Expert Systems Usage and Knowledge Acquisition: An Empirical Assessment of Analogical Reasoning in the Evaluation of Internal Controls

This article reports the results of a laboratory experiment that investigates the effectiveness of expert systems' explanatory capabilities in transferring knowledge to novice auditors. Three types of expert systems were developed and knowledge transfer was assessed through three outcome measures: accuracy, mean absolute error (MAE), and confidence. Findings indicated that expert system type impacted on the outcome measures. In addition, overconfidence was found to be significantly different for the expert system incorporating analogical reasoning techniques than for the other treatment conditions. The no-explanation group had the highest level of overconfidence, followed by the explanation group. The findings suggest that an expert system designed to incorporate analogical learning techniques along with the explanation can enhance knowledge transfer compared to a system that just provides explanations.