When you click on a sponsoring school or program advertised on our site, or fill out a form to request information from a sponsoring school, we may earn a commission. View our advertising disclosure for more details.
Jon Reifschneider is executive in residence at Duke University’s Pratt School of Engineering, where he is the founding director of the master of engineering in artificial intelligence program and executive director of the AI for product innovation master’s program. At Duke, he also leads the Center for Research & Engineering of AI Technology in Education (CREATE), an applied research lab focused on enhancing teaching and learning through AI.
Prior to his academic roles, Reifschneider spent over 15 years in industry developing advanced analytics products, most recently as Senior Vice President at DTN, where he led the Weather Analytics division. His predictive systems have been deployed across the transportation, aviation, and energy sectors. Reifschneider is also the co-founder and CEO of Inquisite AI, a leading platform for research and science teams that leverages artificial intelligence.
Reifschneider holds a BS in mechanical engineering from the University of Virginia, a master of engineering management from Duke University, an MS in analytics from Georgia Tech, and a global MBA from EBS University of Business and Law in Germany.
OnlineEngineeringPrograms.com: What’s something you wish the public understood about AI?
Reifschneider: I wish that the public had a foundational understanding of how large language models (LLMs) work. This would make them much less of a mystery, and help people understand their limitations and when they should or should not be used.
OnlineEngineeringPrograms.com: Do you have any advice for young engineers who want to study and work with AI?
Reifschneider: Having a solid foundation in math and science is critical to understanding how AI works. But beyond that, just go ahead and get started! There are lots of great resources available online to dig into AI, many of them free. And the best way to learn is to start using it—pick a project that involves AI and just start building.
OnlineEngineeringPrograms.com: What does the future of this field look like to you?
Reifschneider: AI is increasingly impacting every aspect of our lives and our work. More and more professionals from various fields are applying it to solve challenging problems in areas as diverse as biology, meteorology, healthcare, etc. I am optimistic that this will significantly increase the rate of progress in these fields, leading to a healthier and more sustainable world.
Duke University Pratt School of Engineering
Duke University’s Pratt School of Engineering offers a master of engineering in artificial intelligence for product innovation, a program designed to bridge the gap between advanced AI techniques and practical product development. This program emphasizes hands-on experience, preparing students to apply AI and machine learning to real-world challenges across various industries.
The curriculum comprises 30 credit-hours, including eight technical courses covering areas such as deep learning applications, modeling processes and algorithms, and sourcing data for analytics. Students also engage in two business and management courses to develop leadership skills, culminating in a capstone project that involves solving real-world problems in collaboration with industry partners.
Admission requirements include a bachelor’s degree in engineering, computer science, or a related field, along with a completed application, transcripts, letters of recommendation, and a personal statement. Notably, GRE scores are optional, and the program offers a preparatory online summer boot camp for those needing to strengthen their programming skills.
Graduates of this program are well-equipped for roles such as AI product managers, machine learning engineers, and data scientists, with opportunities in leading tech companies and startups. The program’s strong industry connections and emphasis on practical experience position students to make immediate contributions in the rapidly evolving field of artificial intelligence.
Johns Hopkins Whiting School of Engineering
Johns Hopkins University’s Whiting School of Engineering offers a fully online master of science in artificial intelligence through its engineering for professionals (EP) program. This program is designed for working professionals seeking to deepen their expertise in AI and apply it to real-world challenges across various industries.
The curriculum requires the completion of 30 credits. Core courses include applied machine learning and artificial intelligence, providing a foundation in AI principles. Other required courses cover advanced topics such as generative AI. Electives include subjects such as production AI, values and ethics in AI, and the theory and practice of large language models (LLMs).
The program emphasizes both theoretical understanding and practical application, preparing graduates to develop and implement AI solutions effectively. Courses are taught by subject-matter experts actively engaged in the field, ensuring that the curriculum remains current with industry advancements.
