C⁴-EDS:Planning Before Prompting for Educational Dialogue Synthesis

Synthetic-data systems can now specify how individual examples are generated in considerable detail, while the data space formed by those examples may remain implicit. In educational dialogue synthesis, rich source, task, role, cognitive, and pedagogical conditions do not by themselves expose which semantic combinations are legal or how dataset composition should be planned. We formulate this gap as a generation-context representation problem and introduce C⁴-EDS. Before a complete context is assembled, C⁴-EDS decouples Content and Task into independently addressable semantic objects, makes their legal relations explicit, and thereby forms an enumerable and allocatable planning space. Selected relations are compiled into executable Jobs and integer candidate workloads. Representation, execution, and quality governance retain distinct responsibilities, while planning identities persist through synthesis and filtering so that retained data can be audited against the original plan. In K–12 Classical Chinese tutoring, 5,628 theoretical Content–Task combinations yield 2,807 legal Pairs and 5,614 executable Jobs. Under a matched workload, explicit reasoning improves structural realization without monotonically improving pedagogical quality; full Job-level planning reaches an automatic hard-pass rate of 88.28% and the highest mean continuous human rating. Downstream training further exhibits a target-dependent Process–Result trade-off. High-quality synthetic data therefore depends on how the generation space is represented, planned, and governed, as well as on how each example is generated.