Applied AI Bachelor of Science
Leading to a Bachelor of Science Degree in Bachelor of Science in Applied AI
The Bachelor of Science in Applied Artificial Intelligence (BSAI) at Wentworth Institute of Technology is an interdisciplinary, career-focused program designed to prepare graduates to apply AI ethically and effectively across diverse industries. BSAI integrates a technical core with application domain-specific pathways in Business Management, Computing, Construction Management, and Life Sciences, with future expansion into Design, Technology, and other growth fields.
Program Educational Objectives
- Apply principles of artificial intelligence to develop and deploy AI-driven solutions in real-world settings across industry, government, or nonprofit sectors.
- Collaborate effectively with interdisciplinary teams, integrating AI techniques with domain-specific knowledge in fields such as business, computing, or the life sciences.
- Demonstrate awareness of the ethical, legal, and societal implications of AI systems, and contribute to the development of responsible, transparent, and fair AI solutions.
- Engage in lifelong learning, professional development, or graduate education in AI or related areas.
- Communicate complex AI concepts, findings, and decisions effectively to both technical and non-technical audiences.
Student Outcomes
- Analyze a complex problem, and apply principles of computing, data science, and AI to identify and formulate effective solutions.
- Design, implement, and evaluate an AI-based solution to meet a given set of requirements, informed by domain knowledge in an area of application.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in AI practice based on legal and/or ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
Four-Year Program
Total Required Credits: 120
This is a four-year full-time program, which begins in the fall semester of the student's first year and is planned to end after the summer semester of the student's fourth year.
| First Year | ||
|---|---|---|
| Fall Semester | Credits | |
| COMP1000 | COMPUTER SCIENCE I | 4 |
| AAAI1000 Introduction to Applied AI | 4 | |
| MATH1825 | CALCULUS FOR COMPUTING AND AI | 4 |
| English Sequence | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| MATH2825 | LINEAR ALGEBRA FOR COMPUTING AND AI | 4 |
| MATH2300 | DISCRETE MATHEMATICS | 4 |
| Domain Elective 1 | 4 | |
| English Sequence | 4 | |
| Credits | 16 | |
| Second Year | ||
| Fall Semester | ||
| COMP3000 | APPLICATIONS OF AI | 4 |
| PHIL4525 | A.I. ETHICS | 4 |
| HSS Elective* | 4 | |
| Free Elective | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| COMP3125 | DATA SCIENCE FUNDAMENTALS | 4 |
| Free Elective | 4 | |
| Domain Elective 1 | 4 | |
| MATH1030 OR MATH2100 Depending on domain requirements | 4 | |
| COOP2500 | INTRODUCTION TO COOPERATIVE EDUCATION | 0 |
| Credits | 16 | |
| Summer Semester | ||
| OPTIONAL COOP EDUCATION | ||
| Credits | 0 | |
| Third Year | ||
| Fall Semester | ||
| COMP3001 | 4 | |
| CSAS4000 | RESPONSIBLE APPLICATIONS OF GENERATIVE ARTIFICIAL INTELLIGENCE | 4 |
| Domain Elective 1 | 4 | |
| Free Elective | 4 | |
| Credits | 16 | |
| Spring Semester | ||
| COOP3500 | COOP EDUCATION 1 | 0 |
| Credits | 0 | |
| Summer Semester | ||
| DATA3333 | PRACTICAL DEEP LEARNING | 4 |
| Domain Elective 1 | 4 | |
| HSS Elective* | 4 | |
| Science Elective | 4 | |
| Credits | 16 | |
| Fourth Year | ||
| Fall Semester | ||
| COOP4500 | COOP EDUCATION 2 (THIS MAY BE SWITCHED WITH ANOTHER SEMESTER) | 0 |
| Credits | 0 | |
| Spring Semester | ||
| COMP4726 | APPLIED LARGE LANGUAGE MODELS | 4 |
| HSS Elective* | 4 | |
| Domain Elective 1 | 4 | |
| Credits | 12 | |
| Summer Semester | ||
| AAAI5500 Senior Design | 4 | |
| Domain Elective 1 | 4 | |
| Domain Elective 1 | 4 | |
| Credits | 12 | |
| Total Credits | 120 | |
ENGL/HSS Note
Students are required to complete:
- At least one course in Humanities: CSAS, HSSI, HIST, HUMN, LITR and PHIL
- At least one course in the Social Sciences: CSAS, HSSI, COMM, ECON, ENVM, POLS, PSYC and SOCL
- The remaining course from either the Humanities or Social Sciences category.
