This paper aims to tie literature in AI to enduring questions in education about teaching and learning and discern ethical considerations that define those ties. The challenge was to answer the question: how do we merge our learning and leadership theories to technologies and the algorithmic biases that may maintain today's social injustices into our future? The paper first reviews the literature to identify the dialogue on AI by computer scientists in relation to enduring questions in education, learning theories, and ethics. Then we summarize data in the form of vignettes written by experts from the humanities, computer science, and social sciences. Some of the vignettes focused on how educational and technological systems are products of the social system and the ethical implications of such connections. Other writings centered data-driven approaches to incorporating AI technologies in classrooms, with concerns around uneven implementation and differential access. The paper concludes that to dialogue with educators AIED will need to move away from discussions of efficiency as measured by educational assessments and incorporate humanistic and social learning theories that embrace the complexities of human relationships. Developers should seek to work directly with educational leaders to establish optimal teaching strategies for the ethical ‘good’ of the learner, while attending to social justice parameters. Equally critical is the need to create ethical parameters between the AI and the student.