Toward Adaptive Educational Intervention: Meta-Adaptive Cross-Modal Gating for Few-Shot Personalized Intervention

The rapid evolution of AI-enhanced learning environments has created an urgent need for intelligent educational systems capable of delivering early and personalized interventions under severe data sparsity conditions. This study proposes a Meta-Adaptive Cross-Modal Gating (MACMG) mechanism integrated within a Multimodal Transformer-based Educational Digital Twin framework for early detection of at-risk students. The proposed approach addresses the cold-start problem by introducing a student-specific, meta-learned gating policy that dynamically fuses textual, behavioral, and physiological educational signals. At the core of MACMG lies a bilevel optimization strategy inspired by Model-Agnostic Meta-Learning (MAML), enabling rapid adaptation to unseen learners using only a few initial interaction episodes. The framework generates time-varying modality weights that emphasize informative signals while suppressing noisy channels. To preserve representational capacity and computational efficiency, the adaptive gating mechanism is integrated only into the top two layers of a six-layer multimodal Transformer using a first-order MAML approximation. The personalized representations are propagated toward a risk prediction module and a digital twin state manager responsible for continuously updating learner knowledge states. Experimental results demonstrate that MACMG achieves AUROC scores of 0.821 on StudentLife and 0.834 on DAiSEE, outperforming static multimodal Transformers, conventional fine-tuning approaches, and full-model meta-learning baselines. Furthermore, the proposed framework reduces learner-specific adaptation time to only 0.47 seconds while maintaining robust performance under sparse-data conditions, highlighting its suitability for real-time personalized educational intervention.

@article{Kaa2026,
  title     = {Toward Adaptive Educational Intervention: Meta-Adaptive Cross-Modal Gating for Few-Shot Personalized Intervention},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Houda Kaa and Hanane Allioui and Ilham Oumaira},
  doi       = {10.14569/IJACSA.2026.0170663},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170663}
}