This experience report describes a partnership between community college faculty and learning scientists to co-design Intelligent Tutoring Systems (ITSs) addressing challenges in cybersecurity workforce training. Our co-design approach combined collaborative reflection on student difficulties from prior course offerings with systematic curricular analysis to identify high-impact intervention points. We targeted two challenge areas: strengthening students' ability to contrast key cybersecurity taxonomies, and providing realistic hands-on training without costly infrastructure. The resulting ITSs include: one employing exercises that scaffold comparison of conceptual categories, and another using lightweight simulations to provide experiential learning while circumventing typical cost and time overhead. Both systems incorporate instructional principles grounded in learning science research, including evidence-based features associated with ITS efficacy such as timely hints and feedback. Through iterative classroom deployment and refinement—including adding task-loop adaptivity to offer repeated practice until mastery—we observed encouraging learning outcomes, alongside insights into mitigating ''gaming the system'' behaviors. We detail our co-design process and formative evaluations—procedures, outcomes, and cautious interpretation due to the limited number of consented learners—and share lessons learned to inform scalable ITS development for cybersecurity workforce training in resource-constrained settings.