Students with a three English course sequence may use the third English course to satisfy a Humanities requirement.
A minimum of 20 credits total, including English, humanities, and social science credit, is required to complete the humanities and social sciences graduation requirement.
At least one of the HSS electives must be an Ethics course.
Math Placement may alter the course schedule above.
Science Electives
| Course | Title | Credits |
|---|---|---|
| BIOL1100 | CELL & MOLECULAR BIOLOGY | 4 |
| BIOL1700 | ANATOMY & PHYSIOLOGY I | 4 |
| BIOL2200 | ADVANCED MOLECULAR BIOLOGY | 4 |
| BIOL3000 | APPLICATIONS IN GENETICS | 4 |
| CHEM1100 | GENERAL CHEMISTRY I | 4 |
| CHEM1600 | GENERAL CHEMISTRY II | 4 |
| PHYS1250 | ENGINEERING PHYSICS I | 4 |
| PHYS1750 | ENGINEERING PHYSICS II | 4 |
| PHYS2000 | INTRODUCTION TO ASTRONOMY | 4 |
| PHYS3100 | MODERN PHYSICS | 4 |
| The following courses require School approval to satisfy the Science Elective requirement | ||
| BIOL2990 | INDEPENDENT STUDY IN BIOLOGY | 4 |
| BIOL3800 | SPECIAL TOPICS IN BIOLOGY | 4 |
| CHEM2990 | INDEPENDENT STUDY IN CHEMISTRY | 4 |
| CHEM3800 | SPECIAL TOPICS IN CHEMISTRY | 4 |
| PHYS2990 | INDEPENDENT STUDY IN PHYSICS | 4 |
| PHYS3800 | SPECIAL TOPICS IN PHYSICS | 4 |
Domain Electives
Students will choose one of several application domains to contextualize their AI education. These domains include both foundational and advanced coursework from a partner discipline, allowing students to apply AI techniques to domain-specific challenges.
A total of 28 semester credit hours of domain electives must be taken as part of the program. Eight of those credits must be STEM courses. Students declare their chosen domain by the end of their first term.
It currently offers the following domains:
- Business Management:
- Computing
- Construction Management
- Life Sciences
Their curriculum structure and course list are shown below.
Business Management Domain
Students complete a minimum of 28 credits:
-
MGMT1000–Introduction to Management (required).
-
At least 8 credits of STEM-designated courses from the following list, such as Decision Analysis, Systems Analysis, or Cybersecurity.
| Course | Title | Credits |
|---|---|---|
| MGMT1025 | COMPUTER BUSINESS APPLICATIONS | 4 |
| MGMT1500 | DECISION ANALYSIS FOR BUSINESS | 4 |
| MGMT2525 | SYSTEMS ANALYSIS, DESIGN, AND IMPLEMENTATION | 4 |
| MGMT2560 | CYBERSECURITY LAW AND POLICY | 3 |
| MGMT2650 | CYBERSECURITY PLANNING | 3 |
| MGMT2800 | CYBERSECURITY MANAGEMENT | 4 |
| MGMT2000 | MANAGEMENT INFORMATION SYSTEMS | 4 |
| MGMT2175 | INTRODUCTION TO SUPPLY CHAINS | 4 |
| MGMT3250 | MANAGERIAL ACCOUNTING | 4 |
| MGMT2550 | APPLIED PROJECT MANAGEMENT | 4 |
-
Remaining credits can come from either the list above or the list below.
| Course | Title | Credits |
|---|---|---|
| MGMT2750 | INTEGRATIVE FINANCIAL ACCOUNTING | 4 |
| MGMT2100 | MANAGEMENT COMMUNICATIONS | 4 |
| MGMT2850 | PRINCIPLES OF MARKETING | 4 |
| MGMT3650 | BUSINESS LAW | 4 |
| MGMT3700 | HUMAN RESOURCES & LABOR MANAGEMENT | 3 |
| MGMT3900 | OPERATIONS MANAGEMENT | 4 |
| MGMT4400 | BUSINESS NEGOTIATION PRINCIPLES | 3 |
| MGMT3225 | GLOBAL BUSINESS | 4 |
| MGMT3070 | TECHNOLOGY ACQUISITION PROJECTS | 4 |
| MGMT3160 | PROJECT RISK | 4 |
| MGMT3550 | ENTREPRENEURIAL FINANCE | 4 |
| MGMT3575 | ENTREPRENEURIAL MINDSET | 4 |
| MGMT4225 | ENTREPRENEURIAL LEADERSHIP | 4 |
| MGMT3080 | MARKETING FOR ENTREPRENEURS | 4 |
| MGMT2150 | CORPORATE FINANCE | 4 |
| MGMT2350 | FINANCIAL INSTITUTIONS AND MARKETS | 4 |
| MGMT4200 | INVESTMENTS | 4 |
| MGMT3150 | INTRODUCTION TO ENTERPRISE RESOURCE PLANNING | 4 |
| MGMT3165 | INTRODUCTION TO LEAN SIX SIGMA | 4 |
| MGMT4275 | SUPPLY CHAIN LOGISTICS AND PLANNING | 4 |
Elective choices enable students to focus on areas such as data-informed decision-making, enterprise systems, finance, operations, supply chain management, or marketing. Technical fluency is developed through the Applied AI core, enabling students to apply AI tools within business contexts.
