Pretty good evidence, theoretically and empirically, that this is better than "give a homework set and a grade" or "literally do nothing between exams", which combined are 90% of univ courses. So I don't get the "lmao" nor do I get the "let's dig up month old posts by my colleague and insult them".
Why is this good? Education researchers refer to Bloom's taxonomy: friction that requires students to explain/teach rather than passively consume increases learning. When you answer wrong on our quizzes, you must explain your thoughts. You pass the quiz when you prove you've learned! 2/2
We've passed 100 paying institutions for www.alldayta.com! Super exciting to see profs using it in 26 countries, from Ivy League grad courses on down. About to release updates to our AI-driven, prof-in-loop, adaptive quiz: first thing we've done some students dislike (but that's a good thing!) 1/2
Folks who know things: what has led so many universities to make speakers "register as suppliers" to get reimbursed? There is no way the law requires this and it's incredibly annoying. I assume the answer is "bureaucrats create bureaucracy", as always, but am I missing something?
(Also, we set a new one-class weekly record for use this week! This is a big Principles class at a large public univ, but 39k uses of Intelligent Quiz (an AI-driven adaptive quiz) + 3k direct questions in one week. Wild. Across term- with exams + your good setup- 40-80 uses/student is normal.)
So much studying & revision in All Day TA classes happens via platform that I can see exact day, time, nature & type of studying students are doing. Gap (in my classes but also for customers) between exam weeks and others makes me thing we need more testing, not less, and more spread out over term.
Something I didn't expect that we can see in www.alldayta.com data: study effort is so correlated with exams. Across courses, if use on Finals week is "1", relative use on midterm week is .7, and on non-exam weeks .05-.1. For courses with no exam, use always sticks at the lower level.
And in general LLMs are both better coders and a better fit for "open languages". I think you would be blown away using Codex or Claude Code with R or Python - it is just native, not "copy and paste".
A must watch - Mokyr's Nobel Prize speech on how ideas matter for progress, where AI fits here, and where bad institutional decisions might harm things. His ageless energy and unlimited memory for history are exactly today as I remember as his student 15 years ago. www.youtube.com/live/jQCkCcu...
A theorem: AI debate today is a rehash of CP Snow's Two Cultures & the Bluesky-level-hostile response by Leavis. But Snow was right - it *should* be as embarrassing for educated people to not know to Laws of Thermodynamics (or basics of how ML works!) as to not read Shakespeare.
Hiring this year in economics or management? I try to read every JMP on innovation, entrepreneurship, or econ of AI; why not make the list public? Here's 54 candidates on the market this year (link below). Many great papers + NBER Innovation PhD boot camp grads! kevinbryanecon.com/2025innovati...
Doing my part on CBC's natl econ show to get normies to learn what 'feeling the AI' means. (More importantly, "compute sovereignty nonsense b/c you don't control stack" & "options aren't adopt AI at car plant or don't adopt, but adopt & keep plant or it goes to China".) www.cbc.ca/listen/live-...
What a great day: legends of innovation economics Mokyr, Aghion and Howitt win the Nobel. Joel was a PhD advisor of mine, so need a full article! Included: good & bad explanations of the Indus Rev, Aghion's charisma, influence of Jon Hughes, French fashion houses: kevinbryanecon.com/mokyraghionh...
Kelly et al is one I love pointing people to. For general reading, Editor's Introduction at PhD level, Industrious Land at lower levels. For anyone, Lever of Riches. For people of a theoretical bent, Gifts. For Irish-Americans named Kevin who wonder why the Famine barely appears, Why Ireland ;-).
PS - 1) Yes, we're building this out even further. Everything a student does, it should be as close as possible to a tutor who knows what you want to teach sitting by their side. 2) And All Day TA also *reports back to you* summaries of these conversations so you know where students went wrong!
The path forward for higher ed w/ AI is that teaching *complements* AI. Students learn more using AI, and learn *exactly* what we want to teach, instead of just cheating. Can do even better than pre-AI! (And if you want this - alldayta.com, $100 per term. Super easy.)
For students, *I don't care* if they get right answers. Why? If wrong, they have to explain to AI what they were thinking before moving on. Cheating doesn't save time b/c I don't grade on correctness, just whether you work through the quiz! Try it here app.alldayta.com/university-o... 3/4
Toss my lecture audio, slides, handouts into a module. A crazy AI workflow pulls out learning goals. AI then spins up questions (you can approve or not) from your docs plus context on why students might get them wrong. 2/4
I know it's my company, but All Day TA AI-driven quizzes are so good. Student cheat on all take-home work. How do you get them to learn? Do even better than we used to by having them learn *as they do low-staked hw*. Here's use just this week in a Texas univ course - students really use this. 1/4
Wasn't sure this particular one was a good fit for the bsky crowd, hah!
Hah! Yeah, no one really studies "social science", but eg at an engineering crowd talk, I wouldn't bat at eye at an economist or a political scientist saying "as a social scientist, how we should look at this is..." or similar
A much more common term in the US than Europe. Social Science departments and even high school classes called "social science" are very common over here so that self description wouldn't make anyone bat an eye.
Syllabus here: kevinbryanecon.com/Bryan-Progre...
Course AI here on AllDayTA (to be updated weekly as we progress, including with AI-driven adaptive quizzes!): app.alldayta.com/university-o... 2/2
New class on Progress starting tomorrow - I'm amped! Trying to put some rigor from economics, economic history, and philosophy on a topic very much in the air. It will be awesome.
(And first class running slides in my all html browser-based slideshow program - details soon!) 1/2
I cover project setup, version control, my daily very simple workflow, what to use for code (Python or R), what AI is high value, why LaTeX, how to do it easily, why all this matters even for qual projects, and links to exactly what to d/l. My own practices were very sloppy-this is a better way. 2/2
Perhaps of interest to folks with social science PhD programs: at Rotman, we added an experimental 3 session "tech stack" training in addition to the math boot camp. My lecture was "how to do reproducible, open, quick research", aka version control, LaTeX, AI. 1/2 kevinbryanecon.com/techstack.html
PS - An awesome dev of ours was testing the featureand told me "I got it wrong on purpose at first for testing, but then forgot to divide by 2 for expected value until the system brought me there!" Exactly. Imagine this help for the student, and then summed up & reported back to you for each hw!
Honestly, it's a really nice system. As always, everything is all siloed: your content is never used for any training nor leaves your course, and is deleted on demand. The whole alldayta.com is $100 per class section per term - a couple bucks per student on average. AI complements our teaching! 6/6
So instead of "here's a hw, half of you go home and cheat, the rest hand it in and get 7/10, and neither you nor the student fully understands what was done wrong", we deter cheating, ensure everyone 'gets enough questions correct' and thinks through incorrect ones, and report back to the prof. 5/6
When the students do their assignment, and get a question wrong, the AI forces them to explain their logic, then uses your lectures, handouts, and so on to try to correct mistakes. We then use another AI system to report back to you precisely where students have been going wrong *and why*. 4/6