The M.S. in Information Science with a concentration in Agentic AI prepares students to be at the forefront of integrating intelligent systems. LLMs enable AI systems to flexibly accept voice and natural text as instructions for complex information management and response tasks, and currently available multimodal (e.g., vision & language) models provide capabilities beyond human-level abilities in an increasing variety of complex tasks.

In addition, these rich, autonomous agents can be specialized and coordinate directly with each other via both natural language and other means. The goal of this concentration is to ensure that students are not only aware of these tools and techniques but also have experience creating and augmenting information systems with them, building on the expertise of our research faculty who manage multi-agent systems for problem-solving.

Completion of the concentration provides students with the skills and competencies to gain strategic and tactical competitive advantage by orchestrating multiple, specialized AI-based agents for complex information system designs and problem-solving. 

 

Required Courses, 12 Credit Hours

A grade of B or better must be earned in all core courses.   

  • INFO 5000 - Information and Knowledge Professions
  • INFO 5094 - Capstone
  • INFO 5200 - Information Organization
  • INFO 5600 - Information Access and Knowledge Inquiry
Concentration Courses, 12 Credit Hours

• INFO 5701 - AI Tools for Information Scientists
• INFO 5715 - Vibe Coding: AI Collaboration for Rapid Development
• DTSC 5737 - Human-Centered AI: Design and Implementation
• INFO 5719 - Human-AI Multi-Agent Interaction Design

Guided elective, 9 Credit Hours

Completion of at least three of the following major prescribed courses with the guidance of the academic advisor:   

  • INFO 5206 - Information Retrieval Design
  • INFO 5305 - Systems Analysis and Design
  • INFO 5306 - Project Management for Information Systems
  • INFO 5307 - Knowledge Management Tools and Technologies
  • INFO 5707 - Data Modeling for Information Professionals
  • INFO 5731 - Computational Methods for Information Systems
General electives, 3 Credit Hours

Additional one elective course selected from the list below or transferred from other programs with the approval of faculty advisor.  All courses outside of this list must be approved by the faculty advisor.

  • ADTA 5130 - Data Analytics I
  • ADTA 5160 - Sport and Entertainment Analytics
  • ADTA 5230 - Data Analytics II
  • ADTA 5240 - Harvesting, Storing and Retrieving Data
  • ADTA 5340 - Discovery and Learning with Big Data
  • DSCI 5240 - Data Mining and Machine Learning for Business
  • DSCI 5330 - Business Intelligence Foundations
  • DSCI 5340 - Predictive Analytics and Business Forecasting
  • INFO 5206 - Information Retrieval Design
  • INFO 5224 - Advanced Metadata Applications in Digital Repositories
  • INFO 5305 - Systems Analysis and Design
  • INFO 5306 - Project Management for Information Systems
  • INFO 5500 - Foundational Principles in Knowledge Management
  • INFO 5701 - AI Tools for Information Scientists
  • INFO 5713 - Telecommunications and Information Professionals
  • INFO 5717 - Networked Data Modeling and Processing
  • INFO 5730 - Microcomputer Applications for Information Management
  • INFO 5735 - Usability and User Experience Metrics
  • INFO 5737 - Information and Cyber-Security
  • INFO 5745 - Information Architecture
  • INFO 5810 - Data Analysis and Knowledge Discovery
  • INFO 5815 - Topics in Digital Imaging for Information Professionals
  • LING 5405 - Programming for Linguistics
  • LTEC 5300 - Learning and Cognition
  • LTEC 5702 - Evaluation of Generative AI Tools in Education
  • ADTA 5250 - Large Data Visualization

or

  • DSCI 5360 - Data Visualization for Analytics

or

  • INFO 5709 - Data Visualization and Communication

Further information concerning these requirements may be obtained by contacting the College of Information (COI) Advising Team by email at ci-advising@unt.edu  or by phone at 940-565-2445.