The purpose of this talk is to focus on the implications of COVID-induced/ accelerated changes on the teaching and training of interpreters. Specifically, this talk will begin with an experiment that investigated how automatic speech recognition (ASR) technologies could help improve simultaneous interpreting (SI) quality. With the increase in the use of English as lingua franca (ELF), interpreters will face speakers with different speech features. Quality of SI may suffer because of source speeches with unfamiliar accents. Understanding accented speech could be cognitively challenging as interpreters need to spend more effort. The increase in cognitive load resulted from trying to comprehend accented speech may lead to omissions, errors and disfluencies in the target language renditions. Experiment results showed that ASR might have the potential to improve the accuracy of SI rendition as the accuracy scores of the captioned segments were higher than those of the non-captioned segments. Because of the speaker’s unfamiliar accent, the participants might have had to exert extra effort to understand what the speaker was saying, which could have led to increased errors and omissions. However, the amount of effort exerted by the interpreter to understand what the speaker said may also be reduced by the use of captions. As a result, the rendition of captioned segments tended to have higher accuracy scores than the rendition of non-captioned segments. Machine-aided interpreting could be realized on platforms that provide RSI also provide caption generated by ASR. Remote simultaneous interpreting (RSI) is likely to stay post COVID. Online conferences may democratize conference attendance of speakers and attendees who might have otherwise not been able to attend some conferences because of various constraints. With wider participation of individuals from different backgrounds, speakers’ accents may be even more diverse. T&I curriculums should introduce machine-aided interpreting subjects to leverage new technologies.