Vision-based control in robotics offers versatile automation; however, accessible educational platforms for exploring its integration with AI are still limited. This paper addresses this gap by presenting a small, 3D-printed parallel SCARA robot designed specifically for educational purposes. We provide details on its construction and demonstrate its application in laboratory exercises, which cover inverse and forward kinematics, vision-based tip positioning, and object detection. Notably, we investigate both supervised (using convolutional neural networks) and unsupervised (through autoencoder latent space exploration) approaches for classifying faulty parts. The unsupervised method achieved high performance, with a precision of 1.00, recall of 0.96, and an F1-measure of 0.98, which is comparable to the supervised approach that yielded a precision of 0.98, recall of 0.97, and an F1-measure of 0.97. This work contributes to the development of a low-cost platform and demonstrates the effectiveness of unsupervised AI techniques for vision-based robotic fault detection in educational settings, paving the way for more advanced AI-integrated robotics curricula.