Joseph Lento

Artificial intelligence is shaping the future, but many AI policies still look backward instead of forward. These policies are often based on assumptions about a simple, controlled world. They imagine that systems are easy to manage, data is always correct, and outcomes are predictable. That kind of world has never existed. Even before AI, human systems were complex and often messy. Yet, policymakers continue to build rules as if everything can be neatly organized. This creates a mismatch between policy and reality. As AI grows more powerful, this gap becomes clearer. Rules fail to guide real use cases, and developers struggle to follow unclear standards. When policies are based on a false past, they cannot support a real future. To fix this issue, we must understand how these outdated ideas influence today’s AI regulations and why they need to change. The Belief That Technology Can Be Fully Controlled Many AI policies assume that technology can be fully controlled. This belief comes ...

Artificial Intelligence (AI) has fundamentally transformed traditional college-level teaching and learning practices. For decades, higher education relied heavily on lecture-based instruction, standardized assessments, and one-size-fits-all curricula. However, the integration of AI tools in education has disrupted this model, introducing more personalized, flexible, and data-driven approaches to learning. AI-powered platforms now allow instructors to move beyond passive teaching methods. Instead of long lectures where students simply absorb information, educators can use AI-driven tools such as intelligent tutoring systems, adaptive learning platforms, and automated feedback software. These tools analyze student performance in real time and adjust content accordingly, ensuring that each learner progresses at their own pace. This shift has also changed the role of professors. Rather than being the sole source of knowledge, instructors now act as facilitators and mentors. AI handles rep...