Schedule of Reductions (SOR)

By Tirath Chhatriwala

Overview

An educational financial-aid tool for students and aid staff to review supported Direct Loan Schedule of Reductions scenarios. Run source-backed estimates, compare cases, and identify missing facts or issues that need school review. Results are estimates, not award or eligibility determinations. Schools must verify loan history, aggregate limits, COD status, and final packaging requirements. This independently developed calculator is not endorsed by the U.S. Department of Education.

Tools

advanced_student_estimate

ChatGPT

Run a complete institution-specific V2 scenario through the shared engine and return a student-readable gross estimate with stages, warnings, and external checks. This is decision support, not an award, and it is stateless.

calculate_sor

ChatGPT

Calculate Direct Loan Schedule of Reductions using source-backed rules. Missing facts produce specific follow-up questions, not demo defaults. Returns gross eligibility, term payouts, warnings, and public source links. Use detailLevel=detailed for full calculation stages.

check_loan_limit_exception

ChatGPT

Triage a student's self-reported program-continuity facts against the 2026-27 interim exception conditions. Returns one of school_confirmation_needed, likely_not_available_from_answers, insufficient_information, or school_record_conflict, with the external checks and a copyable aid-office question. This never returns eligible, grandfathered, or approved, never determines eligibility, and never promises a disbursement. Ask any missing question one at a time using the returned nextQuestion. Stateless and public.

compare_sor

ChatGPT

Run two complete scenarios independently through the shared SOR engine and compare numeric results, term payouts, warnings, and authority status. Do not include student identifiers.

list_scenarios

ChatGPT

Return public SOR fixtures with inputs, expected outputs, source references, sourceStatus, and assertionLevel. Directional or preliminary fixtures are regression examples, not Department-certified dollar oracles. Use them to discover input shapes and compare supported behavior.