Motion artifacts degrade MR image quality affecting clinical diagnoses. Although deep learning-based motion artifact correction (MAC) methods show promise, they are limited by the lack of real paired motion-corrupted and motion-free images. We propose a novel frequency-assisted artifact disentanglement learning framework for MAC of MR images. Our approach integrates a frequency-decomposed motion correction network (FDMC-Net) for content-artifact disentanglement over the real unpaired data, coupled with confidence-guided knowledge distillation using simulated paired data. Specifically, considering that motion artifacts are primarily caused by high-frequency k-space misalignment, FDMC-Net decomposes motion-corrupted MR images into low-frequency and high-frequency components and then employs dedicated encoders to disentangle content and artifact features. FDMC-Net is trained by unsupervised cycle-consistent adversarial loss over realistic unpaired data, and confidence-guided knowledge distillation loss by distilling a teacher model trained on simulated paired data. Experiments demonstrate its state-of-the-art performance, with ablation studies confirming the effectiveness of frequency-assisted disentanglement and confidence-guided distillation.