Admission requirements include a bachelor’s degree in a relevant field, official transcripts, and a completed application. International applicants may have additional requirements.
George Washington University’s School of Engineering and Applied Science offers a fully online master of engineering in artificial intelligence and machine learning. The program emphasizes a balance between advanced algorithmic understanding and real-world application, preparing graduates for roles in various industries, including technology, finance, healthcare, and transportation.
The curriculum comprises ten courses, totaling 30 credit hours, and includes subjects such as machine learning for engineers, natural language processing with deep learning, computer vision, autonomous systems and robotics, and AI security, privacy, and ethics. Students also complete a capstone project, allowing them to apply their learning to real-world problems. The program offers both synchronous and asynchronous learning options, providing flexibility for working professionals.
Admission requirements include a bachelor’s degree in engineering, computer science, physical sciences, mathematics, economics, or business administration, with a minimum GPA of 2.7. Applicants should have completed at least one course in college-level calculus and one in statistics. GRE scores are optional.
The University of Pennsylvania’s School of Engineering and Applied Science offers a fully online master of science in engineering (MSE) in artificial intelligence, designed to equip professionals with both the technical expertise and ethical framework necessary to lead in the evolving field of AI. Graduates of the MSE in Artificial Intelligence program are well-prepared for roles such as AI engineers, machine learning specialists, and data scientists, equipped to navigate the technical challenges and ethical considerations of AI in various industries.
The curriculum comprises ten courses: seven core courses, two technical electives, and one free elective. Core courses include artificial intelligence; natural language processing; machine learning for data science; statistics for data science; principles of deep learning; and either internet and web systems or GPU computing for machine learning systems. Electives are offered in areas such as networked systems, computer vision, and computational photography, and an as-yet-untitled AI practicum.
Admission requirements include a bachelor’s degree in computer science, computer engineering, or a related field. Applicants must submit a resume, personal statement, and letters of recommendation. GRE scores are not required.
Worcester Polytechnic Institute (WPI) offers a master of science in artificial intelligence (MS-AI), a program designed to equip students with both the theoretical foundations and practical skills necessary to excel in the rapidly evolving field of AI. Available both on campus and online, the program emphasizes project-based learning, allowing students to apply AI techniques to real-world challenges across various industries.
The 30-credit curriculum comprises five core courses, each selected from distinct thematic areas: artificial intelligence, fairness/ethics & AI, machine learning, knowledge representation & reasoning, and interaction & action. Students can tailor their education through a range of electives and have the option to complete either a three-credit capstone project or a nine-credit master’s thesis, both designed to provide hands-on experience in applying AI methodologies.
WPI’s interdisciplinary approach allows students to specialize in areas such as AI & security, AI & health, AI & software systems, and more, reflecting the diverse applications of AI in today’s world. The program’s flexibility accommodates students from various academic backgrounds, including computer science, data science, mathematics, and engineering. Applicants are expected to have a quantitative and computational background, with coursework in programming, linear algebra, and statistics. GRE scores are not required for admission.
Graduates of the MS-AI program are well-prepared for careers in AI research, development, and application across sectors such as healthcare, finance, robotics, and cybersecurity. The program’s strong emphasis on ethical considerations ensures that students are not only technically proficient but also mindful of the societal impacts of AI technologies.
Colorado State University Global offers a fully online master of science in artificial intelligence and machine learning, designed to equip professionals with the skills necessary to develop and implement AI-driven solutions across various industries. The program emphasizes both theoretical foundations and practical applications, preparing graduates for roles in sectors such as healthcare, manufacturing, and automotive.
The curriculum comprises ten core courses, totaling 30 credit hours. Courses include: principles of programming; design and analysis of algorithms; foundations of AI; principles of ML; and a capstone project on applying machine learning and neural networks. Students gain hands-on experience with tools like TensorFlow and programming languages such as Python, enabling them to develop solutions capable of modeling human behavior and evaluating AI application performance.
