Pictures are a key component in learning. In education when available, images are widely used to teach and discuss concepts. For example images containing, the combination of sun, plant, and water to describe the process of photosynthesis are widely available. However, as learning progresses and concepts get more abstract and complex i.e. quantum computing, images to describe these concepts are less concrete and not necessarily universally understood. One reason for a lack of educationally-purposed images is that drawing abstract concepts require a deep understanding of the concept and visual arts skills. We propose to build an automated algorithm that creates meaningful images for difficult concepts in technology. Advancements in natural language processing (NLP) and text to image generation algorithms (TIG) could allow us to create images for educational activities, thus allowing students to build upon complicated concepts at an accelerated pace. This experimental approach focuses on the quality and mix of data being fed into the TIGs. One advantage of this method is that the training data for the TIG is specific and clean, meaning that the machine already has a strong body of knowledge with which to produce relevant results. The proposed algorithm, written in python, has two principal components: A data feed handler comprised of a [Learning Map][2] and various [text to image generation algorithms (TIG)][2] The poster will demonstrate the outcome of generating an image e.g, quantum using this approach and the tools we used to build the model. [2]: https://github.com/taoxugit/AttnGAN [1]: https://patents.google.com/patent/US20180102062A1/en?assignee=itay+livni&oq=itay+livni