Quality Meta-Framework: MLOps, NLP and learning analytics - Solís-Céspedes, Jacqueline (2026)
A theoretical proposal is presented for an integrated meta-framework for educational quality management based on Natural Language Processing (NLP) and Learning Analytics (LA), designed to enable the scalable, traceable, and auditable evaluation of teacher-student interactions in digital environments. The conceptual architecture articulates NLP and LA to transform academic discourse and behavioral metadata into key performance indicators (KPIs), aligned with the pedagogical, technical, social, and psychological dimensions of educational quality.The model is structured under a cyclical architecture of three phases: Direction, Operation, and Optimization that integrates strategic design, technical deployment, and continuous calibration. It also incorporates a hybrid annotation strategy that combines the scalability of Large Language Models (LLMs) with periodic human validation, ensuring bias control, semantic consistency, and institutional oversight.Its main contribution lies in the explicit incorporation of an Algorithmic Governance (AG) framework, based on MLOps practices, which operationalizes quality through auditable metrics, validation protocols, and mechanisms for monitoring data and model drift. In this way, the proposal transcends approaches based on isolated KPIs and establishes a second-order architecture that integrates pedagogical evaluation, technical control, and institutional responsibility, promoting transparency, accountability, and continuous improvement in AI-based educational systems.