eclectic, pragmatic, enthusiastic
“Generative AI models don’t understand, they just predict the next token.” You’ve probably heard a dozen variations of this theme. I certainly have. But I recently heard a talk by Shuchao Bi that changed the way I think about the relationship between prediction and understanding. The entire talk is terrific, but the section that inspired … Read more
All of the analysis, design, and development that happen in conjunction with retrieval augmented generation (RAG) and fine-tuning of large language models is instructional design. The instructional design process often begins with an analysis of the learner(s). What do they already know? What skills do they already have? What prior knowledge can we assume will … Read more
Large language models (LLMs) make it possible for faculty to rapidly create a wide range of formative and summative assessments for their students. And, as we hear about so often, students can also use LLMs to write their essays and complete other assignments. (Apparently, when faculty use AI to create assignments, it’s a “productivity gain.” … Read more