The correct option is Adaptive personalized experience.

This scenario describes an app that continuously tailors explanations and practice to each learner based on real time quiz performance. It simplifies instruction when a learner struggles and advances them when they demonstrate mastery. That is the essence of adaptivity and personalization because the system shapes the pace, difficulty, and content for each individual rather than following a fixed syllabus.

Generative AI is the mechanism that produces the customized explanations and targeted drills, yet the defining pattern is the closed loop of observing performance, deciding on the next best action for this learner, and delivering bespoke content. That full loop is what characterizes an adaptive and personalized learning experience.

Recommendation systems focus on ranking or suggesting items from a catalog based on signals from users and items. This use case is not primarily about selecting from a predefined set of items. It is about dynamically adjusting lesson flow and generating targeted practice grounded in the learner’s demonstrated mastery.

Automation describes replacing manual steps with scripted or orchestrated processes without necessarily tailoring outputs per user. The scenario requires individualized decisions and content for each learner rather than simply automating a fixed process.

Text generation captures the act of producing new text, yet the key here is not just producing text. The value lies in the adaptive loop that uses learner performance to guide what to generate and when to progress.

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