In my experimental AI courses, students don't just thrive: they become unstoppable.

By starting with students and not the technology, paths unfold differently for AI and education. By thinking about what we owe them, though, the path becomes clear: transformation, not growth.

Since arriving at the University of Florida, I have been pioneering courses, leading international workshops, and hosting conferences on professional writing and AI. Convinced that how we consider AI has downstream implications for its use in the classroom, I launched the Journal of Writing and Artificial Intelligence to explore the future of writing. My philosophy of AI and education does not disentangle theoretical and practical fronts: conceptualization attaches to appropriate use.

The discourse surrounding AI in higher education suffers from two base complications. First, it is largely analogous (e.g., AI is the new calculator; or, it's like a microwave: good at certain things but bad at others), which, though helpful, gives rise to nebulous conceptualization and moves us away from appreciating structure and process. Second, and relatedly, the very concepts used to describe AI tend toward encampment (e.g., tools can be good or bad; or, uses can be right or wrong). While both issues lead and contribute to misunderstanding, the secondary effects therefrom can have a devastating effect on students (e.g., students become subject to policies that limit potential or party to procedures that likewise imperil futures). This need not be the case. Instead, we can conceptualize AI both in ways that attach to standards (e.g., inspiration, revision, transformation) and in ways that bypass dichotomies (e.g., situational procedures). Toward these ends, I hope to situate the conversation about AI in higher education semantically apart from its popular rhetoric by creating a new socio-cognitive grounding: AI as a model or mode for taking appropriate measures. Thus, conceptualization of AI need not be something but somehow.

In both academe and industry, AI-driven solutions are everywhere. Our obligation to prepare our students for the future must include AI. Students pursuing courses and careers in AI should develop not only capacities for programming and prompting LLMs but also skills in AI-based proposals, presentations, and visuals. Just as with computers, there will be more practitioners of AI than people innovating within AI programming. Thus, as a function of workforce readiness, we must prepare the next generation to communicate in spaces where AI-driven solutions are generated and conveyed. To these ends, I teach various writing for AI courses at UF—the AI University.