Admission requirements include a bachelor’s degree and completion of advanced coursework in discrete mathematics and probability and statistics. Applicants lacking these prerequisites may seek approval from the program chair and complete equivalent courses within the first 12 months of the program. The program is delivered entirely online through asynchronous, 8-week courses, with monthly start dates, allowing for flexibility in completion timelines.
Graduates are prepared for roles such as AI Engineer, Machine Learning Engineer, and Software Developer, with the program providing a strong foundation in both AI principles and their practical applications.
Purdue University offers a fully online master of science in artificial intelligence (MSAI), designed to equip professionals with both technical expertise and strategic insight in the rapidly evolving field of AI. The program provides two distinct tracks to cater to diverse backgrounds and career goals: the AI and machine learning major, and the AI management and policy major.
The AI and Machine Learning track is tailored for individuals with a strong foundation in programming and mathematics. It explores advanced topics such as machine learning, data mining, natural language processing, and statistical analysis. Students engage in hands-on projects, including a capstone experience, to apply their skills to real-world challenges. Prerequisites for this track include prior coursework in algorithms, calculus, linear algebra, and probability theory, as well as programming experience in languages like Python.
The AI Management and Policy track is designed for professionals aiming to lead AI initiatives without a deep technical background. This track focuses on the ethical, legal, and societal implications of AI, as well as leadership and policy development. Applicants are expected to have at least 24 months of relevant work experience.
Both tracks require the completion of 30 credit hours and culminate in a capstone project that synthesizes learning and demonstrates practical application. Courses are delivered asynchronously, providing flexibility for working professionals. Students can complete the program in as few as 18 months, depending on their course load.
The University of San Diego’s Shiley-Marcos School of Engineering offers a fully online master of science in applied artificial intelligence (MS-AAI), tailored for professionals aiming to apply AI solutions across various sectors. This program emphasizes both technical proficiency and ethical considerations, preparing graduates to implement AI responsibly in real-world scenarios.
Structured over five semesters, the 30-unit curriculum includes foundational courses such as probability and statistics for artificial intelligence and an introduction to artificial intelligence. This is followed by advanced topics such as machine learning fundamentals and applications, neural networks and deep learning, and natural language processing. The program culminates in a comprehensive capstone project, allowing students to develop AI-driven solutions to practical problems, often in collaboration with industry partners.
Designed for flexibility, students undertake one seven-week course at a time, completing two courses per 14-week semester. Admission requirements include a bachelor’s degree in a STEM field with a minimum GPA of 2.75. Applicants with non-STEM backgrounds may be considered based on relevant experience and a personal statement. Proficiency in programming languages such as Python, C, or MATLAB is expected.
Graduates are equipped for roles including AI Engineer, Machine Learning Specialist, and Business Intelligence Developer, with alumni employed at organizations like Microsoft, Illumina, and Booz Allen Hamilton.
The University of Michigan–Flint offers a master of science in artificial intelligence (MSAI) program designed to provide students with a comprehensive understanding of AI principles and practical applications. The program is available in both online and on-campus formats, offering flexibility to accommodate diverse learning preferences and schedules.
The MSAI curriculum requires the completion of 32 credit hours. Core courses establish a foundation in machine learning, artificial intelligence, and software engineering. Students can select from eight concentration areas to tailor their studies to specific interests and career goals: machine learning; embedded artificial intelligence; edge AI and the internet of things; robotics and human-centered design AI; smart manufacturing and industry 4.0; augmented and virtual reality; AI and data analytics; and AI and cybersecurity.
The program emphasizes real-world application through team-based projects and offers opportunities for students to engage in AI and machine learning research alongside faculty members. For students without a background in computing, UM-Flint provides fast track options to develop proficiency in programming, object-oriented programming, and data structures.
Admission requirements include a bachelor’s degree, with provisions for students from non-computing fields to acquire necessary competencies during the program. The MSAI program can be completed entirely online, on campus, or through a hybrid approach, catering to the needs of working professionals and full-time students alike.
