Graduates of the BSAAI program will be able to:

1. Apply AI methods to practical problems by selecting appropriate techniques (including machine learning, deep learning, and generative AI) based on requirements, constraints, and risk considerations.
2. Prepare and manage data for AI applications by performing data collection, cleaning, preprocessing, feature preparation, and documentation aligned with quality and governance expectations.
3. Develop and evaluate AI solutions by training or configuring models, validating performance with appropriate metrics, and interpreting results for technical and non-technical stakeholders.
4. Integrate AI into software and business processes by deploying AI components through APIs or tools, implementing human-in-the-loop workflows where appropriate, and supporting operational use.
5. Monitor and maintain AI-enabled solutions by identifying model drift, documenting changes, and applying basic practices for reliability, security, and ongoing improvement.
6. Demonstrate responsible and compliant AI practice by identifying bias and privacy risks, applying transparency and accountability principles, and adhering to relevant ethical and regulatory expectations.
7. Deliver an applied portfolio-level project that demonstrates end-to-end implementation of an AI solution, including problem definition, data workflow, solution design, evaluation, and communication of outcomes.

General Education (14 Courses)

• Course
  Credit Hrs

• ENGL 101 - Oral Communications

  4.5

• ENGL 102 - English Composition

  4.5

• ENGL 103 - Advanced Writing

  4.5

• DIGI 201 - Information Literacy in Contemporary Society

  4.5

• FINS 201 - Financial Literacy in Contemporary Society

  4.5

• MATH 101 - College Algebra

  4.5

• MATH 102 - Calculus

  4.5

• QANT 301 - Statistics

  4.5

• SOSC 101 - Sociology

  4.5

• SOSC 102 - Psychology

  4.5

• SOSC 103 - Political Science

  4.5

• SOSC 201 - Law and Ethics

  4.5

• SOSC 202 - American Cultural Studies

  4.5

• SOSC 203 - World History – Ancient to 1750

  4.5

• SOSC 204 - World History – 1750 to Present

  4.5

• SCIN 201 - Future Studies

  4.5

Common Core (7 Courses)

• BSAI 201 - Principles of Artificial Intelligence

  4.5

• INST 201 - Introduction to Information Systems

  4.5

• MGMT 110 - Business Communications

  4.5

• MGMT 201 - Principles of Management

  4.5

• MGMT 203 - Principles of Project Management

  4.5

• RESH 401 - Research Methods

  4.5

• TECH 301 - Technology Management

  4.5

Program Core (10 Courses)

• BSAI 204 - Essential Applications for AI

  Examine machine learning, deep learning, natural language processing, computer vision, and generative AI, focusing on how applications are selected to solve organizational problems. Prerequisite: None.

  4.5

• BSAI 206 - Programming for AI Applications (Python)

  Build AI-enabled applications in Python, including working with AI libraries, REST APIs, structured and unstructured data, and automation frameworks. Prerequisite: None.

  4.5

• BSAI 208 - Data Preparation and Feature Engineering for AI

  Collect, clean, transform, validate, and document real datasets for AI systems, with attention to data governance and ethical handling of sensitive information. Prerequisite: BSAI 206.

  4.5

• BSAI 302 - Machine Learning for Applied Solutions

  Implement supervised and unsupervised models for classification, regression, clustering, and anomaly detection, and present findings to technical and non-technical stakeholders. Prerequisite: BSAI 206.

  4.5

• BSAI 304 - Applied Deep Learning and Generative AI

  Build image, text, and predictive models with deep learning frameworks, and apply transformer models and prompt-driven generative AI tools. Prerequisite: BSAI 302.

  4.5

• BSAI 402 - AI Systems Integration and Deployment

  Integrate AI models into software systems through APIs, containerization, and cloud deployment, with practical MLOps fundamentals for monitoring and version updates. Prerequisite: BSAI 302.

  4.5

• BSAI 404 - AI for Business and Organizational Applications

  Design AI-enabled solutions aligned with organizational objectives, including ROI analysis, adoption strategy, change management, and vendor evaluation. Prerequisite: BSAI 204.

  4.5

• BSAI 405 - Responsible and Ethical AI Implementation

  Address bias, fairness, privacy compliance, risk management, and accountability frameworks for deploying AI systems in the real world. Prerequisite: BSAI 204.

  4.5

• BSAI 406 - AI Product Development and Innovation

  Manage the lifecycle of AI-enabled products, from problem definition and prototyping through validation, roadmaps, and executive communication. Prerequisite: BSAI 304.

  4.5

• BSAI 408 - AI Automation and Intelligent Workflows

  Design AI-driven automation using prompt engineering, document processing, chatbots, and decision-support systems to improve operational performance. Prerequisite: BSAI 304.

  4.5

Elective (8 Courses)

Capstone (1 Course)

• CAPS 490 - Undergraduate Capstone

  4.5