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This is an integrative review that address the question, "What makes for agood explanation?" with reference to AI systems. Pertinent literatures arevast. Thus, this review is necessarily selective. That said, most of the keyconcepts and issues are expressed in this Report. The Report encapsulates thehistory of computer science efforts to create systems that explain and instruct(intelligent tutoring systems and expert systems). The Report expresses theexplainability issues and challenges in modern AI, and presents capsule viewsof the leading psychological theories of explanation. Certain articles standout by virtue of their particular relevance to XAI, and their methods, results,and key points are highlighted. It is recommended that AI/XAI researchers beencouraged to include in their research reports fuller details on theirempirical or experimental methods, in the fashion of experimental psychologyresearch reports: details on Participants, Instructions, Procedures, Tasks,Dependent Variables (operational definitions of the measures and metrics),Independent Variables (conditions), and Control Conditions.

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI