2026-08-24T11:05:36Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:26741 2026-07-22T11:05:59Z ddc:004 open_access

Ogma: An Intelligent Platform for Anticipating Academic Failure and Recommending Recovery Measures (Short Paper) Queirós, Ricardo Generative AI Educational chatbot Pedagogical agent Gamification Moodle Retrieval-augmented generation Self-regulated learning The growth of class sizes, the increasing complexity of course content, and the multiplicity of digital platforms make it progressively harder for instructors to identify, in a timely manner, students who are at risk of academic failure. This short paper presents the concept of Ogma, a learning analytics application with artificial intelligence components designed to anticipate signs of academic decline and suggest personalized recovery measures. The proposal combines heterogeneous data - student-produced work, grade records, forum messages, and logs from other educational applications - to build a dynamic risk profile. Based on this profile, the system provides instructors with a dashboard containing graded alerts, explanations of risk factors, and pedagogical recommendations generated by a pipeline that combines machine learning, retrieval-augmented generation, and large language models. The paper presents the motivation for the problem, synthesizes related work, and describes the design of a reference architecture for Ogma. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ricardo Queirós 2026 Is Part Of OASIcs, Volume 145, 7th International Computer Programming Education Conference (ICPEC 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICPEC.2026.4 urn:nbn:de:0030-drops-267414 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2026.4 eng https://creativecommons.org/licenses/by/4.0/legalcode