Early-stage ideas and notes
Mijn eigen diploma is in de Onderwijstechnologie. Januszewski en Molenda (2013) definieerden dat vakgebied als "de studie en ethische praktijk van het faciliteren van leren en het verbeteren van prestaties door het creëren, inzetten en beheren van gepaste technologische processen en middelen."
Three years in. That's where we are now with generative AI in higher education. ChatGPT's arrival in late 2022 feels like both yesterday and a lifetime ago. The initial panic ("How do we AI-proof assessment?") has given way to something more interesting, more nuanced, and dare I say it, more hopeful.
For the past few years, I've been experimenting with various large language models (LLMs) and tools to better understand the sense and none-sense of generative AI (GenAI). In my quest for meaningful and efficient uses, I've created various tools for personal and professional use (think: article collector and personal home voice assistant). My sixth iteration of an LLM-based qualitative analysis tool is a prime example of where these models can be quite handy. Here’s what I've learned through trial, error and extensive reading.
Looking for your next read? These eight books won't all sit comfortably together on a shelf—which is exactly the point. They range from scathing critiques of neoliberal universities to surprisingly practical guides on networked learning, instructional design, and (plot twist) no-dig gardening. Each one challenged how I think about teaching, learning, and what actually makes institutions flourish.