Many AIED systems support self-regulated learning, yet, support for setting and achieving practice goals has received little attention. We examine how middle school students respond to system-recommended practice goals, building on the success of similar data-driven recommendations in other domains. We introduce an adaptive dashboard in an intelligent tutoring system. The dashboard tracks progress (time, mastery), suggests dynamic goal adjustments, and visualizes performance—aiming to increase learning opportunities. In a prototyping study with five middle schoolers (grades 6–7), we used scenario-based tasks to examine reactions to goal recommendations during under- or overachievement. Thematic analysis revealed: (1) students accepted system suggestions as guidance but wanted final control; (2) they valued adaptive recommendations tied to past performance to increase their chance of meeting their goal and pace themselves; and (3) they strongly preferred motivational features (e.g., streaks, celebrations) for engagement. Our study contributes design implications for practice goal-setting in tutoring systems, highlighting personalized, explainable learning analytics and motivational design to support goal-directed behavior.