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here's the question: just how bad are the 'just get it online' courses? How much extra value does all that expertise, time and money buy you? If we could spend less money and expand our access proportionately, would it still be worthwhile? I know that the professionals won't applaud the idea of a whole bunch of amateurs doing the job. But my take is, wouldn't it be great if they could? And where is the evidence that they can't?

The loss of our usual habits can cause shock and grief, so one way educators and parents can prioritize predictability is by creating routines.

Because trauma involves a loss of control, inflexible teaching methods can trigger some students into survival mode.

Relationships are key to resilience, “so anything that teachers can do to help foster relationships should be a priority right now,”

The short version: pretty much everyone went online; professors with the least online experience had to make the most adjustments, had the most to learn, and were most likely to just jump into giving lectures by videoconference.

Now we’re getting into the online pivot more substantially, higher education institutions are coming to terms it may not be a short-term emergency shift. It looks like the first semester of the 2020-21 year may be online, and if Covid-19 flares up again, who knows how long it may continue. While you could get away with “sticking classes on Zoom” for the immediate emergency, that won’t cut it in the medium term.

I’m sorry to tell you – there is no Go Online button

The good news is that it is entirely possible to create good, online courses in just about any subject, and students will do well in them and their performance and long term understanding of the topics will be as good, if not better, than those taught face to face. So that’s the good news, higher education isn’t going to die.

Can you give me examples of programs that are rooted in, speaking to, and resonant with conservative and religious communities in this country? In particular, I’d love to know about programs that work in conservative white Evangelical and religious black and LatinX communities? I’d love to hear how educators integrate progressive social justice values into conservative cultural logics. Context: To the best that I can tell, every program I’ve seen is rooted in progressive (predominantly white) ways of thinking. I know that communities who define “fake news” as CNN (as well as black communities who see mainstream media as rooted in the history of slavery and white supremacy) have little patience for the logics of progressive white educators. So what does media literacy look like when it starts with religious and/or conservative frameworks? What examples exist?

Can you tell me how you teach across gaslighting? How do you stabilize students’ trust in Information, particularly among those whose families are wary of institutions and Information intermediaries?Context: Foreign adversaries (and some domestic groups) are primarily focused on destabilizing people’s trust in information intermediaries. They want people to doubt everything and turn their backs on institutions. We are seeing the impact of this agenda. I’m not finding that teaching someone the source of a piece of content helps build up trust. Instead, it seems to further undermine it. So how do you approach media literacy to build up confidence in institutions and information intermediaries?

For what it’s worth, when I try to untangle the threads to actually address the so-called “fake news” problem, I always end in two places: 1) dismantle financialized capitalism (which is also the root cause of some of the most challenging dynamics of tech companies); 2) reknit the social fabric of society by strategically connecting people. But neither of those are recommendations for educators.

the virus will be far more present in far more places when school starts in August than it was when most schools shut down in March

I am among the crowd who both believes that online learning can be done quite well, and that there is something irreplaceable about the experiences of face-to-face learning, when that learning is happening under reasonable conditions that is. These are not reasonable conditions. Do not get me wrong. This is a loss. The experience of community is not the same at a distance or over the internet. It is not necessarily entirely absent, but it is not as present.

In an online environment, seminars will work best if they occur asynchronously in the discussion boards in an LMS

The 4 key elements for a seminar that need to be replicated during remote instruction include: A prompt or text(s) that the student considers independently in advance Guiding questions that require analysis, synthesize and/or evaluation of ideas The opportunity to share personal thinking with a group Ideas being developed, rejected, and refined over time based on everyone’s contributions

Students need specific guidance and support for how to develop, reject, and refine ideas appropriately in your course.  If you want students to share well, consider requiring an initial post where you and students introduce yourselves and share a picture. Describe your expectations for norms in how everyone will behave online Provide a lot of initial feedback about the quality of posting.  Consider giving samples of good and bad posts, and remember to clarify your marking criteria. Focus your expectations on the quality of comments, and set maximums for the amount you expect to reduce your marking load and keep the discussions high quality. Someone will need to moderate the discussion. That includes posting the initial threads, reading what everyone posts all weeks and commenting to keep the discussion flowing.  Likely, the same person (you or a TA) will also be grading and providing private feedback to each student. Consider making the moderation of a discussion an assignment in your course. You can moderate the first few weeks to demonstrate what you want, and groups of students can moderate other weeks. It can increase engagement if done well, and definitely decreases your work load.

How and why do well-educated, well-intentioned scientists produce biased AI systems? Sociological theories of privilege provide one useful lens.

Scientists also face a nasty subconscious dilemma when incorporating diversity into machine learning models: Diverse, inclusive models perform worse than narrow models.

fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent evidence of this grim reality. The commercial necessity of building market share led to the premature release of these systems.

AI tags photos of women in everyday situations as sexually suggestive. They also rate pictures of women as more “racy” or sexually suggestive than comparable pictures of men.

suppressed the reach of countless images featuring women’s bodies, and hurt female-led businesses – further amplifying societal disparities.

“Objectification of women seems deeply embedded in the system.”

Think of ChatGPT as a blurry JPEG of all the text on the Web. It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry JPEG, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.

a way to understand the “hallucinations,” or nonsensical answers to factual questions, to which large-language models such as ChatGPT are all too prone. These hallucinations are compression artifacts, but—like the incorrect labels generated by the Xerox photocopier—they are plausible enough that identifying them requires comparing them against the originals, which in this case means either the Web or our own knowledge of the world. When we think about them this way, such hallucinations are anything but surprising; if a compression algorithm is designed to reconstruct text after ninety-nine per cent of the original has been discarded, we should expect that significant portions of what it generates will be entirely fabricated.

ChatGPT is so good at this form of interpolation that people find it entertaining: they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it.

Please do not conflate word form and meaning. Mind your own credulity.

We’ve learned to make “machines that can mindlessly generate text,” Bender told me when we met this winter. “But we haven’t learned how to stop imagining the mind behind it.”

A handful of companies control what PricewaterhouseCoopers called a “$15.7 trillion game changer of an industry.” Those companies employ or finance the work of a huge chunk of the academics who understand how to make LLMs. This leaves few people with the expertise and authority to say, “Wait, why are these companies blurring the distinction between what is human and what’s a language model? Is this what we want?”