Statistics education rebooted in the age of artificial intelligence

This is the authors' abstract. We don't add key points for this paper.

This systematic review synthesises 20 peer-reviewed studies published between 2020 and 2026 on the use of artificial intelligence (AI), including generative AI, in statistics education. Following PRISMA 2020 guidelines, it examines how AI tools are integrated into statistics teaching and learning, how they affect students’ conceptual understanding and statistical reasoning, and what ethical, pedagogical, and assessment challenges accompany their use. Across the literature, AI is used to provide explanation, adaptive feedback, personalized support, and automated assessment. The reviewed studies suggest that these tools can strengthen conceptual understanding and support statistical reasoning when embedded in well-designed instruction. At the same time, the literature identifies concerns related to academic integrity, hallucinated or misleading outputs, algorithmic bias, and weakening assessment validity when AI substitutes for student reasoning. Overall, the evidence suggests a shift from procedural performance toward interpretation, critique, justification, and responsible AI use, with implications for curricular and assessment reform.