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Program Description
The Bachelor of Science in Artificial Intelligence Methods and Applications is an interdisciplinary program that will prepare students for careers in the rapidly advancing field of artificial intelligence (AI).
Graduates of this program will have strong foundations in core technical skills and AI methodology knowledge including Mathematics for AI, Programming for AI, Knowledge Representation and Inference, AI Problem Solving, and Socially Responsible AI. Additional elective courses will allow students to gain advanced AI technical skills, introduce them to specialized areas within AI and related fields, and broaden their knowledge in chosen application focus areas. The program integrates strengths across a broad range of areas of AI and related disciplines, e.g. computer science, information science, human-computer interaction, data sciences, psychology and cognitive sciences, social sciences, public policy and law, and is a truly innovative and interdisciplinary Bachelor of Science program in AI. Students in their final program year will complete a two-semester capstone course designed to offer practical experience in applying AI to real-world problems or a two-semester research project aimed to offer undergraduate research experience in AI. Students will also gain soft skills necessary for professional success through experiential learning and active participation in real-world projects and team collaboration.
What is Artificial Intelligence Methods and Applications?
The Artificial Intelligence Methods and Applications program integrates AI foundations and techniques, mathematics, computer science, psychology, public policy, and ethics to develop AI solutions for diverse domains. This program could be a great fit if you want to:
- Fundamentally transform diverse application domains through judicious applications of AI.
- Design AI systems that enhance human capacity for decision making, discovery, and creativity.
- Balance technical excellence with ethical reasoning, effective communication, and interdisciplinary collaboration.
- Lead responsible AI initiatives in organizations ranging from tech companies to research institutions, government agencies, and nonprofits.
AI is transforming industries, organizations, scientific discoveries, and society. Professionals with a strong foundation in AI technologies and a commitment to ethical and interdisciplinary thinking are in high demand. AIMA prepares students to tackle real-world problems with AI solutions while ensuring that solutions enhance human judgment, creativity, and well-being. The program balances core technical skills with communication, teamwork, and applied experience, helping students thrive in dynamic, AI-enabled environments. You'll learn:
- Core AI methods for automated problem-solving, knowledge representation, reasoning, and decision-making, natural language processing, multi-agent interaction, multimedia content generation, and coordination
- Skills for tackling real-world challenges such as imprecision and uncertainty, noisy data, eliciting stakeholder needs, managing their expectations, and scaling up solutions as needed
- Human-centered, responsible design, deployment, and governance of AI
- Interdisciplinary collaboration, learning to bridge technical and domain expertise across fields
- Communication skills for explaining AI opportunities and risks to diverse stakeholders
You Might Like This Program If...
- You’re interested in how AI can analyze data to improve decision-making and expedite discovery in business and science.
- You enjoy developing algorithms, analyzing data, and creating AI models for real-world applications.
- You’re excited about exploring the transformative power of AI in fields like software development, finance, health care, marketing, customer service, or transportation.
Entrance to Major
To be eligible for the Artificial Intelligence Methods and Applications major, students must:
- Have completed the following courses with a grade of C or better in each prior to enrolling in the degree program: MATH 140, CMPSC 131, CMPSC 132, (STAT 200 or DS 200), A-I 100.
- Have achieved a minimum grade point average of 2.0 prior to and through the end of the semester during which the entrance to major is requested.
Degree Requirements
For the Bachelor of Science degree in Artificial Intelligence Methods and Applications, a minimum of 120 credits is required:
| Requirement | Credits |
|---|---|
| General Education | 45 |
| Electives | 14 |
| Requirements for the Major | 67 |
6 of the 45 credits for General Education are included in the Requirements for the Major. This includes: 6 credits of GQ courses.
Requirements for the Major
A grade of C or better is required for all courses in the major. To graduate, a student enrolled in the major must earn at least a C grade in each course designated by the major as a C-required course, as specified by Senate Policy 82-44.
