The 4 Best AI Masters Degree Programs in Utah

Workers with AI skills earn 56% higher wages, making your decision to pursue an AI masters degree program in Utah a smart investment in your career. AI-related jobs have grown 38% globally over the last five years, and professionals with advanced degrees earn up to 33% more per year compared to bachelor’s graduates.

Utah’s AI activity centers around Salt Lake City and Silicon Slopes, creating a thriving ecosystem for students pursuing a masters in AI and machine learning. Of course you’ll find options ranging from traditional on-campus programs to an online masters in AI. We’ve assessed the four best programs to help you make an informed decision for 2026.

1. University of Utah – Master’s in Artificial Intelligence

Program Overview and Specializations

The University of Utah’s Kahlert School of Computing offers an Artificial Intelligence track within its MS and PhD programs, directed by Ana Marasović. This track approaches AI as a multidisciplinary discipline that studies the theory and methods underlying thought and intelligent behavior and their implementation in machines. The program trains you to develop systems that can see, learn, reason, communicate, and act in the world.

This AI masters degree program takes a detailed view of the AI field, unlike narrowly focused programs. You’ll study the full spectrum of AI development, from theoretical foundations to practical applications and governance considerations. The track wants to train students for advancing, applying, and governing AI computing technology. This three-pronged approach sets the program apart and addresses not just technical capabilities but also the responsible deployment of AI systems.

The university has expanded its AI offerings substantially. The MS and PhD tracks have been around for a while, but the institution launched Utah’s first bachelor’s degree in artificial intelligence for fall 2026. The Kahlert School also set up an AI minor last fall, and the David Eccles School of Business introduced an AI minor for all undergraduates. These additions demonstrate the university’s commitment to AI education at all degree levels.

Curriculum and Course Structure

The AI track provides flexibility beyond required courses and allows you to tailor your studies to your specific interests. The program emphasizes theory, systems, and hardware while giving you autonomy in selecting electives and research directions. This structure supports both students seeking industry careers and those pursuing research paths.

Your coursework can span multiple AI domains. The program covers heuristic programming, machine learning, pattern recognition, robotics, and computer vision. You’ll also gain exposure to deep learning through the university’s graduate certificate program, which can complement your master’s studies. The certificate provides working knowledge of state-of-the-art deep learning technology.

The university offers dozens of AI-related classes across subject areas beyond the core computing courses. You can explore cognitive science through a minor that draws from mathematics, computing, and AI to get into the mind’s computational nature. The interdisciplinary options extend to the College of Humanities, which proposed an AI-inflected Workplace Writing master’s degree set to launch in fall 2027. This fully online professional graduate degree prepares students for careers in workplace writing shaped by generative AI and emerging technologies.

Full-time students must enroll in 9 credits per term. The program structure allows you to complete requirements in about 2 years, though the maximum allowed duration extends to 4 years. You can adjust your course load based on your circumstances; taking 2 classes in one semester remains acceptable if you’re not receiving scholarships or housing benefits requiring full-time status.

Admission Requirements

You need a bachelor’s degree from a regionally accredited college or university to apply. The university requires a minimum 3.0 GPA on a 4.0 scale for admission. Enter the number as stated on your transcript if your GPA is calculated on a different scale, and international admissions will calculate the equivalent.

The Kahlert School of Computing does not require a computer science bachelor’s degree. You must demonstrate coding and computer science experience to gain admission and succeed in the program, however. This requirement acknowledges that strong technical preparation can come from various educational backgrounds, including mathematics, engineering, or self-directed learning combined with professional experience.

Four years of study is required for most international university degrees. The Graduate School accepts completed international three-year bachelor’s degrees under specific conditions: a minimum of 120 hours of earned credits, accompanied by a completed graduate degree or certificate with a 3.0 or better GPA, or conferred by an institution in Bologna Process participant member nations.

The department can petition for waivers when applicants don’t meet formal requirements but demonstrate clear qualifications. Certain three-year degree programs offer solid preparation for graduate work in computing, and the department will request waivers only for clearly qualified applicants.

