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.

本研究分析、建模和开发了一个创新的数字学习环境 (DLE)，采用创新的私密学习智能 (PLI) 框架。该提出的 PLI 框架利用联邦机器学习 (FL) 技术来自动构建和不断优化个性化学习模型，确保强大的隐私保护。我们的方法是推进 DLE 能力的关键，使学习者能够积极参与个性化的实时学习体验。在 DLE 中整合 PLI 还简化了个性化教学设计和开发需求。我们致力于建立 FL 与学习系统的无缝集成基础，为数字环境中的个性化学习提供一种革命性的方法。我们的实施细节和代码已公开。