Teachers do not always know how well their techniques work. They can hand out tests and ask questions, certainly, but it is not always clear who is at fault if the message does not get through. AI may do the trick soon, although. Researchers at Dartmouth College have made an ML algorithm that calculates activity all over your brain to decide how well you know a particular concept.
The group began out by having intermediate and rookie engineering students both answer questions about pictures as well as take standard tests while sitting in an fMRI scanner. From here, they had the algorithm create “neural marks” that can forecast performance of a student. The more specific parts of the brain lit up, the simpler it was to tell if or not a student understood the concepts.
You are not about to get brain scans among classes, and there are restrictions to the current study. For one, Dartmouth aimed on STEM learning. It’s not certain if your brain might react in the same manner in a literature period. The neural marks also apply only to narrow expressions of knowledge. This can, on the other hand, assist teachers refine their classes by knowing methods that resonate with most students prior to results of exam come in. Do not be shocked if school is ultimately much more engaging.
On a related note, there is no one correct method to develop a robot, just as there is no singular methods of imparting it with smartness. Previous month, the media spoke with Nathan Michael, the director of the Resilient Intelligent Systems Lab and associate research professor at Carnegie Mellon University. His work involves combining and stacking different piecemeal capabilities of a robot together as it learns them into a merged AGI (artificial general intelligence).