Two claims for artificial intelligence techniques in education are that they can increase positive interactive experiences for students, and they can enhance learning. Depending on one's preferences, the critical question might be “how do we configure interactive opportunities to optimize learning?” Alternatively, the question might be, “how do we configure learning opportunities to optimize positive interactions?” Ideally, the answers to these two questions are compatible so that desirable interactions and learning outcomes are positively correlated. But, this does not have to be the case – interactions that people deem negative might lead to learning that people deem positive, or vice versa. The question for this talk is whether there is a “sweet spot” where interactions and learning complement one another and the values we hold most important. I will offer a pair of frameworks to address this question: one for characterizing learning by the dimensions of innovation and efficiency; and one for characterizing interactivity by the dimensions of initiative and idea incorporation. I will provide empirical examples of students working with intelligent computer technologies to show how desirable outcomes in both frameworks can be correlated.