Ah, the PMAISE model—sounds like a fancy acronym for a sci-fi gadget, but it's actually a smart blueprint for making AI play nice in college classrooms. This systematic review dives into 73 studies and boils it down: AI chatbots, adaptive learning systems, and predictive analytics aren't magic wands that zap students into engagement on their own. Nope, they need a human touch—think flipped classrooms or project-based learning—to really shine. It's like giving AI a script but letting teachers direct the show; without that mediation, you're just yelling lines into the void.
I love how the authors keep it real by flagging the sticky bits: ethics, privacy pitfalls, and the digital divide that could leave some students in the dust. It's a pragmatic nudge—innovate, sure, but don't pretend we're all starting from the same finish line. For educators staring down a syllabus, this means experimenting with AI not as a replacement, but as a booster rocket for tried-and-true methods. Imagine a chatbot handling routine queries so profs can dive into the juicy debates—efficient, engaging, and way less like herding cats.
Critically, though, let's not overhype. AI's no silver bullet; it amplifies good teaching but can flop spectacularly if the pedagogy's off. This review encourages us to think like tinkerers: test, tweak, and measure those affective (feeling hooked?), behavioral (actually participating?), and cognitive (minds blown?) engagement vibes. If you're in higher ed, grab this framework and iterate—because in the end, the best innovations aren't flashy; they're the ones that stick. Source: Frontiers | Artificial Intelligence in Higher Education (AIHE): A systematic review of their impact on student engagement and the mediating role of teaching methods
