The Role of Artificial Intelligence in Analyzing the Performance of Iraqi Theatrical Actors

AI presents transformative possibilities in the assessment of theatrical performances, allowing for an objective and standardized basis for evaluation. This paper discusses the use of AI models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to review the key performance indicators for Iraqi theatrical actors, such as, movement fluidity, facial expressiveness, speech modulation, and audience engagement. Using a dataset of 500 high-resolution video recordings spanned across different theatrical genres combined with various advanced AI techniques such as motion tracking, facial emotion recognition, and acoustic analysis. Findings show that performers who display more fluidity, facial expressiveness, and variability in vocal inflection engender not only significantly higher audience engagement, but also greater perceived overall effectiveness of performance. Overall, these results show the value potential of AI to connect subjective interpretations of artistic merit to objective, datadriven scrutiny, which could enable more directed actor training, better casting choices, and general advances in the quality productions. It recommends further integration of AI into theatre to expand traditional performance evaluation and aid the development of Iraqi theatrical tradition.