Prior knowledge and trust calibration moderate GenAI learning effects: fluent output is most persuasive when learners least can challenge it¶

Claim · All claims
Evidence · 1 study (2 entries) · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Better-calibrated novices could ignore poor AI suggestions while struggling students could be pulled further off course. → Qusay H. Mahmoud 2026 q2 i? Students valued ChatGPT's explanations while recognizing the need for background knowledge to judge them. → Qusay H. Mahmoud 2026 (2)

Evidence¶

Qusay H. Mahmoud 2026¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473 doi:10.48550/arxiv.2609.29473

q2 · i? · review · r2

The review reports the novice-programming "widening gap" finding: "stronger or better-calibrated novices could ignore poor suggestions, while struggling students could be pulled further off course", making calibration a learning outcome.

"The “widening gap” study shows why this matters in practice: stronger or better-calibrated novices could ignore poor suggestions, while struggling students could be pulled further off course"

Qusay H. Mahmoud 2026 (2)¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473

q2 · i? · review · r2

The review attributes to Shoufan's study that "students valued ChatGPT’s explanations while recognizing the need for background knowledge to judge them"; large reviews also list over-reliance and prior knowledge as recurring moderators.

"Shoufan [2023] found that students valued ChatGPT’s explanations while recognizing the need for background knowledge to judge them."

Discussion¶

Learner Variables¶

• Prior Knowledge — moderator: an instructional effect differs with it

Related Claims¶

• Fluent, confident AI presentation gives reason to expect trust miscalibration, especially among learners with limited domain knowledge — possibly the same claim (merge candidate)
• Calibrated epistemic vigilance, given adequate prior knowledge, is the binding constraint on productive augmentation with AI — a broader claim this one bears on
• Prior Knowledge Needed For Accurate Self Assessment — a broader claim this one bears on
• Fluent Illusions Mislead Self Assessment — related
• Students evaluate GenAI explanations for assumed background and audience, reading unfamiliar cultural references and excessive scaffolding as signals about who the explanation was written for — related
• GenAI chemistry outputs can be fluent and locally persuasive while chemically invalid across representations — related
• Students exhibited high trust in GenAI but limited critical evaluation of its outputs — related
• A student maintained critical distance from GenAI outputs through a paradoxical trust in their untrustworthiness, deriving practical value while staying sceptical