to go through a period of trial and error. Whether it’s crafting the right prompts or making sense of the output, the initial phase can feel slow and even counterproductive. Yet, once you pass the initial hurdles, these tools can significantly improve your ability to process large volumes of information and generate useful insights—whether for summarizing lectures, brainstorming ideas, or studying more efficiently. Both tools share another important similarity: customization. Just as I tailored Unstuck to fit my study habits and preferences, AI tools can be fine-tuned through prompts and feedback loops to deliver more relevant outputs. The challenge is learning how to get there.
Looking forward, in my opinion one improvement for the tools would be more intuitive onboarding. With better tutorials and adaptive learning paths, users could overcome early struggles more quickly. By guiding new users step by step, both Unstuck and Generative AI could shorten the frustrating learning phase and deliver their value sooner. In the end, persistence pays off. Even if efficiency feels out of reach at first, sticking with these tools can lead to greater productivity and deeper insights.
