Retrieval-Augmented Generation (RAG) systems promise to reduce large language model hallucinations by grounding responses in verified sources, which makes them candidates for supporting students at academic risk; however, evidence on their use for institutional risk monitoring remains scattered. This systematic review, conducted following PRISMA guidelines across eight databases, identified 135 studies published between 2020 and 2026, of which 100 met the inclusion criteria, complemented by a scientometric analysis of the scholarly output. Results reveal a recently formed corpus —two thirds of the studies belong to the 2025-2026 period— and a bifurcation between two bodies of literature that rarely cite each other: predictive academic risk analytics, active since 2020, and conversational artificial intelligence, whose RAG implementations do not appear before 2024 yet reach 38% of the corpus. Both traditions rely on disjoint metric families: no study jointly evaluates risk prediction accuracy together with chatbot reliability, and the two mitigation repertoires —class imbalance treatment and generative text control— never coexist within a single design. The review concludes that no hybrid architecture coupling risk prediction with retrieval-augmented generation exists to date, and proposes a research agenda focused on designing such architectures, building joint evaluation frameworks, and combining both mitigation repertoires under teacher oversight.
Abstract Retrieval-Augmented Generation (RAG) systems promise to reduce large language model hallucinations by grounding responses in verified sources, which makes them candidates [...]
The integration of Artificial Intelligence (AI) in education has transformed learning personalization and student performance management. This study aimed to analyze the technologies, implementation strategies, and reported outcomes in recent scientific literature regarding AI workflows and intelligent educational agents. Under the PRISMA methodology, a systematic review was conducted on 70 studies published between 2020 and 2026 in high-impact databases. The findings reveal a growing trend toward the use of learning analytics, multi-agent systems, and academic chatbots—tools that have proven effective in early risk detection and automated monitoring. However, critical barriers were identified, such as the lack of technological interoperability and ethical dilemmas in data governance. It is concluded that while these technologies are promising for the evolution of digital environments, their success depends on developing standardized architectures and conducting longitudinal studies to validate their long-term pedagogical impact.
Abstract The integration of Artificial Intelligence (AI) in education has transformed learning personalization and student performance management. This study aimed to analyze the technologies, [...]
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Retrieval-Augmented Generation (RAG)-based chatbots for monitoring and supporting student academic progress in educational institutions: a systematic literature review 
