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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.

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.

About that obsession you’ve got with students turning on their cameras during class. I understand why you’ve got it. I’d like to help you deal with it. I say “deal with it” because many students complain to me that they don’t like being forced to turn their cameras on

it’s probably essential to our wellbeing to see human faces. As a teacher and presenter and facilitator, seeing facial expressions and reactions of audience/participants makes a huge difference. I get it. I get that you need to know someone is listening, and see those reactions. I get it. I recently gave a keynote and asked a few friends to be on webinar panel so I could see their smiling faces. However, when I am in a position of power like in the class, I never ask students to turn on their cameras. And my students were *almost always all engaged* last semester in our Zoom calls.

You can’t make eye contact online

In the long run, we believe, teachers need to help students develop a critical awareness of generative machine models: how they work; why their content is often biased, false, or simplistic; and what their social, intellectual, and environmental implications might be. But that kind of preparation takes time, not least because journalism on this topic is often clickbait-driven, and “AI” discourse tends to be jargony, hype-laden, and conflated with science fiction.

Make explicit that the goal of writing is neither a product nor a grade but, rather, a process that empowers critical thinking

No one should present auto-generated writing as their own on the expectation that this deception is undiscoverable. 

The surge of attention around ChatGPT is prompting pressure inside tech giants including Meta and Google to move faster, potentially sweeping safety concerns aside

Tech giants have been skittish since public debacles like Microsoft’s Tay, which it took down in less than a day in 2016 after trolls prompted the bot to call for a race war, suggest Hitler was right and tweet “Jews did 9/11.”

Some AI ethicists fear that Big Tech’s rush to market could expose billions of people to potential harms — such as sharing inaccurate information, generating fake photos or giving students the ability to cheat on school tests — before trust and safety experts have been able to study the risks. Others in the field share OpenAI’s philosophy that releasing the tools to the public, often nominally in a “beta” phase after mitigating some predictable risks, is the only way to assess real world harms.

The problem waiting round the corner for universities is essays generated by AI, which will leave a textual pattern-spotter like Turnitin in the dust. (Earlier this year, I came across one essay that felt deeply odd in some not quite human way, but I had no tangible evidence that anything untoward had occurred, so that was that.)

To accuse someone of plagiarism is to make a moral charge regarding intentions. But establishing intent isn’t straightforward. More often than not, the hearings bleed into discussions of issues that could be gathered under the heading of student ‘wellbeing’, which all universities have been struggling to come to terms with in recent years.

I have heard plenty of dubious excuses for acts of plagiarism during these hearings. But there is one recurring explanation which, it seems to me, deserves more thoughtful consideration: ‘I took too many notes.’ It isn’t just students who are familiar with information overload, one of whose effects is to morph authorship into a desperate form of curatorial management, organising chunks of text on a screen. The discerning scholarly self on which the humanities depend was conceived as the product of transitions between spaces – library, lecture hall, seminar room, study – linked together by work with pen and paper. When all this is replaced by the interface with screen and keyboard, and everything dissolves into a unitary flow of ‘content’, the identity of the author – as distinct from the texts they have read – becomes harder to delineate.

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?”