Learning objectives (LOs) are a pillar of course design and execution and thus a focus of curricular reforms. This study explored the extent to which the creation and usage of LOs might be facilitated by three leading chatbots: ChatGPT-4o, Claude 3.5 Sonnet, and Google Gemini Advanced. We posed three main questions, as follows: "question A": when given course content, can chatbots create LOs that are consistent with five best practices in writing LOs?; "question B": when given LOs for a low level of the revised Bloom's taxonomy, can chatbots convert them to a higher level?; and "question C": when given LOs, can chatbots create assessment questions that meet six criteria of quality? We explored these questions in the context of four undergraduate courses: Applied Exercise Physiology, Human Anatomy, Human Physiology, and Motor Learning. According to instructor ratings, chatbots had a >70% success rate on most individual criteria for questions A--C. However, chatbots' "difficulties" with a few criteria (e.g., provision of appropriate context for an LO's action, assignment of an appropriate revised Bloom's taxonomy level) meant that, overall, only 38.3% of chatbot outputs fully met all criteria and thus were possibly ready for use with students. Our findings thus underscore the continuing need for instructor oversight of chatbot outputs but also illustrate chatbots' potential to expedite the design and improvement of LOs and LO-related curricular materials such as test question templates (TQTs), which directly align LOs with assessment questions.