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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,”

My recommendation for people planning their courses, is to stop thinking about ‘contact hours’. A contact hour is a constraint that is applied to the learning process because of the organizational need to have people share a space in a building. Also called a credit hour, (particularly for American universities) this has meant, from a workload perspective, that for every in class hour a student is meant to do at least 2 (in some cases 3) hours of study outside of class. Even Cliff Notes agrees with me. So… for a full load, that 30 to 45 Total Work Hours for students per course that you are designing.

Simple break down (not quite 90, yes i know) Watch 3 hours of video* – 5 hoursRead stuff – 20 hoursListen to me talk – 15 hoursTalk with other students in a group – 15 hoursWrite reflections about group chat – 7.5 hoursRespond to other people’s reflections – 7.5 hoursWork on a term paper – 10 hoursDo weekly quiz – 3 hoursWrite take home mid-term – 3 hoursWrite take home final – 3 hours

A thousand variations of this might be imagined

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.

“You cannot have one single uncontested definition of raciness.”

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

researchers showed that there are large amounts of privately identifiable information (PII) in OpenAI’s large language models. They also showed that, on a public version of ChatGPT, the chatbot spit out large passages of text scraped verbatim from other places on the internet

ChatGPT’s “alignment techniques do not eliminate memorization,” meaning that it sometimes spits out training data verbatim. This included PII, entire poems, “cryptographically-random identifiers” like Bitcoin addresses, passages from copyrighted scientific research papers, website addresses, and much more.

The researchers wrote that they spent $200 to create “over 10,000 unique examples” of training data, which they say is a total of “several megabytes” of training data. The researchers suggest that using this attack, with enough money, they could have extracted gigabytes of training data. The entirety of OpenAI’s training data is unknown, but GPT-3 was trained on anywhere from many hundreds of GB to a few dozen terabytes of text data.