  Explainable Artificial Intelligence (XAI) has recently gained a swell of
interest, as many Artificial Intelligence (AI) practitioners and developers are
compelled to rationalize how such AI-based systems work. Decades back, most XAI
systems were developed as knowledge-based or expert systems. These systems
assumed reasoning for the technical description of an explanation, with little
regard for the user's cognitive capabilities. The emphasis of XAI research
appears to have turned to a more pragmatic explanation approach for better
understanding. An extensive area where cognitive science research may
substantially influence XAI advancements is evaluating user knowledge and
feedback, which are essential for XAI system evaluation. To this end, we
propose a framework to experiment with generating and evaluating the
explanations on the grounds of different cognitive levels of understanding. In
this regard, we adopt Bloom's taxonomy, a widely accepted model for assessing
the user's cognitive capability. We utilize the counterfactual explanations as
an explanation-providing medium encompassed with user feedback to validate the
levels of understanding about the explanation at each cognitive level and
improvise the explanation generation methods accordingly.
