The applications of artificial intelligence in education (AIED) are increasingly subject to scrutiny concerning the fairness of their automated decision-making processes (e.g., auto-grading, problem sequencing, personalization, interventions). However, little attention has been paid to the fairness of the procedures by which these algorithmic decisions are made, i.e., their procedural fairness. This procedural fairness is of critical importance; even seemingly fair decisions can be objectionable to students, teachers, and other stakeholders if the decision-making process itself is unfair. Without a structured framework, AIED applications risk perpetuating biases that disadvantage certain groups, deepen educational inequalities, and marginalize minority voices. Our full-day Fair4AIED workshop will take the first steps toward developing a blueprint to systematically integrate algorithmic fairness into AIED research, practice, and adoption. Participants are engaged in short-lightning talks, Q&A sessions, and discussions to explore opportunities to improve fairness and raise awareness of its importance for AIED. Subsequently, small-group discussions followed by a world-café-based session foster a structured, hands-on approach to envision the blueprint’s content.