We investigate whether a teacher-student curriculum learning approach using a teacher network with a simpler structure than the student network can achieve better results at meta-learning. The goal of meta-learning is to learn from a set of tasks, and then perform well on a new, structurally similar but unseen task with minimal retraining. Instead of sampling uniformly from all data to create the training batches, the curriculum-learning approach aims to create a sequence of mini-batches that enhances the training process, also known as a curriculum. During teacher-student curriculum learning a "teacher" network is trained in the standard manner, and then its outputs are used to order the training samples by difficulty and categorise them into mini-batches. This curriculum is then used to train the "student" network. Previous teacher-student models either had pre-trained more complex teachers, or teachers with the same structure as the student network. We investigate whether a teacher network with a simpler structure can also increase accuracy, while preserving computational resources. We find that using such a curriculum worsens performance compared to not using any curriculum at all.