Here, I developed an integrated environment to serve as a possible assistant in the educational process. Also, I showed how a ANN engine can store sub-symbolic knowledge and how a rule extraction module can transform it into symbolic knowledge, in order to provide useful information and assistance during the teaching process, which can deal only with symbolic knowledge.
I presented a case of stock exchange development study, as an application available on my NNKEE.
The advantage provided by my system is that it gives teachers a chance to significantly improve their teaching materials and/or style by combining the symbolic knowledge of the domain theory with the rules extracted from the empirical sub-symbolic knowledge stored in ANNs trained on examples, and to add practical knowledge, structured in a rigorous way, to their pure theoretical knowledge.
For further work an enhancement of this environment would be interesting, by adding more modules for rule extraction. Also, different teaching fields can be added to the system.
Finally, it would be useful to make this NNKEE environment available on the Internet, for an easy access for teachers from everywhere.
For the Educational field, higher order (meta-) knowledge is important. On the present example, such knowledge processing as suggested does not yet exist. This item can belong to future research.
From the response of the questioned experts, such a system seems to be required, so further developments should be pursued. I mention that as a result of this line of research a Romanian-Japanese cooperation with the theme "Knowledge Extraction from Neural Networks" has started. This project will take about 2-3 years.