There was some interest in my post yesterday about what I called “generative textbooks,” but based on people’s comments I don’t know that I explained the idea very clearly. The idea of a “generative textbook” is that, instead of containing instructional content, it provides the learner with a series of prompts they can use to elicit information from a large language model like ChatGPT. Here’s a brief example to clarify what I mean. ...
I’ve limited myself to one hour of writing for this post, so it’s more a collection of thoughts than a coherent narrative. I expect I’ll have lots more to say on this topic in the future. For now, I just want to get the beginnings of this idea out into the world, together with some initial implications. Since ChatGPT’s meteoric rise to popularity, I’ve constantly been amazed by the creative ways people have imagined to make use of generative AI. For months now it feels like each week I read about some new way generative AI is being used that just completely blows my mind. For example, my son who is studying cybersecurity recently told me prompted ChatGPT along these lines: ‘You are the hiring manager for a cybersecurity position at a large company. Conduct a mock interview with me, asking me ten conceptual questions. After I have answered all the questions, give me feedback on my answers. Then ask me ten technical questions. After I have answered all the questions, give me feedback on my answers again.’ My son tells me this exercise was incredibly helpful and he plans to do it several more times before having a “real” interview. ...
Our work at Lumen is focused on eliminating race and income as predictors of student success in the US postsecondary setting. One thing we’ve learned as we’ve worked to erase this persistent gap in academic performance is that it is far easier to “slide the gap to the right” than it is to close it. In other words, interventions intended to benefit the lowest performing students often benefit all students, so that everyone’s academic performance improves. That’s great from one perspective - everyone learned more! But rather than decreasing the size of the gap, these interventions leave the gap in tact and nudge it up the scale to the right. Interventions that have an accurately targeted effect can be hard to find. ...
If you can remember the web of 30 years ago(!), you can remember a time when all it took to make a website was a little knowledge of HTML and a tilde account on the university VAXcluster (e.g., /~wiley6/). While it’s still possible to make a simple website today with just HTML, making modern websites requires a dizzying array of technical skills, including HTML, CSS, JavaScript frameworks, databases and SQL, cloud devops, and others. While these websites require far more technical expertise to build, they are also far more feature-rich and functional then their ancestors of 30 years ago. (Imagine trying to code each of the millions of pages on Wikipedia.org or Amazon.com completely by hand with notepad!) ...
At some point over the last decade, open educational resources (OER) advocacy in US higher education became zero textbook costs (ZTC) advocacy. The two are intertwined now in a manner that would be difficult to disentangle even if you wanted to try. There are plenty of practical reasons why this might have happened. For example, politicians understand costs much better than they understand learning, which makes policy work and other political advocacy around eliminating textbook costs far easier than advocating for ways that “open” (whatever that word means) might be leveraged to improve student outcomes. But OER / ZTC advocates have had a fundamental problem simmering for many years now, and the recent advent of large language models (LLMs) like GPT-4 will quickly bring that simmer to a boil. ...
The journal Nature, and all other Springer Nature titles, have updated their Guide to Authors with rules for using LLMs like ChatGPT when writing research articles for the publication. To summarize, the rules say: 1. DO NOT list the LLM as an author, and 2. DO describe how you used the LLM in a Methods, Acknowledgments, or other appropriate section. With a journal as prestigious as Nature having established formal guidelines, I expect other journals will adopt similar rules relatively quickly. ...
2022 saw some significant advancements in artificial intelligence. My threshold for “significant” here being that the advances moved out of labs and arXiv.org preprints and into tools that many people were using and talking about. Lots of people thought text-to-image tools like DALL-E, Stable Diffusion, and Midjourney were fun. But Large Language Models (LLMs), and particularly the recent demo of ChatGPT, seem to have put the fear of God into everyone from middle school English teachers to the CEO of Google. The potential partnership between OpenAI (the makers of ChatGPT) and Microsoft may even present the first substantive challenge to Google’s search monopoly we’ve ever seen - and that’s saying something. While most of the dialog around AI and education seems to be focused on assessment, I think the implications for instructional designers are critically important, too. And, because you’ve got to play the hits, let’s look at what their impact will be on OER as well. ...