Student dropout in distance learning represents a critical socio-educational challenge, with attrition rates exceeding 30% in many institutions, leading to significant financial losses for learners and systemic inefficiencies. While predictive analytics has gained traction, traditional machine learning approaches (e.g., logistic regression, decision trees) and even recent deep learning models (e.g., LSTMs, transformers) face three fundamental limitations:
(1) inability to contextualize unstructured data (e.g., forum comments, emails) against domain-specific knowledge,
(2) reliance on manual feature engineering for sparse behavioral traces (e.g., login frequency, quiz attempts), and
(3) poor interpretability for actionable pedagogical interventions.

To address these gaps, we propose EduGuard, a novel AI framework that synergizes four advanced paradigms to redefine dropout prediction:

Retrieval-Augmented Generation (RAG):
Dynamically grounds student-generated text (e.g., discussion posts, feedback) in institutional knowledge bases (course syllabi, past successful interactions) using a dual-encoder architecture, enabling context-aware sentiment and intent analysis.

Multi-Strategy Prompt Engineering:
Combines chain-of-thought prompting to elicit self-reflective sentiment reasoning (e.g., "Describe your academic challenges and emotional state step-by-step...") and dynamic few-shot prompts tailored to demographic subgroups, improving sentiment F1-score by 15% over static templates.

Contrastive Behavioral Pretraining:
Employs SimCLR-derived contrastive learning on raw interaction logs (clickstreams, video-watching patterns) to generate robust, low-dimensional embeddings without manual feature curation, reducing engineering overhead by 60%.

Cross-Modal Graph Fusion:
Integrates textual, behavioral, and demographic modalities via a heterogeneous graph neural network (HGNN), where nodes represent students, course elements, and temporal sessions, with edges weighted by engagement metrics.

Evaluated on a longitudinal dataset of 18 543 distance learners (3 universities, 2020–2023), EduGuard achieves 86.4% accuracy (95% CI ±1.2%) in predicting dropout 6–8 weeks in advance, surpassing XGBoost (82.1%), temporal transformers (83.7%), and graph-based baselines (84.2%). Crucially, it demonstrates 19% higher precision (85.3% vs. 71.8% for BERT) in identifying high-risk students, minimizing false alarms. The AUC-ROC reaches 0.913, outperforming state-of-the-art multimodal architectures (e.g., TCN+BiLSTM: 0.874) by 4.5%. Ablation studies reveal each component’s contribution: removing RAG drops accuracy by 5.2%, while deactivating contrastive pretraining increases feature engineering costs by 73%. Qualitative analysis shows EduGuard’s interpretability: for a struggling learner, it retrieves analogous successful student cases and highlights "declining forum participation (-44%) coinciding with negative sentiment spikes in assignments" as key risk factors.

This work makes three key contributions:
- Methodological: Demonstrates how orchestrating RAG, prompt engineering, and contrastive learning overcomes longstanding data-scarcity and context-blindness issues in educational AI.
- Practical: Reduces false-positive interventions by 31% compared to institutional baselines, enabling targeted support.
- Theoretical: Establishes that cross-modal alignment of temporal, textual, and behavioral signals is critical for modeling academic perseverance.

Keywords: Student dropout prediction, Distance learning analytics, Retrieval-Augmented Generation (RAG), Prompt engineering, Contrastive learning, Cross-modal fusion, Educational AI, Interpretable machine learning, Behavioral analytics, Early warning systems, Heterogeneous data integration, Precision education.