Criteria for selecting artificial intelligence tools and systems for educational purposes

2.6. Adaptability and personalisation

Effective AI tools and systems in education are defined by their ability to adapt to different learning styles and the individual needs of students. Personalising learning in this way can positively influence motivation, confidence, and success in mastering material, as it allows students to progress at their own pace with content that matches their interests and prior knowledge. Platforms that dynamically adjust activities for each student contribute to greater engagement and a higher-quality educational experience.

Examples:

• The Curipod tool uses artificial intelligence to generate interactive teaching content, such as quizzes, surveys, and problem-solving tasks, that adapt to students' knowledge and interests. Teachers can further tailor activities to the specific needs of their group, increasing pedagogical effectiveness and student engagement. The system also provides real-time feedback, helping students recognise and correct errors more quickly and enabling teachers to better monitor progress.
• Knewton is a platform that supports personalised learning in higher education. The system continuously analyses student progress, identifies areas of strength and those needing additional support, and adjusts educational content accordingly. Because of this flexibility, Knewton easily integrates with existing educational materials and learning environments, making it a valuable tool to support students throughout their studies.