The spread of AI-embedded systems involved in human decision making makes it critical to build these systems according to trustworthiness standards. To understand whether this goal was achieved, users' trust in these systems must be studied. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. To get an overview of the current practices in the experimental protocols for studying trust in the context of AI-assisted decision making, we conducted a systematic review of such papers. We annotated, categorized, and analyzed them along the constitutive elements of an experimental protocol (i.e., participants, task). Drawing from empirical practices in social and cognitive studies on human-human trust, we provide practical guidelines and research opportunities to improve the methodology of studying Human-AI trust in decision-making contexts. In this workshop, we would like to start the discussion about how these guidelines and research questions can be used in the laboratory and in the wild.