You’ll submit your application through the University of Utah’s online system. Note your status in your statement of purpose and upload available transcripts to date if you’re currently finishing your degree. You can be admitted based on this information, with official transcripts verified before the semester starts. You must complete your bachelor’s degree before starting the graduate program.

Application fees cost $55.00 for domestic applicants and $65.00 for international students. International applicants must meet English proficiency requirements as the Office of Admissions sets them.

Tuition and Financial Aid

The University of Utah charges $10,848 for in-state students and $35,198 for out-of-state students annually. These figures include both tuition and fees. The total cost, including room and board, books and supplies, transportation, and personal expenses, reaches $34,879 for in-state students and $58,434 for out-of-state students.

The net price averages $22,958 for in-state students and $43,883 for out-of-state students receiving need-based aid. This represents the average cost after aid and scholarship funds are discounted from the total cost.

Most MS students do not receive financial support, and you should plan to cover the full cost of tuition. Opportunities exist to offset expenses through assistantships, however. MS students can apply for teaching assistantships with the department or approach individual faculty members about research assistantships. These opportunities aren’t available for first-year students, but strong performance in your first year may lead to TA positions in your second year.

Teaching assistants receive $10,750 per semester for a 20-hour appointment. You become eligible for tuition benefit when you work as a TA or RA, which provides reduced or waived tuition for that term. This benefit reduces your out-of-pocket costs substantially if you secure an assistantship position.

Research assistantships involve working with a research group, while teaching assistantships require supporting a course through activities like running lab sections, grading homework, and holding office hours. The competitive nature of these positions means you should not rely on assistantships to cover your expenses, but they represent valuable opportunities for qualified students who excel.

Program Format and Duration

The program operates on a semester system with full-time status defined as 9 credits or more. Full-time students complete the program in 2 years, while part-time students take 2.5 to 4 years to finish. The 9-credit-per-term requirement for full-time status implies graduation in about 3 semesters (9/9/12 credit distribution), though this timeline can extend based on your course load decisions.

You can pursue the degree at your own pace within the 4-year maximum timeframe. Part-time study remains feasible while maintaining full-time employment, as long as you’re not bound by scholarship or housing requirements mandating full-time enrollment. This flexibility accommodates working professionals seeking to advance their careers through a masters in AI and machine learning without leaving their current positions.

The program delivers courses on campus, though the university has expanded online offerings. The institution offers continuing education courses that introduce how AI is shaping various sectors and provide practical AI skills. The university also partnered with organizations to offer bootcamp-style programs, though these represent separate professional education pathways rather than the traditional MS track.

You’ll complete your degree through a combination of coursework and potential research opportunities. The program structure allows you to focus on your areas of interest while meeting core requirements that ensure breadth across AI fundamentals. Your academic advisor can help you work through course selections and identify faculty whose research aligns with your goals, particularly if you’re interested in transitioning to a PhD program or pursuing research-oriented career paths.

2. Utah Tech University – Master in Artificial Intelligence (MAI)

Program Overview and Specializations

Utah Tech University launched its Master in Artificial Intelligence (MAI) as a graduate program built for industry application rather than doctoral preparation. The 30-credit degree prepares you to address complex, real-life problems through AI and machine learning methodologies. You’ll gain experience in high-demand domains that cover computer vision, natural language processing, intelligent agents, and generative AI.

The program’s structure reflects workforce needs through its stackable credential approach. The MAI builds on the Post-Baccalaureate Certificate in Applied Artificial Intelligence and Machine Learning, which has the first 15 credits and serves as an interim credential. This design allows you to earn a workforce-relevant certificate while keeping a clear pathway toward completing the full master’s degree. Students enrolled in the certificate can progress into the MAI by submitting an internal form rather than a new application.

You’ll demonstrate technical competency in core AI domains that cover deep learning, generative AI, large language models, and intelligent systems by the program’s conclusion. The curriculum trains you to construct complete machine learning pipelines covering data collection, preprocessing, model training, evaluation, and deployment. You’ll apply AI and ML techniques to diverse real-life problems in a variety of industries while communicating solutions through data visualization and model interpretability. The program has ethical and responsible AI practices that promote fairness, transparency, and accountability in model development.

