ACComplish
An AI study-execution copilot that turns exact study scope, current readiness, deadlines, and daily capacity into a focused Study Line with curated resources, execution tracking, and explicit adaptive recovery.
Problem
Online students often do not lack content; they lack a reliable execution path through too much of it. Searching, switching resources, over-planning, and rebuilding a static schedule after it slips can consume the attention that should go into studying.
Approach
ACComplish uses a PLAN → EXECUTE → REPORT → ADAPT loop. Structured onboarding captures the immediate academic goal, exact supplied topics, readiness, target date, and daily capacity. AI generates a bounded Study Line, Tavily-backed search curates a deliberately small Resource Lane, and Done / Partial / Skipped execution evidence can drive an explicit recovery plan when the original schedule no longer fits reality. Local validation and deterministic degraded-mode fallbacks protect key product constraints when model output is unusable.
Outcome / Learning
A production-deployed hackathon MVP that demonstrates a complete study-execution loop rather than a generic tutoring chatbot: context-aware planning, constrained resource discovery, progress reporting, and explicit recovery when execution diverges from the original plan. The product was shipped as the Pixel Forge AI Hackathon submission and remains available as a live application and public repository.
Key Features
- Structured academic onboarding and exact supplied-topic scope
- Readiness-aware short-horizon Study Line generation
- Daily time-capacity enforcement
- Curated Primary, Backup, and Practice Resource Lane
- Done, Partial, and Skipped execution tracking
- Explicit Adaptive Recovery based on execution evidence
- Bounded AI fallback and deterministic degraded-mode planning
- Local validation of topic scope, dates, capacity, and plan coherence
- Focus Mode with system, light, and dark themes
- Responsive phone, tablet, and desktop experience