Training programs are a popular method, in industry globally, to increase awareness of desired concepts to employees and employers and play a critical part in changing or supporting performance improvements. The predominant method to assess the effectiveness of training programs is to have the participants answer Multiple Choice Question (MCQ) and True/False (T/F) questions after the training; however, the metrics typically used to report the outcome of such assessments have drawbacks that make it difficult for the trainer and organization to easily identify the concepts that need more focus and those that do not. This study introduces measures of the Assessment of Training Effectiveness Adjusted for Learning (ATEAL) method, which compensate the assessment scores for prior knowledge and negative training impact in quantifying the effectiveness of each concept taught. The results of this method are compared to the results of the most popular methods currently used. A simulation of various scenarios and the training effectiveness metrics that result from them is used to illustrate the sensitivity and limitation of each method. Results show that the proposed coefficients are more sensitive in detecting prior knowledge and negative training impact. Additionally, the proposed ATEAL method provides a quick and easy way to assess the effectiveness of the training concept based on the assessment results and provides a directional guide on the changes that need to be made to improve the training program for the participants. A companion paper expands the concepts using results from actual training sessions in multiple industries. [For Part II, see EJ1270913.]