Mentra AI

Students today have access to countless productivity tools and AI assistants, yet many still struggle with one fundamental question: "What should I do next?" Existing tools help organize tasks, while general AI models provide answers when prompted. However, neither proactively guides students through the complex decisions involved in balancing assignments, exams, sleep, and personal well-being.

To address this, we built Mentra (working name), an autonomous AI study agent that goes beyond conversation. Instead of simply responding to prompts, it continuously observes a student's academic context, reasons over competing priorities, generates personalized study plans, and adapts recommendations as progress changes. By integrating scheduling, personalized planning, focus sessions, and progress tracking into one workflow, our goal is to help students make better decisions rather than simply manage tasks.

Throughout this project, we explored how AI agents differ from traditional chatbots. We designed Mentra around the AI agent lifecycle—Observe, Reason, Plan, and Act—allowing it to proactively support students instead of waiting for instructions. This taught us the importance of context awareness, personalization, and autonomous decision-making when building AI-powered applications.

One of our biggest challenges was designing an experience that felt genuinely agentic rather than simply wrapping a large language model in a user interface. We focused on creating a seamless journey where the AI understands the student's goals, generates actionable plans, and continuously adapts based on changing workloads and progress.

Ultimately, our project demonstrates how AI agents can simplify everyday student life by transforming overwhelming academic information into intelligent, personalized decisions that improve learning, reduce burnout, and promote long-term success.