AI Support for Struggling Learners

  Summary by Ruan et al., (2024)

This study reimagines teacher support through intelligent technology. Ruan et al. developed a reinforcement-learning (RL) tutor that provides adaptive feedback during math lessons, functioning as a digital co-teacher. The RL model most benefited students with lower pretest scores, tailoring guidance to their weaknesses. Explainable-AI components let teachers interpret system logic and adjust classroom strategies. Validation with a new student cohort confirmed consistent gains, demonstrating scalability. Rather than replacing teachers, the model extends their reach—allowing real-time personalization while teachers focus on motivation and conceptual clarity. By merging teacher intuition with data-driven insight, AI tutoring enhances classroom differentiation. Teachers can identify learners who need extra help and provide timely intervention. Such systems may reduce instructional inequality in large classrooms by automating low-level feedback. Ultimately, teacher–AI collaboration can transform classrooms into adaptive, student-centered spaces where support is immediate and continuous.

Reference:  Ruan, et al. (2024) Reinforcement learning tutor better supported lower performers in a math task. Machine Learning, 113(12), 3023–3048. https://doi.org/10.1007/s10994-023-06423-9

Suggested Citation: Ross & Malar (2025). AI Support for Struggling Learners. Excellent Educator, 2(21), 1.