Conditions for Effective Learning Without Upfront Instruction: How Practice with Feedback Supports Memory, Generalization, Motivation, and Metacognition

Abstract What conditions are necessary for students to learn from practice and feedback, without the need for upfront lecture? Across two experiments ( N = 597), we examined how practice with feedback can support memory, generalization, metacognition, and motivation. Participants were randomly assigned to one of three instructional formats: a traditional lecture, practice with correct-answer feedback, or practice with explanatory feedback (predefined or adaptive and AI generated). In both studies, the lecture condition introduced linear regression through definitions and a worked example, while the practice conditions used matched problem sets with feedback that either (a) provided only correct answers or (b) explained why answers were correct. Study 1 used multiple-choice questions; Study 2 used open-ended questions with personalized explanatory feedback generated in real time by GPT-4o. For memory, both types of feedback outperformed lecture, suggesting that attempting a response and receiving feedback—even without explanations—enhances encoding. For generalization, however, feedback needed to include explanations, and learners needed sufficient prior knowledge to benefit. Study 2 also showed that practice—regardless of feedback type—improved metacognitive calibration compared to lecture, helping learners more accurately assess their understanding. While lecture produced greater situational interest for less-confident learners in Study 1, this pattern reversed in Study 2, where personalized, AI-generated feedback elicited higher interest for this group. Together, these findings clarify when and for whom practice with feedback can replace lecture-based instruction, and they highlight the potential of generative AI to scale personalized, explanatory feedback.