ECE 5494 Innovation Pathways in Artificial Intelligence and Machine Learning | ECE | Virginia Tech


Course Information


Convergence of digital technologies in networked devices, big data, and advanced breakthroughs in artificial intelligence (AI) and machine learning (ML). Meaning, theory, and construction of socio-technical systems. Analysis of technical aspects and opportunities of AI/ML systems in organizations. Technosystem due diligence of advanced AI/ML systems. Assessment of the viability of emerging technological solutions. Social impacts of disruptive change upon individuals, organizations, and society-at- large. Frameworks for the design and implementation of advanced AI/ML systems. Planning for the future of AI/ML. Master of Information Technology (MIT) students only.

Why take this course?

As technology becomes more fundamental in society, the need for professionals who can critically navigate increasingly complex technological systems becomes essential. These leaders need to be able to assess the transformative potential of automation, machine learning, and cognitive technologies (i.e., Artificial Intelligence) to create new opportunities while also anticipating the negative repercussions caused by the disruption. This will require students to develop a comprehensive view of socio-technical systems, understanding how to balance the technical benefits of emerging systems as well as the social, ethical, and political implications of deploying these technologies to solve important problems in society. The MIT program at Virginia Tech offers a number of courses focusing on the design, development, operation, and strategic management of technical systems, but no course develops the integrated understanding essential for the recognition and evaluation of the balance between the transformative and disruptive potential of emerging technologies geared toward students aspiring to work as technical leaders, managers, and founders.

Learning Objectives

  • 1. Categorize types of artificial intelligence (AI) and machine learning (ML) technologies and technological systems.
  • 2. Identify components in the construction of technological systems.
  • 3. Apply theories of technological change to AI and ML systems.
  • 4. Analyze AI and ML technologies.
  • 5. Practice technosystem due diligence.
  • 6. Assess the social impacts of disruptive change and the viability of emerging technological solutions.
  • 7. Develop frameworks and perspectives for critically evaluating the potential impact of AI and ML technologies.
  • 8. Plan for the future of artificial intelligence and machine learning technologies.

Course Topics


Percentage of Course

Technosystem Foundations 10%
Construction of AI and ML Systems 10%
ML Technosystem Analysis 20%
AI Technosystem Analysis 20%
Impact of AI and ML 10%
Technosystem Due Diligence 10%
Designing and Implementing AI and ML Systems 10%
Future of Technology 10%