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| A-I 100 | Artificial Intelligence: Automated Thinking to Augment Human Intellect  | 3 |
| A-I 341W | Responsible Artificial Intelligence | 3 |
| A-I 370 | Problem Formulation and Automated Problem Solving | 3 |
| A-I 375 | Knowledge Representation and Inference | 3 |
| CMPSC 131 | Programming and Computation I: Fundamentals | 3 |
| CMPSC 132 | Programming and Computation II: Data Structures | 3 |
| MATH 140 | Calculus With Analytic Geometry I | 4 |
| MATH 225 | Mathematical Foundations for Machine Learning | 4 |
| STAT 401 | Experimental Methods | 3 |
| Additional Courses | ||
| Additional Courses: Require a grade of C or better | ||
| Any First-Year Seminar | 1 | |
| CMPSC 360 | Discrete Mathematics for Computer Science | 3 |
| or MATH 311W | Concepts of Discrete Mathematics | |
| DS 200 | Introduction to Data Sciences | 4 |
| or STAT 200 | Elementary Statistics | |
| Select 3 credits from the following: | 3 | |
| Data Structures | ||
| Data Structures and Algorithms | ||
| Algorithmic Methods and Tools | ||
| Select 3 credits from the following: | 3 | |
| Applied Machine Learning in Data Science | ||
| Machine Learning and Algorithmic AI | ||
| Machine Learning for Data Analytics | ||
| Select 6 credits from the following: | 6 | |
| AI Capstone I: Project Design and AI Capstone II: Project Implementation | ||
| Research Project | ||
| Supporting Courses and Related Areas | ||
| Supporting Courses and Related Areas: Require a grade of C or better | ||
| Select 9 credits from the Technical Supporting Course list. At least 3 credits must be at the 400-level. | 9 | |
| Select 9 credits from the Application Supporting Course list. At least 3 credits must be at the 300- or 400-level. Up to 6 credits of ROTC. | 9 | |
General Education
Connecting career and curiosity, the General Education curriculum provides the opportunity for students to acquire transferable skills necessary to be successful in the future and to thrive while living in interconnected contexts. General Education aids students in developing intellectual curiosity, a strengthened ability to think, and a deeper sense of aesthetic appreciation. These are requirements for all baccalaureate students and are often partially incorporated into the requirements of a program. For additional information, see the General Education Requirements section of the Bulletin and consult your academic adviser.
The keystone symbol appears next to the title of any course that is designated as a General Education course. Program requirements may also satisfy General Education requirements and vary for each program.
Foundations (grade of C or better is required and Inter-Domain courses do not meet this requirement.)
- Quantification (GQ): 6 credits
- Writing and Speaking (GWS): 9 credits
Breadth in the Knowledge Domains (Inter-Domain courses do not meet this requirement.)
- Arts (GA): 3 credits
- Health and Wellness (GHW): 3 credits
- Humanities (GH): 3 credits
- Social and Behavioral Sciences (GS): 3 credits
- Natural Sciences (GN): 3 credits
Integrative Studies
- Inter-Domain Courses (Inter-Domain): 6 credits
Exploration
- GN, may be completed with Inter-Domain courses: 3 credits
- GA, GH, GN, GS, Inter-Domain courses. This may include 3 credits of World Language course work beyond the 12th credit level or the requirements for the student’s degree program, whichever is higher: 6 credits
University Degree Requirements
First Year Engagement
All students enrolled in a college or the Division of Undergraduate Studies at University Park, and the World Campus are required to take 1 to 3 credits of the First-Year Seminar, as specified by their college First-Year Engagement Plan.
Other Penn State colleges and campuses may require the First-Year Seminar; colleges and campuses that do not require a First-Year Seminar provide students with a first-year engagement experience.
First-year baccalaureate students entering Penn State should consult their academic adviser for these requirements.
Cultures Requirement
6 credits are required and may satisfy other requirements
- United States Cultures: 3 credits
- International Cultures: 3 credits
Writing Across the Curriculum
3 credits required from the college of graduation and likely prescribed as part of major requirements.
Total Minimum Credits
A minimum of 120 degree credits must be earned for a baccalaureate degree. The requirements for some programs may exceed 120 credits. Students should consult with their college or department adviser for information on specific credit requirements.
Quality of Work
Candidates must complete the degree requirements for their major and earn at least a 2.00 grade-point average for all courses completed within their degree program.
Limitations on Source and Time for Credit Acquisition
The college dean or campus chancellor and program faculty may require up to 24 credits of course work in the major to be taken at the location or in the college or program where the degree is earned. Credit used toward degree programs may need to be earned from a particular source or within time constraints (see Senate Policy 83-80). For more information, check the Suggested Academic Plan for your intended program.
Academic Advising
The objectives of the university's academic advising program are to help advisees identify and achieve their academic goals, to promote their intellectual discovery, and to encourage students to take advantage of both in-and out-of class educational opportunities in order that they become self-directed learners and decision makers.
