Distinguishing performance gains from learning when using generative AI

2026-05-13

AI Summary

While generative AI can enhance learners’ task performance, it does not necessarily foster deep cognitive or metacognitive processing, leading to a disconnect between performance gains and genuine learning outcomes. This study proposes an integrative analytical framework grounded in educational psychology theory, employing cognitive and metacognitive assessment methods to systematically differentiate surface-level performance from deeper learning achievements in AI-supported contexts. The findings reveal the nuanced and complex impact of generative AI on learning quality, underscoring the necessity for educational AI design to move beyond mere task performance and instead prioritize authentic learning mechanisms. This work provides both theoretical grounding and practical guidance for the development of future intelligent educational tools that effectively support meaningful learning.

Abstract

Generative artificial intelligence (AI) is increasingly being integrated into education, where it can boost learners' performance. However, these uses do not promote the deep cognitive and metacognitive processing that are required for high-quality learning.

Problem

• generative AI
• learning
• performance gains
• education
• cognitive processing

Innovation

• generative AI
• learning
• performance gains
• cognitive processing
• metacognitive processing