Two questions run beneath this week’s AI signals: whether institutions can keep assessment standards consistent, and whether their faculty, curriculum, and computing infrastructure are ready for the work AI now demands.
Tag: AI in higher education
As AI makes finished products weaker proof of learning, institutions are redesigning assessment around evidence of thinking, while AI fluency spreads well beyond computer science.
Fresh off finishing my doctorate and busier than ever, I said yes to my first faculty PD summit presentation anyway. Here’s what that decision, and C-Man and Sir Claude a-lot, taught me about the judgment AI still can’t do for us.
As faculty prepare for a new academic year, higher education is retiring AI detectors and facing a harder question: how to prove students actually learned. This edition covers the shift from detection to evidence of learning, OpenAI’s new role-specific education plugins, and the widening gap between AI adoption and governance.
Two tensions defined the week in higher education and AI: a sharpening question about what a degree is worth, and a widening gap between AI access and the governance, faculty preparation, and assessment design needed to use it well. This edition covers the Ithaka S+R value brief, a 156,135-student study on AI and grades, and new AI degree programs from Chicago to South Dakota.
The era of AI experimentation in higher education is over. The conversation now is about ROI. This week’s brief looks at why institutions are shifting to enterprise-wide metrics, how AI pedagogical agents are increasing STEM retention by 14 percent, and what the new EDUCAUSE framework means for campus strategy.
AI didn’t create the assignment problem in higher education—it revealed it. A practical framework for faculty to redesign coursework around judgment, accountability, and visible thinking in an AI-shaped academic environment.