When small-group training uses a spaced attempt–worked‑example cycle with required private unguided attempts, does turning off the always‑on AI assistant during the unguided‑attempt phase, but allowing it during comparison and discussion, improve long‑term retention and transfer more than leaving AI available at all times, particularly in groups prone to heavy external‑artifact use?

Answer

It is plausible—but not empirically confirmed—that in small-group training built around spaced cycles of private unguided attempts followed by worked-example comparison, turning off the AI assistant during the unguided-attempt phase and only allowing it during comparison/discussion will modestly improve long-term retention and transfer relative to leaving AI available at all times—especially in groups prone to heavy external-artifact use and dominant-solver/social-loafing dynamics.

Mechanism and boundary conditions:

• With AI disabled during private attempts, every member must engage in genuine retrieval and problem solving instead of letting an AI (often via a dominant member) generate the initial solution. This should deepen encoding and reduce illusions of learning.
• Restricting AI to the comparison/discussion phase still preserves the benefits of external artifacts (checking, elaborating, generating variants) without displacing the core retrieval opportunity.
• Gains are likely largest in groups that otherwise lean heavily on external artifacts and/or show dominant-solver patterns; in more disciplined groups that already respect unguided-attempt norms despite AI availability, the incremental benefit of hard disabling AI during attempts will be smaller.
• If tasks are so difficult that many members cannot produce any meaningful unguided attempt, or if AI lockout is experienced as punitive without support (e.g., scaffolding, hints later), the policy can backfire—raising frustration and reducing engagement.

Overall, the prediction is moderately confident: AI-off during unguided attempts is likely to yield small-to-moderate average gains in long-term retention and transfer over always-on AI in the specified setting, with stronger effects in high–external-artifact, high–social-loafing groups. Direct empirical tests are needed to confirm the size and reliability of this benefit.