A prespecified adaptive plan involves automating the analysis of interim clinical trial data and adjusting elements of the trial in response. In implementing these plans, we experience random highs and lows in the data, adjacent doses of a drug with drastically different results, and lots and lots of uncertainty. To facilitate training in adaptive trials, newcomers need to see data as it might accumulate within a trial and attempt to make design decisions based on that data. To this end we have created ANTICS, a free public R/Shiny based tool that guides a user through a single adaptive trial. ANTICS has modules for dose escalation, dose finding, enrichment, and staged/seamless designs. Repeated plays of ANTICS introduce the idea of simulation and emphasize how the same rules can produce different results when faced with random data. ANTICS has modules for dose escalation, dose finding, enrichment, and staged/seamless designs. A scoring system guides the decision making, emphasizing real world incentives such as getting a correct arm into phase 3 or penalizing players for running a phase 3 in a poor arm. We hope it is a valuable resource for anyone beginning an exploration of adaptive trials.