Artificial Intelligence. Ph.D.

Location(s): Online, Executive

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Requirements: 60 Credit Hours

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The Doctor of Philosophy in Artificial Intelligence at the University of the Cumberlands in its School of Computer and Information Sciences provides a practical and theoretical understanding of artificial intelligence through a hands-on and research-based approach that includes working on major end-to-end projects, research assignments, and exercises. It is designed to equip students with the advanced expertise needed for professional roles such as AI Research Scientist, Machine Learning Engineer, Chief AI Officer, Data Science Director, AI Ethics Consultant, and AI Policy Advisor. The program fosters critical thinking, innovation, and problem-solving skills. Graduates will be well-prepared to lead and drive advancements in artificial intelligence, shaping the future of AI across diverse industries.

PhD AI Program Requirements

Research:

Students will formally identify a dissertation topic and write the review of the literature in fulfillment of the requirements for DSRT 736 - Dissertation Seminar . The remaining sections of the first three chapters of the dissertation will be written while enrolled in DSRT 839 - Advanced Research Methods . Candidates will carry out their research to complete the dissertation.

Comprehensive Exam:

Candidates sit for the Comprehensive Examination prior to enrolling DSRT 930 - Dissertation . The specific content, structure, and scheduling of this examination is determined by the Program Director. Tailored to each student’s program of study, questions on the Comprehensive Examination are generated by the program’s four content-related goals:

Prepare students to design, implement, and optimize AI systems using advanced methodologies like machine learning, deep learning, and natural language processing to solve real-world challenges.
Equip students with the skills to apply statistical, analytical, and computational tools for rigorous AI research and data-driven problem-solving.
Enable students to design responsible, transparent, and ethical AI systems that prioritize fairness, accountability, and societal trust.
Empower students to innovate and contribute to AI advancements in areas like generative AI and data engineering while addressing global challenges.

The Comprehensive Examination is proctored and graded by contributing members of program faculty, with passing marks required on all sections of the examination before the student may register for DSRT 930 .

The Dissertation

The dissertation is the capstone experience in the Artificial Intelligence, Ph.D. program. A dissertation is a research-based project that may use a wide-range of statistical, quantitative, qualitative, and applied-project based methods. However, in the University’s practitioner-focused program, the dissertation is conceived as a reality-based project in which the candidate engages in authentic professional problem-solving to extend best practices in the field. Specific details on dissertation requirements are included in the Dissertation Handbook.

All program features highlighted above combine to make the doctoral program at University of the Cumberlands a rigorous academic experience focused on strengthening the skills of information technology professionals.

Required Core Courses: 27 Credit Hours

PhDAI 730 - Statistics for AI Credit(s): 3 hours
PhDAI 731 - Analytical Methods for Machine Learning Credit(s): 3 hours
PhDAI 732 - Fundamentals of AI-Enabled Systems Credit(s): 3 hours
PhDAI 733 - Python Application for Data Analytics in AI Credit(s): 3 hours
PhDAI 830 - Applied Machine Intelligence and Reinforcement Learning Credit(s): 3 hours
PhDAI 831 - Deep Learning and Natural Language Processing Credit(s): 3 hours
PhDAI 832 - Ethics in Responsible AI Credit(s): 3 hours
PhDAI 833 - Data Engineering for AI Credit(s): 3 hours
PhDAI 834 - Trustworthy and Explainable AI Credit(s): 3 hours

Required Elective Courses: 9 Credit Hours

ITS 833 - Information Governance Credit(s): 3 hours
ITS 834 - Emerging Threats and Countermeasures Credit(s): 3 hours
ITS 836 - Data Science and Big Data Analytics Credit(s): 3 hours
OR
INTR 799 - Applied Learning Practicum Credit(s): 1 hour (Executive format students)*
INTR 899 - Applied Learning Practicum, Dissertation Phase Credit(s): 3 hours (Executive format students)*
* For Executive format students, the applied learning component (internship/practicum) is an integral and essential part of the established curriculum. Due to the applied learning component required for the degree program, Executive format students must enroll in INTR every term of enrollment. In addition to the required course enrollment, successful completion of three (3) hours of INTR 799/INTR 899 may replace one of the PhDIT electives, ITS 833 , ITS 834 , or ITS 836 with approval of the Department Director and/or Chair.

Required Professional Research Courses: 24 Credit Hours

DSRT 734 - Inferential Statistics in Decision-making Credit(s): 3 hours
DSRT 736 - Dissertation Seminar Credit(s): 3 hours
DSRT 834 - Advanced Statistical Applications Credit(s): 3 hours
DSRT 837 - Professional Writing and Proposal Development Credit(s): 3 hours
DSRT 839 - Advanced Research Methods Credit(s): 3 hours
DSRT 850 - Qualitative Research Methods Credit(s): 3 hours
DSRT 930 - Dissertation Credit(s): 3 hours
DSRT 931 - Dissertation Credit(s): 3 hours *
* If the dissertation is not completed at the end of DSRT 931, the candidate repeats DSRT 931 as needed, for three (3) credit hours each term, until the dissertation is completed and successfully defended.