Abstract: A system and method trained to identify areas of learning challenge and essential concepts and generate recommendations with customized practice and instructional materials to address and capture all students' needs. The recommendations are generated based on a data-based progression of educational content. The system creates models for each student, which are adjustable or modifiable to teacher planning preferences and teacher knowledge of the student, and can adapt to interruptions and lost instructional time due to uncontrollable circumstances (e.g., global pandemic.). A graphical interface displays recommendations to instructors or students for selection. A separate clustering algorithm uses an underlying student model to group students who would benefit from working on the same educational activities for optimal learning. The recommendations may be aligned to selected educational goals or a general list of effective choices for any given student or a group of students.

Abstract: Some embodiments of a system and methods that automate and facilitate the alignment of educational content from diverse sources are disclosed. This system and methods facilitate and enhance automated content connections. They are configured to create models that automate and facilitate the alignment of educational content from diverse sources, ranging from specific questions to instructional plans. These may be aligned independently or through a knowledge graph. This alignment includes jurisdiction dependent definitions of educational goals, such as standards and learning objectives. Model inputs include one content set being assimilated and associated metadata persisted in a data store where another content set can be added for comparison. Content is evaluated for perfect text matches and also imperfect matches that are close in semantic meaning. The system's algorithm compares multiple products, their education units of information, and academic standards as designated between various agencies.