Agentic Artificial Intelligence Model for the Integrated Management of School Absenteeism
DOI:
https://doi.org/10.23824/ase.v0i44.1027Downloads
Abstract
School absenteeism continues to be managed in a fragmented and reactive manner, which hampers prevention and reduces equity across schools. This paper presents an agentic artificial intelligence model designed to integrate dispersed data, automate operational tasks and enhance coordination among educational stakeholders through a multi-agent architecture grounded in the MAPE-K cycle and supported by human oversight at critical points. The model is developed on the basis of a structured narrative review of scientific and regulatory literature (2000–2025) and is organised into twelve functional modules that incorporate ethical safeguards, enhanced traceability, interoperability standards and API-based integration. The proposal is implemented through a multilevel architecture encompassing the school, local area and district levels, aligned with existing educational protocols and the requirements of the GDPR and the EU AI Act. The system automates the ingestion and analysis of attendance data, generates early-risk alerts, supports personalised planning and coordinates interventions among teachers, families and educational services. It integrates HITL oversight mechanisms, a framework of key performance indicators and a phased implementation roadmap. The findings indicate high technical and organisational feasibility, although dependent on data quality, potential algorithmic bias and the need for robust institutional governance. The model strengthens early detection and evidence-informed decision-making, while maintaining human supervision to ensure equity, transparency and public accountability.
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Agentic artificial intelligence, multi-agent systems, school absenteeism, educational governance; human-in-the-loop supervision (HITL), organizational learning, ethical and regulatory compliance, equityLicense
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