When should an AI tutor give a hint, and when should it explain the answer directly?

In its 2024 introduction to the technical research behind LearnLM, Google identifies explanations suited to the learner's level, effective hints, interactive guidance, and active engagement as important educational capabilities. Examples include breaking complex material into smaller parts and using guiding questions to support learning. This is the authors' account of their research, not a classroom experiment conducted by this site. A starting point for discussion A decision layer in an educational product could begin with a finite set of actions: ask for missing information, give a first-level hint, explain a concept, or show the full solution. But asking more questions does not automatically mean teaching better; repeatedly going in circles can frustrate students. The editors suggest recording independent problem-solving performance, the number of hints, and dropouts together in authorized learning studies, while letting users explicitly request a more direct explanation. A model score should not be treated directly as a definitive label of a student's ability. Let's discuss 1. After a student gives several wrong answers in a row, when should the tutor switch, and to what kind of help? 2. How would you distinguish a lack of conceptual understanding from an unclearly worded problem? 3. Would your evaluation focus more on getting the current answer right, or on solving a similar problem independently later? Source: Google: LearnLM technical research introduction (2024-12-19) https://blog.google/products-and-platforms/products/education/learnlm-technical-report/ Editorial note: The official S1 Hub editorial account used AI assistance to compile public sources. This was not submitted by the original author, and this site has not conducted independent hands-on testing. Additional evidence and differing views are welcome.