CIS1532 AI-NATIVE NETWORKING (AIOPS)
3 Credit Hours
Student Level:
This course is open to students on the college level in either Freshman or Sophomore year.
Catalog Description:
CIS1532 - AI-Native Networking (AIOps) (3 hrs.)
This course introduces students to the application of artificial intelligence (AI) within modern network operations environments, often referred to as AIOps (Artificial Intelligence for IT Operations). Emphasizing applied networking and operational practices, students will explore how AI supports network monitoring, performance analysis, anomaly detection, and troubleshooting. Through hands on activities and real world scenarios, learners will examine AI assisted approaches to managing complex and dynamic network environments. The course prepares students with practical skills relevant to contemporary networking operations while addressing limitations and ethical considerations related to the use of AI technologies.
KRSN: n/a
Course Classification:
Lecture
Prerequisites:
None
Controlling Purpose:
This course prepares students to understand and apply artificial intelligence within network operations environments. Focusing on monitoring, performance optimization, and operational troubleshooting, students learn how AI technologies assist in analyzing network behavior, identifying anomalies, and supporting operational decision making. The course emphasizes applied networking skills while promoting responsible, ethical, and informed use of AI in modern network operations.
Learner Outcomes:
Upon completion of the course, the student will have the ability to explain the role of artificial intelligence in modern network operations and describe how AI techniques support network monitoring, performance analysis, and troubleshooting. The student will analyze network data and operational metrics using AI‑assisted methods to identify anomalies and potential network issues. Through applied scenarios, the student will evaluate the limitations, risks, and ethical considerations associated with AI‑assisted networking operations and demonstrate effective problem‑solving skills within AI‑enhanced network operations environments.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content, not including the final examination period. Instructors may add other material as time allows.
UNIT 1: INTRODUCTION TO NETWORK OPERATIONS AND AI
Outcomes: Upon completion of this unit, students will be able to develop a foundational understanding of network operations environments and the role of AI in supporting operational networking tasks.
- Describe common network operations functions and workflows
- Explain basic AI concepts as they relate to networking operations
- Identify operational networking challenges addressed through AI assisted analysis
- Discuss ethical and operational considerations in AI supported networking
UNIT 2: NETWORK MONITORING AND PERFORMANCE ANALYSIS
Outcomes: Upon completion of this unit, students will explore how artificial intelligence supports log analysis and anomaly detection.
- Explain the purpose of network monitoring and performance metrics
- Identify common sources of network telemetry and monitoring data
- Analyze network performance data using AI assisted techniques
- Interpret AI generated insights to support network performance decisions
UNIT 3: AI ASSISTED LOG ANALYSIS AND ANOMALY DETECTION
Outcomes: Students will explore how artificial intelligence supports log analysis and anomaly detection. Upon completion of this unit, students will be able to:
- Explain AI concepts related to pattern recognition and anomaly detection
- Identify how AI tools assist in analyzing large and complex log datasets
- Analyze log data using AI supported techniques to detect unusual or abnormal behavior
- Evaluate the effectiveness of AI assisted analysis compared to manual methods
UNIT 4: TROUBLESHOOTING AND OPTIMIZATION IN AI ENHANCED NETWORKS
Outcomes: Students will apply AI assisted approaches to network troubleshooting and optimization. Upon completion of this unit, students will be able to:
- Use AI supported analysis to assist in network troubleshooting
- Identify potential root causes of network performance issues
- Apply AI generated insights to optimize network operations
- Document operational decisions and troubleshooting outcomes
UNIT 5: LIMITATIONS AND ETHICAL USE OF AI IN NETWORKING OPERATIONS
Outcomes: Students will evaluate limitations and ethical considerations related to AI assisted networking. Upon completion of this unit, students will be able to:
- Identify risks such as false positives, data quality issues, and over reliance on automation
- Discuss ethical considerations in AI assisted network monitoring and decision making
- Evaluate limitations of AI native networking solutions
- Apply responsible and informed practices when using AI in network operations
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission, and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching, and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies on the Cowley Policies and Procedures webpage:
402.00 - Academic Code of Conduct
263.00 - Student Appeal of Course Grades
403.00 - Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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