Computing Domain
The Computing domain provides students with strong foundations in computer science and modern AI techniques. The curriculum emphasizes data structures, algorithms, probability theory, and both classical and contemporary machine learning methods.
While students may fulfill their statistics requirement through either MATH 1030 or MATH 2100 in the shared core, those pursuing the Computing domain are expected to take MATH 2100 to ensure adequate preparation in probability and statistics. Students complete 28 credits of computing-related coursework, including:
| Course | Title | Credits |
|---|---|---|
| COMP1050 | COMPUTER SCIENCE II | 4 |
| COMP2000 | DATA STRUCTURES | 4 |
| COMP2350 | ALGORITHMS | 4 |
| COMP4700 | FOUNDATIONS OF CLASSICAL AI | 4 |
| or COMP5700 | ||
| MATH4050 | MACHINE LEARNING | 4 |
| COMP/DATA/MATH Elective | 4 | |
| COMP/DATA/MATH Elective | 4 | |
This sequence integrates theoretical foundations with practical experience in designing, implementing, and evaluating AI systems. Students gain the computational thinking and analytical skills necessary to build reliable and scalable AI solutions.
Construction Management Domain
Students in the Construction Management domain complete the following courses alongside the Applied AI core. These courses provide a foundation in building construction, BIM (Building Information Modeling), plan reading, estimating, project management, and project scheduling. The curriculum emphasizes the full construction lifecycle, from design to delivery, while integrating emerging AI-driven tools such as computer vision for site monitoring, large language models for document analysis, and IoT sensors for safety and efficiency.
A minimum of 28 credits must be completed in this domain, including two Construction Management electives. Electives may be chosen from any CONM course offerings.
| Course | Title | Credits |
|---|---|---|
| CONM1200 | BUILDING CONSTRUCTION | 4 |
| CONM1525 | INTRODUCTION TO BUILDING INFORMATION MODELING (BIM) | 2 |
| CONM1550 | INTRODUCTION TO PLAN READING & SPECIFICATIONS | 2 |
| CONM2200 | ESTIMATING | 4 |
| CONM3100 | CONSTRUCTION PROJECT MANAGEMENT | 4 |
| CONM3201 | CONSTRUCTION PROJECT SCHEDULING (CONM Elective) | 4 |
| CONM4825 | ARTIFICIAL INTELLIGENCE IN CONSTRUCTION | 4 |
| CONM Elective | 4 | |
Life Sciences Domain
The Life Sciences domain prepares students to apply artificial intelligence and data science methods to problems in biology, biotechnology, and medicine. Students gain foundational knowledge in cellular and molecular biology, chemistry, and genetics, while developing computational skills for analyzing biological data and simulating complex systems.
Students complete the 32 credits of STEM-designated courses listed below and need not take another Science course, and can count that requirement as fulfilled.
| Course | Title | Credits |
|---|---|---|
| BIOL1100 | CELL & MOLECULAR BIOLOGY | 4 |
| BIOL2200 | ADVANCED MOLECULAR BIOLOGY | 4 |
| CHEM1100 | GENERAL CHEMISTRY I | 4 |
| CHEM1600 | GENERAL CHEMISTRY II | 4 |
| CHEM2000 | BASICS OF ORGANIC & BIOCHEMISTRY | 4 |
| BIOE2100 | BIOSTATISTICS FOR BIOENGINEERS | 4 |
| BIOE3500 | GENETICS AND TRANSGENICS | 4 |
| BIOE4400 | SYNTHETIC BIOLOGY | 4 |