In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be explored. This thesis first introduces a framework for developing different Artificial Intelligences to conduct autonomous satellite operations for CubeSat satellites. Three different implementations are then compared using Cal Poly's CP6 CubeSat and the University of Tokyo's XI-IV CubeSat to determine which method is most effective.