The greater the proliferation of AI in educational contexts, the more important it becomes to ensure that AI adheres to the equity and inclusion values of an educational system or institution. Given that modern AI is based on historic datasets, mitigating historic biases with respect to protected classes (ie, fairness) is an important component of this value alignment. Although extensive research has been done on AI fairness in education, there has been a lack of guidance for practitioners, which could enhance the practical uptake of these methods. In this work, we present a practitioner-oriented, step-by-step framework, based on findings from the field, to implement AI fairness techniques. We also present an empirical case study that applies this framework in the context of a grade prediction task using data from a large public university. Our novel findings from the case study and extended analyses underscore the importance of incorporating intersectionality (such as race and gender) as central equity and inclusion institution values. Moreover, our research demonstrates the effectiveness of bias mitigation techniques, like adversarial learning, in enhancing fairness, particularly for intersectional categories like race-gender and race-income.