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<p>What conditions are necessary for students to learn from practice and feedback, without the need for upfront lecture? Across two experiments (<i>N</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Michael W. Asher,
  • Paulo F. Carvalho

摘要

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.