OmniEdu: Open Foundation Models for Learning and Teaching

Educational foundation models must do more than produce correct answers: they must understand where a problem sits in a curriculum, diagnose why a learner is struggling, and choose an appropriate instructional response. Existing educational language models often specialize in either subject problem solving or tutoring, while their training mixtures are commonly organized by source or task and do not explicitly balance these capabilities. We present OmniEdu, an open family of foundation models for K–12 learning and teaching, trained with a capability-oriented instruction-tuning corpus. The corpus combines more than 100 educational resources and general instruction sources and organizes supervision around four complementary capabilities: subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding. A multi-stage pipeline performs deterministic cleaning, semantic auditing and rewriting, taskspecific quality scoring, token-budgeted diversity selection, and pedagogical instruction assignment, yielding 69,999 examples and 15.96M supervised response tokens, including 60,951 educationspecific examples.

Introduction. Large language models are increasingly used in education, but educational usefulness is not captured by answer accuracy alone. A capable learning and teaching assistant must solve a student’s problem, connect it to the appropriate knowledge point and prerequisite structure, identify the misconception behind an incorrect attempt, and select an intervention that advances learning. In other words, an educational model must coordinate what to teach, where the learner is, and how to respond. Treating education as ordinary question answering therefore leaves out the structure that makes tutoring effective. Recent educational models have made progress in individual parts of this problem. Some emphasize subject competence and examination-style problem solving; others target tutoring dialogue, Socratic questioning, or curriculum alignment [4, 10, 22, 29]. However, these capabilities are often developed in separate systems or evaluated in isolation.

Discussion / Conclusion. In this work, we present OmniEdu, a family of open foundation models for K–12 learning and teaching, trained with a capability-oriented instruction-tuning corpus. Rather than organizing training data solely by source or subject, we structure supervision around four complementary educational capabilities: subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding. We construct a compact, high-quality corpus through a multi-stage pipeline that combines deterministic cleaning, semantic auditing and revision, task-specific quality assessment, and diversity-aware sampling. Experiments across 4B, 9B, and 27B models show consistent improvements across model scales. In particular, the OmniEdu family achieves the strongest open-weight results across the curriculum-grounding and pedagogical-tutoring benchmarks, while OmniEdu-27B remains competitive with frontier proprietary models on K–12 problem solving and substantially outperforms existing open-weight educational models.