For the design evaluation of automated vehicles (AVs), eyetracking is often used. However, the conditions required for an eyetracking study, such as expensive and complex technology or the necessity for participants to be present on-site, limit this research method. This paper investigates alternative methods to traditional eyetracking to collect data on participants’ fixations on stimuli through online studies. The CodeCharts method is examined in the context of observing an AV with an external human-machine interface (eHMI), and the results are compared with a reference study using a remote eyetracker. Additionally, an initial test evaluates whether an AI-based assessment of the stimuli using ChatGPT produces similar results. The CodeCharts method demonstrates similar results in the heatmaps to the reference study in this application and confirms its findings regarding the interactions between eHMI and visible vehicle sensors on the exterior. For the AI evaluation, it becomes clear that the tested combination of AI model, stimuli, and prompts is not effective and requires significantly more extensive preparation.