Artificial Intelligence and Evolution of the Virtual University

In this chapter we invite the reader to consider the importance of context in the history and evolution of both artificial intelligence and the virtual university. There are histories in the terminology as much as what they refer to. Thus, the virtual universities that emerged late last century are very different in scope and vision compared to contemporary practice. There are likewise different theoretical perspectives with Gartner’s “Hype Cycle,” one among many methods that provides perspective on the stages of AI-driven advances in technological innovation. An imperative to foreground questioning of the domain is what frames our discussion. One key question among many concerns the affordances and challenges that AI presents for a virtual university in the coming decades. A university must prepare for AI-based auditing of the integrity of its student records. AI can help spot plagiarism and fake IDs and support academic integrity. Policy-makers might use AI to spot potentially impossible situations that could give rise to big penalties. But as AI merges into our environment, it is no longer just a tool with new ethical and social issues arising. There already exist forms of colonial biases embedded into AI and machine learning systems. What practical choices exist now? Every virtual university might consider collaborating on how to ensure trustworthiness of their operational systems that use AI. Emerging public policy such as the EU’s Artificial Intelligence Act provides a legal mechanism for building a reliable AI ecosystem. Research and innovation into both AI and the kinds of services virtual universities will need into the future are now proliferating. Many futures are possible. From an educational perspective developing human agency through this next era should be a priority. Students could be taught how to use AI-supported tools to solve increasingly challenging questions. Even more imperative will be the questions they ask.