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The inspection, analysis, and modification of robot programs in series production processes is a challenging recurring task in the automotive industry. Especially the holistic analysis of detected defects and the subsequent derivation and application of corrective measures to the underlying robot program is a complex task requiring extensive knowledge in multiple fields. Existing scientific literature has demonstrated that the utilisation of Augmented Reality (AR) assisted robot programming systems (ARRPS) improves programming efficiency as well as intuitiveness when compared to traditional programming methods. However, the accuracy especially of mobile AR devices limits the applicability of AR in industrial applications. We propose to leverage the iterative nature of the optimisation process common in series production and utilise the visual capabilities of the human worker to define corrective measures based on the inspected real process result. This mitigates the impact of limited tracking accuracy as the optimisation is based on the visual perception of the user and thus decoupled from the accuracy of the employed AR device. We implemented our system based on the requirements of a sealing workstation of the Mercedes-Benz Group in Germany. A conducted experiment validates the functionality and indicates that the presented strategy of iterative AR assisted robot programming enables optimisations with a similar accuracy as the Teach-In programming as well as a significantly reduced programming time. This suggests that utilising an ARRPS can be highly beneficial in the inspection, analysis, and modification of sealing applications common in automotive series production processes. As the presented iterative AR assisted robot programming is neither limited to sealing nor to the automotive sector it can be adapted for various robot-automated applications like path-welding, gluing, or painting and can extend its utility to related sectors such as aviation or the marine industry. [ABSTRACT FROM AUTHOR]