Both advisers and advisees share responsibility for making the advising relationship succeed. By encouraging their advisees to become engaged in their education, to meet their educational goals, and to develop the habit of learning, advisers assume a significant educational role. The advisee's unit of enrollment will provide each advisee with a primary academic adviser, the information needed to plan the chosen program of study, and referrals to other specialized resources.
READ SENATE POLICY 32-00: ADVISING POLICY
University Park
Undergraduate Academic Advising Center
E103 Westgate Building
University Park, PA 16802
814-865-8947
advising@ist.psu.edu
Suggested Academic Plan
The suggested academic plan(s) listed on this page are the plan(s) that are in effect during the 2025-26 academic year. To access previous years' suggested academic plans, please visit the archive to view the appropriate Undergraduate Bulletin edition.
Artificial Intelligence Methods and Applications, B.S. at University Park Campus
The course series listed below provides only one of the many possible ways to move through this curriculum. The University may make changes in policies, procedures, educational offerings, and requirements at any time. This plan should be used in conjunction with your degree audit (accessible in LionPATH as either an Academic Requirements or What If report). Please consult with a Penn State academic adviser on a regular basis to develop and refine an academic plan that is appropriate for you.
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 140 (GQ)*‡#† | 4 | CMPSC 132*# | 3 |
| CMPSC 131*# | 3 | STAT 200 or DS 200 (GQ)*‡#† | 4 |
| A-I 100 (GS)*# | 3 | ENGL 15 (GWS)‡ | 3 |
| First-Year Seminar* | 1 | CAS 100A, 100B, or 100C (GWS)‡ | 3 |
| General Education Course | 3 | General Education Course | 3 |
| 14 | 16 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| CMPSC 360 or MATH 311W* | 3 | DS 305, CMPSC 465, or CMPSC 462* | 3 |
| MATH 225* | 4 | STAT 401* | 3 |
| General Education Course | 3 | ENGL 202A, 202B, 202C, or 202D (GWS)‡ | 3 |
| General Education Course | 3 | General Education Course | 3 |
| Elective | 3 | General Education Course | 3 |
| 16 | 15 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| DS 310, CMPSC 448, or CMPSC 445* | 3 | A-I 375* | 3 |
| A-I 341W* | 3 | Support Course Selection (Technical or Application)* | 3 |
| A-I 370* | 3 | Support Course Selection (Technical or Application)* | 3 |
| Support Course Selection (Technical or Application)* | 3 | General Education Course | 3 |
| Elective | 3 | Elective | 3 |
| 15 | 15 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| AIMA 430 or A-I 494* | 3 | AIMA 440 or A-I 494* | 3 |
| Support Course Selection (Technical or Application)* | 3 | Support Course Selection (Technical or Application)* | 3 |
| Support Course Selection (Technical or Application)* | 3 | General Education Course | 3 |
| General Education Course | 3 | General Education Course | 3 |
| Elective | 3 | Elective | 2 |
| 15 | 14 | ||
| Total Credits 120 | |||
- *
Course requires a grade of C or better for the major
- ‡
Course requires a grade of C or better for General Education
- #
Course is an Entrance to Major requirement
- †
Course satisfies General Education and degree requirement
University Requirements and General Education Notes:
US and IL are abbreviations used to designate courses that satisfy Cultural Diversity Requirements (United States and International Cultures).
W, M, X, and Y are the suffixes at the end of a course number used to designate courses that satisfy University Writing Across the Curriculum requirement.
General Education includes Foundations (GWS and GQ), Knowledge Domains (GHW, GN, GA, GH, GS) and Integrative Studies (Inter-domain) requirements. N or Q (Honors) is the suffix at the end of a course number used to help identify an Inter-domain course, but the inter-domain attribute is used to fill audit requirements. Foundations courses (GWS and GQ) require a grade of 'C' or better.
All incoming Schreyer Honors College first-year students at University Park will take ENGL 137H/CAS 137H in the fall semester and ENGL 138T/CAS 138T in the spring semester. These courses carry the GWS designation and satisfy a portion of that General Education requirement. If the student’s program prescribes GWS these courses will replace both ENGL 15/ENGL 30H and CAS 100A/CAS 100B/CAS 100C. Each course is 3 credits.