Curriculum and Course Structure

The program requires 30 graduate credit hours structured around six lecture-based courses and four project-based courses. Five foundational courses have three lecture-based and two project-based components covered in the certificate portion. Five additional advanced courses extend this foundation and guide you toward domain-specific implementations.

Your coursework follows a cohort model with students admitted each fall to promote collaboration and continuity through shared projects. The program spans six semesters across two calendar years. Students take a manageable 5 credits per semester across fall, spring, and summer terms. This allows steady progress while balancing full-time employment.

The Advanced Deep Learning course covers state-of-the-art architectures and optimization strategies in different areas. You’ll integrate and apply AI techniques to real-life problems through two consecutive project courses before moving into advanced implementations. The capstone serves as your final project course with increased rigor that builds across the project series. An elective option, CS 6359R Artificial Intelligence and Machine Learning Independent Project, allows 1-3 credits of specialized work.

Applied coursework and faculty-mentored projects are the program’s foundation. The degree targets industry professionals seeking to design, implement, and deploy AI tools in practice. Project-based learning provides hands-on experience with end-to-end machine learning pipelines. This emphasis distinguishes the MAI from research-focused programs that prepare students for doctoral study.

Admission Requirements

You must complete an online application to begin the admission process. A bachelor’s degree from a regionally accredited institution in Computer Science, Engineering, Mathematics, or a related technical field satisfies the educational requirement. You can also qualify with a bachelor’s degree in any field if you have a minimum of two years of professional software development experience.

Applicants whose native language is not English must demonstrate English proficiency per Utah Tech graduate policy. International students must demonstrate proficiency in Standard American English according to university standards.

The program admits students as cohorts each fall. This cohort structure makes collaboration easier through shared projects and keeps continuity across the two-year curriculum. Students enrolled in the Post-Baccalaureate Certificate in Applied Artificial Intelligence and Machine Learning can transition easily by submitting an internal program approval form rather than submitting a new application.

Transfer credit provisions allow flexibility for students with prior graduate work. You can transfer up to 10 credits earned at an accredited institution toward the degree upon program approval. This policy supports students transitioning from related graduate study while preserving Utah Tech’s academic standards. But at least 20 credits must be completed at Utah Tech University to satisfy residency requirements.

Tuition and Financial Aid

Graduate tuition at Utah Tech University costs $438.00 per credit hour. For the 30-credit MAI program, your total tuition reaches about $13,140.00. This rate applies to the program beginning Fall 2026.

Students from Western states may qualify for reduced tuition through the Western Region Graduate Program Waiver (WRGP). This exchange program allows students to pay 150% of resident tuition for qualified graduate programs. You must be admitted to a participating Utah Tech graduate program and be a legal resident of Alaska, American Samoa, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Washington, Wyoming, Guam, Republic of the Marshall Islands, Federated States of Micronesia, Republic of Palau, or the Commonwealth of the Northern Mariana Islands.

The WRGP considers you upon full acceptance into a qualified graduate program. You must remain in good status with your graduate program and make progress toward your degree to keep eligibility. The waiver has no credit or GPA requirements for each semester. The waiver prorates to your actual credit enrollment if you enroll in fewer than 12 credits in any given semester.

International students pay nonresident tuition and fees. Graduate program tuition varies by program, and scholarships are available to eligible international students to help reduce costs. Utah Tech does not offer scholarships covering the full cost of attendance.

Program Format and Duration

The MAI uses HyFlex delivery and combines in-person and remote options. This modality accommodates working professionals by providing flexibility in how you attend classes. The program structure spreads 30 credits across six semesters in a two-year timeline. With 5 credits per semester across fall, spring, and summer terms, you can keep full-time employment while building AI proficiency at a sustainable pace.

You must keep a 3.0 cumulative GPA in all graduate coursework. You must also complete all coursework with a B- or higher. The program requires at least 20 credits completed at Utah Tech University to satisfy residency requirements.

The 30-credit total has 18 credits from lecture-based courses and 12 credits from project-based courses. Your capstone project serves as the final course in the sequence, with cumulative rigor across the project series rather than a separate standalone requirement. This structure integrates applied learning throughout your studies and builds skills from foundational concepts to advanced implementations.

