# Analysis, Modeling and Design of Personalized Digital Learning Environment

arXiv: https://arxiv.org/abs/2405.10476 · HTML: https://mlsanity.com/paper/2405.10476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 4 of 4)

- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective](https://mlsanity.com/paper/2111.03308/) (2021)
- [Android Private Compute Core Architecture](https://mlsanity.com/paper/2209.10317/) (2022)
- [Automated Reading Passage Generation with OpenAI's Large Language Model](https://mlsanity.com/paper/2304.04616/) (2023)

## Abstract (arXiv)

This research analyzes, models and develops a novel Digital Learning Environment (DLE) fortified by the innovative Private Learning Intelligence (PLI) framework. The proposed PLI framework leverages federated machine learning (FL) techniques to autonomously construct and continuously refine personalized learning models for individual learners, ensuring robust privacy protection. Our approach is pivotal in advancing DLE capabilities, empowering learners to actively participate in personalized real-time learning experiences. The integration of PLI within a DLE also streamlines instructional design and development demands for personalized teaching/learning. We seek ways to establish a foundation for the seamless integration of FL into learning systems, offering a transformative approach to personalized learning in digital environments. Our implementation details and code are made public.