UA Little Rock’s new AI degree: resolve the credit total before transferring

Bring both credit totals to advising

The University of Arkansas at Little Rock's October 12, 2026 report describes the inaugural fall semester of its Bachelor of Science in Artificial Intelligence. It calls the program 120 hours and describes technical coursework, hands-on projects and two capstones. This establishes a newly operating degree. It does not establish that every course within it is new.^[1]

The linked one-page degree plan is headed 123 HOURS and visibly marked LAST REVISION:9/29/2026. Its eight semester loads are 17, 16, 17, 16, 15, 15, 15 and 12 hours. Their sum is 123. This is a cross-document discrepancy, not evidence that the university has formally added three hours to a previously verified requirement. Neither document explains the difference. Ask which catalog year and approved total govern your enrollment, and request the governing catalog entry or written clarification.^[1][2]

Do not multiply either headline total by a tuition rate to estimate your remaining cost. First establish the applicable requirement, accepted substitutions and outstanding courses. The news report includes transfer students' experiences, which do not determine how another student's credits will be evaluated. The recommendation to obtain a written audit is editorial planning advice; this article has not evaluated a student's transcript or calculated a completion date.^[1][2]

Check the foundations against actual course equivalencies

The map places CPSI 17503 Programming I and MATH 24004 Calculus I in semester one. Semester two includes CPSI 27603 Programming II, MATH 25004 Calculus II and MATH 26103 Discrete Mathematics. MATH 31263 Linear Algebra appears in semester three, followed by STAT 35203 Applied Statistics I in semester four. For a transfer discussion, compare your completed courses with these named requirements instead of treating a similar title as a confirmed equivalency.^[2]

The plan also requires a Chemistry Track or Physics Track, with laboratory classes in both semesters. Its PHIL 23093 Ethics and Society entry is a suggestion: the note allows any humanities course listed on the General Core. These details can change the questions you bring to an audit. Ask separately about the selected science sequence and about a humanities credit already completed, rather than assuming every printed title is the only permitted option.^[2]

Use the map as a planned sequence. It is not a current section schedule, and its arrows do not provide a verified catalog prerequisite definition. Ask the adviser to confirm formal prerequisites, substitutions and actual offering terms for any course that could delay your next step. Do not assume that transfer standing, a broad computing background or an earlier position on the diagram guarantees entry to a later class.^[2]

Plan applied work and capstones within the remaining sequence

AI Governance, CPSI 21803, appears in semester three. AI Tools and Techniques, CPSI 20803, and AI Practicum, CPSI 23003, appear in semester four. Machine Learning I and II, CPSI 31003 and CPSI 31303, appear in semesters five and six. Later mapped subjects include natural language processing, computer vision, intelligent agents, deep learning and reinforcement learning. This is specific curriculum content, rather than a claim that an AI degree consists only of learning current chat tools.^[2]

Capstone I, CPSI 41003, and Capstone II, CPSI 42003, occupy semesters seven and eight on the map. Ask how a transfer plan reaches both, what entry conditions apply and when they will actually run. Bring course descriptions or syllabi through the university's approved advising channel. Request an audit that distinguishes accepted credit, required substitutions, outstanding work and unresolved decisions. Do not publish a private transcript to seek informal answers.^[1][2]

Separate the learning opportunity from a hiring claim

The university report describes faculty AI and machine-learning research and opportunities to connect classroom learning with research and applied projects. It also quotes a student's AI-engineer ambition and an industry representative's interest in partnership. Those statements are institutional context and individual aspirations. They do not measure placements, salary returns, employer demand for this particular degree or the readiness of a graduate for an AI Engineer or FDE role.^[1]

For your own practical learning record, retain a project specification, the work you contributed, the evaluation you actually ran and the limits you found. That is an editorial suggestion for documenting applied coursework, with no tested hiring benefit. An authoritative catalog entry or official clarification could resolve the 120-versus-123 question; these two documents alone do not.^[1][2]