The program coordinates with Graduate Admissions and Operations to enroll qualified certificate students in the master’s program. This coordination ensures smooth transitions for students completing the Post-Baccalaureate Certificate who wish to continue toward the full degree.

3. Utah Valley University – MS in Applied Artificial Intelligence

Program Overview and Specializations

Utah Valley University announced its Master of Science in Applied Artificial Intelligence (MS-AAI) alongside the establishment of the Applied Artificial Intelligence Institute in 2024, with student enrollment beginning fall 2025. The MS-AAI program gives professionals from diverse backgrounds expertise to apply AI technologies strategically and craft effective business solutions. This program focuses on practical integration and governance of AI and guides you in leveraging AI in industries of all types.

The program addresses the growing need for AI professionals in the workforce. According to program data, 73% of companies already adopted AI into their business operations as of 2024. AI Product Managers earn an average salary of $185,602. Professionals with master’s degrees earn up to 33% more per year compared to bachelor’s graduates. These market factors demonstrate the value proposition of this AI masters for career advancement.

You’ll showcase your aptitude in deploying effective ground AI applications through an applied AI capstone project. This capstone represents the culmination of your studies and requires you to integrate multiple competencies developed throughout the curriculum. Individuals who aspire to lead AI-driven innovations and promote ethical AI practices in their organizations will be well-prepared upon completion.

Curriculum and Course Structure

The MS-AAI requires 30 total credit hours structured around 27 credits of discipline core requirements and 3 credits of electives. Your curriculum includes foundational and advanced courses that cover topics such as AI strategy and implementation, data management, and ethical implications. This balance between technical depth and strategic thinking prepares you for leadership roles in AI deployment.

The discipline core has nine required courses spanning the full spectrum of applied AI competencies. You can select from courses that include Management of Technological Innovation, Intellectual Property Fundamentals, Healthcare Systems/Finance/Operations, Six Sigma Project Management, Cloud Computing, Artificial Intelligence, Deep Learning, People and Culture, or Independent Study to satisfy your 3-credit elective requirement. This flexibility allows you to tailor your studies toward specific industry applications or technical specializations.

All courses must be completed with an overall GPA of 3.0 or higher. Each course requires a grade of C or higher to satisfy graduation requirements. Your coursework must be finished within a five-year period, as no courses older than five years will apply toward graduation. This time constraint keeps your knowledge current in the AI field that evolves faster.

Transfer credits provide some flexibility if you’ve completed relevant graduate work elsewhere. You may transfer up to ten semester credit hours into this master’s program, on condition that these courses receive approval from the graduate program faculty. You must complete at least 20 hours at Utah Valley University to satisfy residency requirements, though. Graduate credits from another accredited institution must have been completed within four years of your matriculation and cannot be older than six years at graduation.

Admission Requirements

You must hold a bachelor’s degree from an accredited university to qualify for admission. The program requires an undergraduate GPA of 3.0 or higher on a 4.0 scale. This GPA can be calculated either across all undergraduate courses or from your most recent 60 credit hours, whichever is greater. Students in extraordinary circumstances may request an exception by appealing to the Graduate Council Appeals Committee, though programs cannot waive or adjust this requirement independently.

Your application materials include official transcripts from all institutions of higher education attended, three professional letters of recommendation, and a personal statement. The GRE exam is waived for this program. Prior work experience is preferred but not required for admission. Students with several years or more of work experience benefit from the program as it builds on practical knowledge gained in the workplace. Students learn more from each other when they have experiences to share. Applicants without prior work experience receive full consideration and may be admitted depending on availability.

The MS-AAI program is not yet open to international students from outside the United States, however. Only U.S. citizens or permanent residents (green card holders) may be accepted currently. This restriction limits enrollment to domestic applicants at this time.

Tuition and Financial Aid

The MS-AAI charges the same resident tuition rate to students living out-of-state as in-state students as a fully online program. This tuition equity makes the program available whatever your geographic location within the United States. You can review specific MS-AAI program tuition rates on the graduate programs tuition page.

