A new research-oriented explainer argues that AI tutoring systems can improve student understanding and performance by using Socratic dialogue and targeted scaffolding. The piece describes how AI tutors can provide personalized help on specific weaknesses and offer learning support at any time, supplementing—rather than replacing—human instruction. It addresses the primary concern that AI tutors might provide inaccurate guidance by citing comparative study evidence from Germany, where researchers examined outcomes after combining textbook learning with AI tutoring. The cited study design included a “reliable textbook answers” group before students used AI, aiming to test whether the pairing improves results despite possible AI errors. The explainer frames tutoring adoption as slower than other AI learning uses, in part due to concerns about accuracy and whether students will view AI as shifting instructor responsibility. It argues that students already use AI as learning support and that AI tutors should be treated as additional resources in a broader learning ecosystem. For faculty and instructional designers, the central message is that AI tutoring’s value depends on reliability and alignment to course learning goals—while the emerging research base supports continued cautious expansion.