Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.

We introduce a synthetic benchmark and decision-support framework for qualified educational capacity planning.

The model is a stylized single-institution service system with heterogeneous support-demand categories, backlog-only dynamics, continuous preparation states with hard threshold qualification and decay, and capacity-consuming training.

The benchmark includes seed-controlled scenarios for announced and surprise new support categories, staff absences, and demand surges; exact feasibility discipline; declared per-policy information sets; requalification and greenfield-qualification counters; access-dispersion metrics; replay checksums; and paired statistics.

We compare service-only, reactive, static-insurance, water-filling, and rolling-horizon mixed-integer controllers, with an attribution chain separating service planning, qualification maintenance, and acquisition, plus a perfect-foresight reference.

The central result is a regime map governed by whether a newly required qualification can be acquired within the controller's reaction reach.

When it can, the closed-loop controller wins across the core and adversarial suites, with value concentrated in just-in-time qualification acquisition.

When the training lag exceeds the horizon, lean static insurance wins structurally, and a reactive trainer that starts after onset can be worse than no training.

Backlog perishability shifts this boundary without erasing either regime.

EduCapacity Studio reproduces exported scenarios bit-for-bit.

All evidence is stylized and synthetic; the framework makes no claims about real student outcomes, compliance, or individual placements.