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Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting

Mahir Akgun, Sacip Toker

TL;DR

The paper investigates AI-assisted pretesting using GenAI (ChatGPT) across three experiments to determine how task complexity and motivation modulate learning gains. Study 1 shows pretesting before AI interaction improves final transfer test performance without altering recall. Study 2 finds no advantage for AI-assisted pretesting over fixed pretesting in a computational task, suggesting benefits depend on task type. Study 3 demonstrates that adaptive AI pretesting boosts both learning outcomes and motivational measures in a higher-order, model-selection task, underscoring the importance of task alignment. Collectively, the findings support integrating AI with pretesting to preserve productive struggle and optimize learning, especially for cognitively demanding tasks.

Abstract

This study examines the role of AI-assisted pretesting in enhancing learning outcomes, particularly when integrated with generative AI tools like ChatGPT. Pretesting, a learning strategy in which students attempt to answer questions or solve problems before receiving instruction, has been shown to improve retention by activating prior knowledge. The adaptability and interactivity of AI-assisted pretesting introduce new opportunities for optimizing learning in digital environments. Across three experimental studies, we explored how pretesting strategies, task characteristics, and student motivation influence learning. Findings suggest that AI-assisted pretesting enhances learning outcomes, particularly for tasks requiring higher-order thinking. While adaptive AI-driven pretesting increased engagement, its benefits were most pronounced in complex, exploratory tasks rather than straightforward computational problems. These results highlight the importance of aligning pretesting strategies with task demands, demonstrating that AI can optimize learning when applied to tasks requiring deeper cognitive engagement. This research provides insights into how AI-assisted pretesting can be effectively integrated with generative AI tools to enhance both cognitive and motivational outcomes in learning environments.

Struggle First, Prompt Later: How Task Complexity Shapes Learning with GenAI-Assisted Pretesting

TL;DR

The paper investigates AI-assisted pretesting using GenAI (ChatGPT) across three experiments to determine how task complexity and motivation modulate learning gains. Study 1 shows pretesting before AI interaction improves final transfer test performance without altering recall. Study 2 finds no advantage for AI-assisted pretesting over fixed pretesting in a computational task, suggesting benefits depend on task type. Study 3 demonstrates that adaptive AI pretesting boosts both learning outcomes and motivational measures in a higher-order, model-selection task, underscoring the importance of task alignment. Collectively, the findings support integrating AI with pretesting to preserve productive struggle and optimize learning, especially for cognitively demanding tasks.

Abstract

This study examines the role of AI-assisted pretesting in enhancing learning outcomes, particularly when integrated with generative AI tools like ChatGPT. Pretesting, a learning strategy in which students attempt to answer questions or solve problems before receiving instruction, has been shown to improve retention by activating prior knowledge. The adaptability and interactivity of AI-assisted pretesting introduce new opportunities for optimizing learning in digital environments. Across three experimental studies, we explored how pretesting strategies, task characteristics, and student motivation influence learning. Findings suggest that AI-assisted pretesting enhances learning outcomes, particularly for tasks requiring higher-order thinking. While adaptive AI-driven pretesting increased engagement, its benefits were most pronounced in complex, exploratory tasks rather than straightforward computational problems. These results highlight the importance of aligning pretesting strategies with task demands, demonstrating that AI can optimize learning when applied to tasks requiring deeper cognitive engagement. This research provides insights into how AI-assisted pretesting can be effectively integrated with generative AI tools to enhance both cognitive and motivational outcomes in learning environments.

Paper Structure

This paper contains 43 sections, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Theoretical Background
  3. Pretesting as a Catalyst for Learning
  4. Cognitive Mechanisms Underlying Pretesting
  5. Pretesting in the Digital Information Age
  6. Generative AI: New Opportunities and Challenges
  7. Present Study
  8. Study 1
  9. Method
  10. Experimental Design
  11. Participants
  12. Procedure
  13. Materials
  14. Phase 1 Activity.
  15. Recall Test.
  16. ...and 28 more sections

Figures (7)

  • Figure 1: Study 1 Procedure
  • Figure 2: Scenario used in Phase 2 and pretest questions
  • Figure 3: The Recall and Final Test Performances for No-pretest and Pretest groups
  • Figure 4: Study 2 Procedure - Phase 2
  • Figure 5: The Recall and Final Test Performances for Fixed Pretesting and AI-Assisted Pretesting groups (Study 2)
  • ...and 2 more figures