Technical Supporting Courses:
- Select 9 credits; at least 3 credits must be at the 400-level:
- Artificial Intelligence (A-I):
- Computer Science (CMPSC):
- Cybersecurity (CYBER):
- Data Sciences (DS):
- Electrical Engineering (EE):
- Human-Centered Design and Development (HCDD):
- Mathematics (MATH):
- Statistics (STAT):
Application Supporting Courses:
- Select 9 credits; at least 3 credits must be at the 300- or 400-level; up to 6 credits of ROTC.
- It is required that students choose a focus area and take a sequence of courses from the chosen focus area:
- Agricultural Sciences Focus:
- AI and National Security Focus:
- Business Fundamentals Focus at University Park Campus:
- Business Fundamentals Focus at Commonwealth Campuses:
- ETI Focus:
- Health Policy and Administration Focus:
- Human Development and Family Studies Focus:
- Nutritional Sciences Focus:
- PHIL Focus:
- PSYCH Focus:
- ROTC Focus:
- AIR
- ARMY
- NAVSC
Career Paths
Graduates of AIMA are equipped to work at the intersection of technology, decision-making, and innovation. They are prepared for a broad range of careers in AI research and development, the implementation of AI-powered systems, and the use of AI to enhance human decision-making, discovery, and creativity. These graduates contribute to AI-driven solutions across diverse sectors, including but not limited to health care, business, science, engineering, agriculture, education, software, manufacturing, public policy, and the creative arts.
In addition to core technical roles, AIMA graduates can also pursue opportunities in emerging interdisciplinary areas such as AI policy, ethics, infrastructure, and product design. These careers are found not only in the tech sector, but also in domains like drug discovery, law, public governance, education, and entertainment—where AI is rapidly transforming workflows, tools, and outcomes. Below are just a few examples of the potential career paths AIMA graduates might pursue, depending on their interests, skills, and the domains they choose to impact:
Technical Roles
- AI Research Scientist
- Advance the field of artificial intelligence by developing new methods and algorithms.
- Focus: AI research and foundational innovation
- Applied AI Engineer
- Design and deploy AI models in production systems across domains like speech, vision, or text.
- Focus: AI development and real-world implementation
- Machine Learning Engineer
- Design, build and deploy machine learning models and systems to learn from data and assist with making intelligent decisions.
- Focus: ML algorithms, tools and infrastructure development and deployment
- Automation & Systems Engineer (AI-Powered Automation)
- Design systems that integrate AI to automate workflows and optimize operations.
- Focus: AI-powered automation and infrastructure
- Decision Intelligence Specialist
- Develop AI-enabled tools and frameworks that support and enhance human decision-making in complex environments.
- Focus: Augmenting human judgment with interpretable and interactive AI systems
- NLP Specialist (Natural Language Processing)
- Create AI systems that process and understand human language to improve communication and interaction.
- Focus: Human-AI collaboration and creative AI applications
- Computer Vision Engineer
- Develop AI systems that analyze and interpret visual data for applications like autonomous vehicles, medical imaging, and quality control.
- Focus: Image processing, pattern recognition, and visual AI applications
- Model Operations Engineer (MLOps)
- Automate and manage the lifecycle of AI models in production environments.
- Focus: Model deployment, monitoring, and continuous improvement
Strategic and Applied Roles
- AI Product Manager
- Lead cross-functional teams to integrate AI into user-facing products and services.
- Focus: Strategic planning, feature development, and impact evaluation of AI capabilities
- AI Compliance Specialist
- Ensure AI systems meet regulatory requirements and industry standards for safety, privacy, and performance.
- Focus: Regulatory compliance, risk assessment, and audit procedures
- Responsible AI Specialist
- Implement frameworks to ensure fairness, transparency, and ethical deployment of AI systems within organizations.
- Focus: AI governance, bias mitigation, and responsible development practices
- Healthcare AI Specialist
- Develop AI systems that support medical professionals in diagnosis, treatment planning, and patient care.
- Focus: Medical AI applications, clinical decision support, and healthcare data analysis
- AI Learning Experience Designer (Education)
- Design personalized learning platforms powered by AI to adapt to student needs.
- Focus: Improving education outcomes through intelligent tutoring and adaptive content
- Smart Manufacturing Systems Analyst (Manufacturing)
- Implement AI for predictive maintenance, quality control, and supply chain optimization.
- Focus: AI-driven automation and efficiency in industrial production
- AI Policy Advisor (Public Policy)
- Analyze and advise on the societal impacts and regulations of AI deployment.
- Focus: Shaping responsible AI policy and governance