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Academic Success Coaching

Did you know? Advising at UCF is now called Academic Success Coaching. Learn what this means for you here.

Welcome to the CECS Academic Affairs Office

The nationally recognized advising staff within the College of Engineering and Computer Science Academic Affairs Office (AAO) is available to assist students through every step of their academic career. Advisors provide academic support services to enhance the educational experience of all CECS students and strive to empower students to develop and implement academically focused strategies that promote student success.

Through intentional and targeted services, Advisors help students navigate College and University requirements, policies, and procedures. AAO also coordinates CECS scholarship applications and awards, and serves as the administrative portal for CECS-affiliated student organizations.

Associate Dean’s Welcome

AAO Vision

The CECS Academic Affairs Office (AAO) is the office of first destination for academic advising, information, and assistance for students, staff, and faculty.

AAO Mission

The CECS Academic Affairs Office (AAO):

  • Provides a welcoming, caring, and honest environment to foster students’ success from orientation through their timely graduation.
  • Advise, encourage, and motivate all CECS students toward their professional and personal goals through diverse means of communication.
  • Supports CECS faculty and staff in their efforts to continually meet programmatic and institutional accreditation standards

Facts and Rankings

With 12 bachelor’s degree programs, 10 doctoral programs, 18 master’s programs, and a variety of graduate and undergraduate certificate programs, AAO is dedicated to helping each individual student achieve their academic goals.

Frequently Asked Questions

Need assistance? We’ve answered some of the most commonly asked questions for you.

Events

AAO advising events, CECS special events, speakers, seminars, and more.

Speaker: Dr. Kevin Bello From: Soroco Abstract Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships.…

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