# Distinguishing performance gains from learning when using generative AI

arXiv: https://arxiv.org/abs/2605.13731 · HTML: https://mlsanity.com/paper/2605.13731/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 2 of 2)

- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Promises and challenges of generative artificial intelligence for human learning](https://mlsanity.com/paper/2408.12143/) (2024)

## Built on by (most cited first, 2 of 2)

- [Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment](https://mlsanity.com/paper/2608.11625/) (2026)
- [An emancipatory vision for designing (generative) AI for learner flourishing](https://mlsanity.com/paper/2609.07715/) (2026)

## Abstract (arXiv)

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.