  This paper evaluates AI from concrete to Abstract (AIcon2abs), a recently
proposed method that enables awareness among the general public on machine
learning. Such is possible due to the use of WiSARD, an easily understandable
machine learning mechanism, thus requiring little effort and no technical
background from the target users. WiSARD is adherent to digital computing;
training consists of writing to RAM-type memories, and classification consists
of reading from these memories. The model enables easy visualization and
understanding of training and classification tasks' internal realization
through ludic activities. Furthermore, the WiSARD model does not require an
Internet connection for training and classification, and it can learn from a
few or one example. WiSARD can also create "mental images" of what it has
learned so far, evidencing key features pertaining to a given class. The
AIcon2abs method's effectiveness was assessed through the evaluation of a
remote course with a workload of approximately 6 hours. It was completed by
thirty-four Brazilian subjects: 5 children between 8 and 11 years old; 5
adolescents between 12 and 17 years old; and 24 adults between 21 and 72 years
old. The collected data was analyzed from two perspectives: (i) from the
perspective of a pre-experiment (of a mixed methods nature) and (ii) from a
phenomenological perspective (of a qualitative nature). AIcon2abs was
well-rated by almost 100% of the research subjects, and the data collected
revealed quite satisfactory results concerning the intended outcomes. This
research has been approved by the CEP/HUCFF/FM/UFRJ Human Research Ethics
Committee.
