| Graduate Program Head | Kevin Linderman |
|---|---|
| Program Code | AAIBT |
| Campus(es) | University Park |
| Degrees Conferred | Master of Artificial Intelligence (M.A.I.) |
| The Graduate Faculty |
The Master of Applied Artificial Intelligence for Business Transformation program is designed to equip students with applied AI technical foundations, alongside the strategic business acumen to leverage these technologies for organizational change and innovation. Graduates will be able to bridge the gap between AI capabilities and business needs, driving digital transformation initiatives, optimizing processes, and creating data-driven strategies to give companies a competitive edge in the rapidly evolving AI-powered business landscape. With core course work in data analytics, prompt engineering, change management, teaming, industry-specific applications, models and platforms and AI integration across organizations. The core curriculum will focus on AI in business analytics, AI management, and workflow.
A solid foundation in AI applications and analytics, prompt engineering and organizational change inspired by AI, will make the target audiences more attractive for positions requiring knowledge, skill and experience in AI integration. These learning outcomes will be achieved by a combination of lectures by faculty, invited guest lecturers, reading of key literature, individual and team projects, and practical involvement in a culminating project.
Admission Requirements
Applicants apply for admission to the program via the J. Jeffrey and Ann Marie Fox Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.
Applicants apply for admission to the program via the Fox Graduate School application for admission. The following are required:
- Baccalaureate degree with a 3.0 minimum undergraduate GPA (or equivalent).
- Submission of a completed online Fox Graduate School Application for Admission, including a Statement of Purpose, and Resume.
- Official transcripts from all post-secondary institutions attended.
The language of instruction at Penn State is English. English proficiency test scores (TOEFL/IELTS) may be required for international applicants. See GCAC-305 Admission Requirements for International Students for more information.
Core Application Packet
- Completed online Fox Graduate School application and payment of nonrefundable application fee.
- Statement of purpose: a 2–3-page essay articulating career and educational goals that demonstrates written communication skills.
- Vita or Résumé.
- Official transcripts from all post-secondary institutions attended.
- Candidates may be asked to participate in a video interview as part of the admissions process.
Degree Requirements
Requirements listed here are in addition to Graduate Council policies listed under GCAC-700 Professional Degree Policies.
The 30-credit program will be delivered in a resident hybrid model with 25 percent of the instruction of each course on-campus and 75 percent of the instruction of each course delivered in a live remote synchronous
manner. The program is designed to serve audiences that want the community and learning experience of a residential program with the flexibility of traditional part-time programs.
The Master of Applied AI for Business Transformation requires a minimum of 30 credits at the 400, 500, or 800 level, with at least 18 credits at the 500 or 800 level.
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| BAN 830 | Descriptive Analytics for Business | 3 |
| BAN 840 | Predictive Analytics for Business | 3 |
| BAN 850 | Prescriptive Analytics for Business | 3 |
| BAI 810 | Interaction with AI | 3 |
| BAI 811 | Implications of AI on Organizations and Teams | 3 |
| BAI 812 | Understanding AI: Business-Specific Utilization and Trends to Watch | 3 |
| BAI 813 | AI Models and Platforms | 3 |
| BAI 814 | Customization of AI | 3 |
| Electives | ||
| Select three credits from a prescribed list of professional graduate courses offered by the Smeal College of Business. | 3 | |
| Culminating Experience | ||
| BAI 815 | AI Integration in Business Workflows | 3 |
| Total Credits | 30 | |
Minor
A graduate minor is available in any approved graduate major or dual-title program. The default requirements for a graduate minor are stated in Graduate Council policy GCAC-218 Minors.
Student Aid
Refer to the Tuition & Funding section of the J. Jeffrey and Ann Marie Fox Graduate School's website. Students in this program are not eligible for graduate assistantships.
Courses
Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.
Learning Outcomes
- KNOW: Graduates will demonstrate broad understanding of AI concepts and methodologies, including their theoretical foundations and practical applications within business contexts and strategy.
- APPLY/CREATE: Graduates will apply AI tools and techniques to create and implement innovative solutions that address complex organizational and team challenges, generate measurable value, and advance new applications or methods.
- COMMUNICATE: Graduates will translate AI concepts, engagement outcomes and strategic business implications clearly and persuasively to multiple audiences, including management, teams, and non-technical stakeholders in context of decision making and management.
- THINK: Graduates will analyze and evaluate AI’s strategic impact on business models, processes, people, and industries, demonstrating the ability to think critically, assess trade-offs, understand integration, and make data-informed decisions.
- PROFESSIONAL PRACTICE: Graduates will model responsible AI leadership, teamwork, upholding ethical standards, mitigating bias, and ensuring ethical compliance in the design, deployment, and governance of AI systems.