Blog posts

Texture and Pattern Repetition: AI sometimes struggles with complex textures or patterns, leading to noticeable repetition or awkward transitions. Students should look for unnatural patterns in textures like hair, skin, clothing, or background elements.

Despite their increasing use, there's limited evidence that AI-generated avatars significantly improve learning outcomes.

A study by Sung and Mayer (2012) suggests that any graphic in a learning experience will fall into one of these three categories: Instructive images: These visuals directly support learning and facilitate essential cognitive processing of core concepts. For example, a diagram illustrating Porter’s Five Forces can help students better understand this business strategy framework. Decorative images: These graphics enhance aesthetics but don’t influence learning. For example, an image of a business handshake can be visually appealing but won’t support or obstruct students’ understanding of negotiation strategies. Distracting images: Sung and Mayer call this category “seductive” images. While these visuals may relate to the topic, they impede learning because they require extraneous cognitive processing. As an example, consider a complex organizational chart of a full corporation in a lesson on team leadership. The image connects broadly to the lesson but also highlights a lot of irrelevant details, distracting students from the key concepts.

While LLMs generate text one word at a time, always moving forward, diffusion models start with random static and transform the entire image simultaneously through dozens of steps. It is like the difference between writing a story sentence by sentence versus starting with a marble block and gradually sculpting it into a statue, every part of the image is being refined at once, not built up sequentially.

But what makes diffusion models interesting is not their increasing ability to make photorealistic images, but rather the fact that they can create images in various styles.

Unlike diffusion models that transform noise into images, multimodal generation lets Large Language Models directly create images by adding tiny patches of color one after another, just as they add words one after another. This gives AIs deep control over the images it creates.