Takeaways
- Students using AI tools to complement rather than substitute their learning activities show better educational outcomes, whereas those who rely on AI to replace core learning processes demonstrate decreased understanding despite covering more content (Lehmann et al. (2025), Wecks et al. (2024))
- Personalized AI learning systems improve academic achievement by adapting content based on students' prior knowledge, learning pace, and interests, with studies showing significant increases in test scores and higher student motivation (Pardamean et al. (2022), Chen (2024), Chen et al. (2024))
- AI systems that scaffold problem-solving through multi-level hints rather than providing complete solutions lead to stronger learning outcomes, with studies showing students engage longer with learning materials and develop better understanding when given guidance that supports their own thinking (Hou et al. (2025), Xiao et al. (2024))
- AI-powered tools benefit students with language barriers by providing translations, explanations in simpler language, and culturally relevant examples, with studies showing improved performance for non-native English speakers and those from countries with lower Human Development Index scores (Alneyadi & Wardat (2023), Nie et al. (2024)).
- Students benefit most from generative AI when receiving training in prompt engineering and effective AI interaction strategies, with studies showing substantial differences in learning outcomes based on how students structure their questions and interpret AI responses (Woo et al. (2023), Alves & Pereira Cipriano (2024), Wei et al. (2025))