Western Governors University (WGU) offers a fully online master of science in computer science with a specialization in artificial intelligence and machine learning (AI/ML), tailored for professionals seeking to deepen their expertise in AI technologies and applications. This program emphasizes a balance between theoretical foundations and practical skills, preparing graduates for advanced roles in AI-driven industries.
The curriculum comprises ten courses, totaling 31 competency units (CUs), and includes subjects such as AI and ML foundations, ML for computer scientists, deep learning, natural language processing, and advanced AI for computer scientists. Students also engage with topics like formal languages, computer architecture and systems, and governance, risk, and compliance, ensuring a comprehensive understanding of both AI technologies and their ethical implications The program culminates in a capstone project, allowing students to apply their learning to real-world problems.
WGU’s competency-based education model enables students to progress through the program by demonstrating mastery of subject matter, offering flexibility for working professionals. Admission requirements include a bachelor’s degree in computer science, information technology, or a related field. Applicants should possess a strong foundation in programming and mathematics to ensure readiness for the program’s rigor.
Graduates of the program are well-prepared for roles such as AI engineer, machine learning engineer, and data scientist, equipped with the knowledge and skills to develop and implement AI solutions across various sectors.
The Bureau of Labor Statistics does not currently offer a specific and singular occupational category for AI engineers. However, AI engineers generally fall into one of two categories: software developers, who build AI production systems and applications; and computer and information research scientists, who research AI/ML and develop the algorithms that power it. Many engineers working in AI will overlap between these two categories, and even spill over into others — however, the employment prospects and their accompanying salaries remain altogether extremely positive.
According to the Bureau of Labor Statistics (BLS 2025), the career outlook for software developers is promising, with overall employment projected to grow 17 percent in the next decade, which is much faster than the average of 4 percent for all occupations from 2023 to 2033.
On average, software developers earn $144,570 per year The percentiles for wages for software developers are:
According to the Bureau of Labor Statistics (BLS 2025), the career outlook for computer and information research scientists is also promising, with overall employment projected to grow 26 percent in the next decade, which is much faster than the average of 4 percent for all occupations from 2023 to 2033.
On average, computer and information research scientists earn $152,310 per year The percentiles for wages for computer and information research scientists are:
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineering faculty at top universities, highlighting their research in areas such as nuclear power, artificial intelligence, soft robotics, and semiconductor design. He’s also worked with leaders and subject matter experts at the Institute for Electrical and Electronics Engineers (IEEE), the California Department of Water Resources (DWR), the National Society of Professional Engineers (NSPE), and the Society of Women Engineers (SWE).
Today, digital twins are not limited to just physical objects. With the rise of virtual and augmented reality technologies, digital twins can now replicate entire environments and systems in a virtual space. This has opened up new possibilities for testing and simulation, allowing companies to reduce costs and risks associated with physical prototypes.
Diversity and inclusivity aren’t purely idealistic goals. A growing body of research shows that greater diversity, particularly within executive teams, is closely correlated with greater profitability. Today’s businesses are highly incentivized to identify a diverse pool of top talent, but they’ve still struggled to achieve it. Recent advances in AI could help.
The ability of a computer to learn and problem solve (i.e., machine learning) is what makes AI different from any other major technological advances we’ve seen in the last century. More than simply assisting people with tasks, AI allows the technology to take the reins and improve processes without any help from humans.
This guide, intended for students and working professionals interested in entering the nascent field of automotive cybersecurity, describes some of the challenges involved in securing web-enabled vehicles, and features a growing number of university programs, companies, and people who are rising to meet those challenges.
Unlike fungible items, which are interchangeable and can be exchanged like-for-like, non-fungible tokens (NFTs) are verifiably unique. Broadly speaking, NFTs take what amounts to a cryptographic signature, ascribe it to a particular digital asset, and then log it on a blockchain’s distributed ledger.
