Overview:
Picture the smartest, chillest academic advisor living in your phone. You dump in your major requirements (or just your catalog PDF), and it auto-builds a semester-by-semester plan that actually makes sense: no hidden prereq traps, no time collisions, no panic at registration. You can toggle different goals—fastest to graduate, lightest workload, GPA-maximizing—and it shows the trade‑offs with receipts. When it’s time to pick sections, it flags capacity risk and nudges you toward professors/sections that fit your style (based on grading trends, workload signals, and student preferences), then syncs the whole thing to your calendar. Start lean with CSV/manual imports and a drag‑and‑drop builder; add SIS/LMS hookups and richer professor analytics once you’ve nailed product‑campus fit. Think Coursicle/Schedule Planner meets an explainable, AI-powered degree audit you actually trust.
The Trends:
Rapid institutional adoption of AI-driven academic advising platforms that scale personalized guidance, automate routine advising tasks, and augment advisor capacity (chatbots, automated degree planning, career-match features). (1, 2)
Degree-audit and planning systems are being modernized and integrated with campus SIS platforms (PeopleSoft/Banner) and third‑party planners (Stellic, Navigate) to support automated course scheduling and graduation-path simulations. (3)
Wider use of predictive analytics and data‑driven interventions to identify at‑risk students early and prescribe targeted supports, often combined with nudges and CRM-style outreach. (4, 5)
Personalized course and instructor recommendations (including professor ratings and course-fit signals) are being incorporated into planning tools so students can choose sections that match learning style, workload, and graduation timelines. (6, 7)
Growing regulatory and privacy scrutiny—colleges and vendors must address FERPA, vendor contracts, data retention, and model training safeguards as AI tools ingest student records and behavioral data. (8, 9, 10)
Your Answer:
AI-driven course planner that ingests degree requirements (CSV or SIS integration), runs a degree audit, and auto-generates semester-by-semester schedules to show the fastest/healthiest paths to graduation.
Automatically flags prerequisites, time conflicts, overload risk, and course capacity issues; simulates multiple graduation scenarios (fastest, highest-GPA, low-workload) so students can compare trade-offs.
Suggests professors and sections based on fit (grading trends, workload, student preferences and reviews), helping students pick classes that match learning style and graduation goals.
MVP approach: allow manual/CSV degree imports + calendar sync, a drag‑and‑drop schedule builder, conflict detection, and a simple recommender; then add SIS/LMS integration and richer professor analytics.
Solves pain points: eliminates confusing degree audits and advisor queues, reduces missed prereqs and delayed graduations, and saves hours of registration planning—boosting student confidence and on‑time completion.
Go‑to‑market & monetization: freemium for basic planning, paid tier for personalized professor insights/priority planning and advisor exports; campus contracts and orientation partnerships for rapid adoption.
Built for trust: transparent, explainable recommendations, editable plans for advisor handoff, FERPA‑aware data handling, and audit logs so students and schools can validate decisions.
Your Roadmap:
No-code quick MVP: build a Google Sheet + Apps Script front end that stores degree rules and course catalog.
Use GPT (via OpenAI API) to parse degree requirement text / PDF into structured requirements (major, cores, electives).
Create a scheduler that simulates semesters by assigning courses to terms in the sheet and flags time conflicts (simple time blocks).
Expose a lightweight UI with Glide or Softr to let students import degree PDF, edit preferences, and see a proposed plan.
Add professor suggestions by pulling short scraped summaries or crowd-sourced notes stored in a Sheet (manual CSV import for MVP).
Iterate from early users (friends / student orgs) and capture common edge-cases to refine parsing prompts and scheduling rules.