In recent years, many interventions have been developed to reduce people’s susceptibility to misinformation, such as media literacy programs or gamified inoculation. Researchers have typically evaluated the effectiveness of these interventions based on a simple discernment measure, i.e., the difference between true and false news ratings. These measures have been shown to be problematic, as they conflate sensitivity with response bias. Here, we will re-assess the findings of this literature by analyzing their data using a Signal Detection Theory (SDT) framework. This will allow us to differentiate between two different kinds of intervention effects. First, the effect on sensitivity, which is the (true) ability to discriminate between true and false news. Second, the effect on response bias, i.e., the extent to which participants generally become more or less skeptical in their accuracy ratings, such as rating all news as false. We will also test potential moderators of these effects, such as the political concordance of the headlines. We will run an Individual Participant Data meta-analysis (IPD) based on a sample of studies that we identified via a systematic literature review following the PRISMA guidelines. We use a two-stage approach: First, we extract individual participant data and run a Signal Detection Theory analysis separately for each experiment. Second, we run a meta-analysis on the experiment-level outcomes. We have already pre-registered the meta-analysis and conducted the systematic literature review. Our findings will provide methodological insights to improve these interventions as well as practical recommendations on which intervention works best and for whom.