Criteria for selecting artificial intelligence tools and systems for educational purposes

2.4. Evidence of effectiveness

Before introducing AI-based tools and systems into teaching, it is necessary to consider whether research and evidence support their effectiveness and contribution to learning. AI tools that have already been evaluated in real educational settings are a safer choice, as their functionality and pedagogical value are based not only on marketing claims but also on results from testing and application in educational practice. Such validation reduces the risk of inadequate implementation and allows teachers to make informed decisions about how much the tool can enrich the learning experience.

Example:

• Duolingo is one of the most well-known examples of AI-based educational tools whose effectiveness has been confirmed in numerous studies. By using adaptive algorithms that respond to individual user progress, it enables systematic vocabulary acquisition and the development of language comprehension. Because of these pedagogical advantages, it is increasingly integrated into school and university curricula as an additional and easily accessible tool for learning foreign languages.

• Gradescope is widely accepted in educational practice as a reliable system for automated grading and providing structured feedback to students, especially in STEM fields. Research and teaching experience confirm that it facilitates consistent and transparent evaluation for teachers while providing students with faster insight into their own mistakes and opportunities for improvement. As a result of these benefits, Gradescope has been integrated into many university programmes, including those at Harvard University and the Massachusetts Institute of Technology (MIT).