# Using Artificial Intelligence to Improve Classroom Learning Experience

arXiv: https://arxiv.org/abs/2503.05709 · HTML: https://mlsanity.com/paper/2503.05709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Extended Reality (XR): The Next Frontier in Education](https://mlsanity.com/paper/2602.23601/) (2026)

## Abstract (arXiv)

This paper explores advancements in Artificial Intelligence technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students learning experiences by using Machine Learning algorithms to : identify a student preferred learning style and predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. A case study, with 76,519 candidates and 35 predictor variables, assesses academic dropout risk using Logistic Regression, achieving a test accuracy of 87.39%. In comparison, the Stochastic Gradient Descent classifier achieved an accuracy of 83.1% on the same dataset.