Every day we are surrounded by all sorts of noises that challenge our ability to listen and interpret sounds or conversations accurately. However, the human brain is so powerful that in most cases it is capable of accurately filtering out background noises and processing the sound to give the correct interpretation. This phenomenon, however, varies for every individual, and people with hearing disabilities suffer the most challenges. For example, if such a person is placed in a crowded and noisy place when a sentence is spoken, they may only hear garbled and unintelligible noises and thus be unable to interpret the sound correctly. Research tested how the brain interprets information and this showed that the brain can be ‘primed’ to quickly tune in and understand the language or sound by playing clearer audio versions of the sound before replaying the garbled versions, i.e. the listener learns to understand the sounds.Using this concept, our research focuses on providing a software-based training solution that uses electroencephalogram (EEG) data to determine whether a person with a hearing disability is learning or not. An EEG is a method of recording electrical signals of the brain using electrodes attached to the scalp of a person. The EEG captures a snapshot in time of the electrical activity in the brain and saves the recording as wavy lines. In this paper we present the proposed system framework and also perform experiments to show the feasibility of our approach. Our experiments successfully captured EEG data from the participants when various auditory stimuli are applied and performed preliminary processing on the data to determine whether a user is learning or not.