UVU offers Graduate Tuition Waiver Scholarships for both residents and non-residents. These scholarships can offset your tuition costs, though availability varies. You should contact the program coordinator for current scholarship information and application procedures.

Many employers support employees in attending UVU’s graduate programs. You should check with your employer to determine if they provide financial support for graduate education. Employer tuition assistance represents a significant funding source for working professionals pursuing their masters in AI and machine learning.

Program Format and Duration

The program provides flexible online and in-person learning options within its 30-credit-hour structure and is designed for full-time working professionals. This hybrid approach accommodates your work schedule while maintaining program rigor. The online component allows you to complete coursework from any location, while optional in-person elements provide networking opportunities with faculty and peers.

The MS-AAI uses a cohort model where students are grouped together and progress through the curriculum as a unit. You’ll take two three-credit-hour courses per semester to finish 10 courses (30 credit hours total) in five semesters, spanning 20 months. Students benefit from staying together as a group and progressing through the program quickly.

You may take fewer than two courses per semester if you have scheduling conflicts or other needs. This flexibility accommodates variations in your work demands or personal circumstances. You must finish the program within six years. Students may also take more than 30 credit hours by adding elective courses to their schedule, provided they finish within the six-year time limit.

An online graduation plan offers a flexible yet structured approach to your academic experience. While sample plans serve as general guidelines, individual plans may differ based on your specific circumstances. Meeting with an academic advisor is recommended to customize your plan and ensure all graduation requirements are met. Your advisor can help you balance course sequencing with your professional commitments to optimize your learning experience in this online masters in AI.

4. Udacity Institute – Online Masters in AI

Program Overview and Specializations

The Udacity Institute of AI and Technology offers a fully accredited online masters in AI through partnership with Woolf, a European higher education institution. This represents the first AI degree recognized around the world and built on Udacity’s project-based learning model. Woolf holds accreditation from Malta’s Further and Higher Education Authority. Degrees are recognized in 60+ countries through European Credit Transfer System (ECTS) credits. The program combines academic credentials with practical AI training and positions you for career advancement without geographic constraints.

Curriculum and Course Structure

You’ll complete 2,250 credit hours equivalent to 11-14 Nanodegree programs plus a capstone project. The core curriculum requires 875 hours across mandatory modules that include Generative AI, Agentic AI, Introduction to Machine Learning with PyTorch, and Artificial Intelligence. You’ll then complete 625 elective hours from over 20 available Nanodegree programs such as Computer Vision, Natural Language Processing, and AI Trading Strategies. Your capstone involves seven industry-style projects where you design, build, and justify complete AI systems.

Admission Requirements

A bachelor’s degree from an accredited institution qualifies you for direct admission. Performance-Based Admission (PBA) provides an alternative pathway if you lack a traditional degree and requires no prior qualifications. Prerequisites include foundational Python programming knowledge, basic mathematics and statistics background, and English proficiency. A non-refundable enrollment fee of $199-200 applies.

Tuition and Financial Aid

Your Udacity subscription remains a prerequisite for enrollment. Recognition of Prior Learning converts eligible past Udacity coursework into academic credit and can reduce completion time.

Program Format and Duration

This online masters in AI operates self-paced. You can learn on your own schedule with an active subscription. Most learners complete the degree in 18-24 months. You can pause your subscription for up to 90 days while you retain progress. Technical mentors, project reviewers, and community support assist you throughout your studies.

Next Steps

Just to repeat, each of these four programs offers distinct advantages depending on your career goals and circumstances. The University of Utah provides detailed AI research opportunities with potential teaching assistantships. Utah Tech University delivers industry-focused training through its HyFlex model at an affordable price point. Utah Valley University emphasizes AI implementation strategies for working professionals, while Udacity Institute offers exceptional flexibility and global recognition at under $5,000.

Choosing between these options may seem overwhelming. You can simplify your decision by identifying your priorities. Determine whether you prefer traditional campus experiences or online learning. Assess your budget constraints and timeline priorities. Pick the program that lines up best with your professional objectives and learning style once you’ve clarified these factors.