  To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.
