In digital learning systems, gaming the system refers to occasions when students attempt to succeed in an educational task by systematically taking advantage of system features rather than engaging meaningfully with the content. Often viewed as a form of behavioral disengagement, gaming the system is negatively associated with short- and long-term learning outcomes. However, little research has explored this phenomenon beyond its behavioral representation, leaving questions such as whether students are cognitively disengaged or whether they engage in different self-regulated learning (SRL) strategies when gaming largely unanswered. This study employs a mixed-methods approach to examine students’ cognitive engagement and SRL processes during gaming versus non-gaming periods, using utterance length and SRL codes inferred from think-aloud protocols collected while students interacted with an intelligent tutoring system for chemistry. We found that gaming does not simply reflect a lack of cognitive effort; during gaming, students often produced longer utterances, were more likely to engage in processing information and realizing errors, but less likely to engage in planning, and exhibited reactive rather than proactive self-regulatory strategies. These findings provide empirical evidence supporting the interpretation that gaming may represent a maladaptive form of SRL. With this understanding, future work can address gaming and its negative impacts by designing systems that target maladaptive self-regulation to promote better learning.

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.