The challenge

University students face a critical challenge: they make major academic decisions (course load, study strategy, time allocation) with incomplete information about workload sustainability and burnout risk. Current tools—syllabi, course schedules, calendar applications—provide no predictive insight into whether a chosen study approach will succeed or lead to burnout. Key Pain Points: Impact: Over 60% of university students experience significant academic stress and burnout during their studies.

The approach

SwastueAI addresses this problem through a multi-agent AI simulation platform that predicts academic outcomes and recommends optimal study strategies. Core Approach Three Independent AI Agents: Each agent models a distinct study strategy and simulates the entire semester independently: Automated Syllabus Intelligence: The platform uses LLM-powered parsing to extract course structure, assignments, deadlines, and workload distribution from PDF/CSV syllabi in seconds. No manual data entry required.

What we did

• //001Consistent Agent
• //002Intensive Agent
• //003Balanced Agent
• //004GPA trajectory (cumulative grade impact)
• //005Stress levels (workload intensity, deadline clustering)
• //006Sleep hours (cognitive performance indicator)