AI Use Disclosure
Last updated November 17th, 2025
1. Description of PAL (Personalized Adaptive Learning model)
Enya's Personalized Adaptive Learning model (PAL) is an artificial intelligence system designed to enhance educational outcomes by providing personalized learning experiences. PAL analyzes user interactions, learning patterns, and performance data to adapt content and difficulty levels to individual learner needs.
2. Explanation of AI Functionality
PAL utilizes several AI technologies, including:
- Machine learning algorithms that identify patterns in learning behavior
- Natural language processing to analyze text inputs and responses
- Predictive analytics to anticipate learning needs and challenges
- Adaptive sequencing to optimize the order of learning materials
These technologies work together to create a responsive learning environment that adjusts to each student's pace, preferences, and proficiency level.
3. Types of Data Used for Personalization
PAL processes the following types of data to personalize the learning experience:
- Performance data (quiz results, completion rates, time spent)
- Interaction data (response patterns, help requests, navigation choices)
- Progress metrics (skill mastery, concept understanding)
- Learning preferences (content types, difficulty preferences)
- Historical learning data (previous sessions, improvement trends)
4. Personalization Methods and Processes
PAL personalizes the learning experience through:
- Dynamic difficulty adjustment based on performance
- Content recommendation based on learning gaps and strengths
- Pace optimization to match individual learning speeds
- Learning path customization to address specific educational needs
- Feedback tailoring to provide appropriate guidance and encouragement
The personalization process is continuous and evolves as more data is collected about the learner's interactions with the system.
5. Transparency about Algorithmic Decision-Making
PAL makes algorithmic decisions about:
- Which content to present next
- Appropriate difficulty level for activities
- When to review previously covered material
- What types of hints or support to offer
- How to group similar learners for collaborative activities (if applicable)
These decisions are based on established educational principles and are designed to optimize learning outcomes while maintaining engagement.
6. Limitations of AI Technology
We acknowledge the following limitations of our AI technology:
- PAL is not a replacement for human teachers or tutors
- The system may contain biases reflected in its training data
- Personalization accuracy improves over time with more user data
- Some learning needs may require human intervention
- Technical limitations may affect system performance
We continuously work to address these limitations through ongoing research, development, and human oversight.
7. Continuous Improvement Process
Our AI systems undergo continuous improvement through:
- Regular review of performance metrics
- Feedback from educators and learners
- Updates to algorithms and models
- Quality assurance testing
- Ethical review of system behavior and outcomes
8. User Control over AI Personalization
Users maintain control over AI personalization through:
- Preference settings that can be adjusted
- Options to reset personalization data
- Ability to provide feedback on recommendations
- Rights to access and delete personal data
- Options to use non-personalized versions of content