“Students aren’t great at asking questions well.” This was Kristen DiCerbo, Khan Academy’s Chief Learning Officer, in a recent Chalkbeat article. She was offering her explanation for why Khanmigo hadn’…
How Khan Academy Is Building a Better AI Tutor: Our Most Recent Learnings
Khan Academy shares how it improved its AI tutor Khanmigo with faster responses, smarter data use, and better student learning outcomes.
Apple's Student Discount
Learning in the Open: What AI Is (and Isn’t) Changing
Khan Academy shares what’s working with AI tutoring, what isn’t, and what we’ve learned from Khanmigo to better support student learning.
Do People Ask Good Questions?
People ask questions in order to efficiently learn about the world. But do people ask good questions? In this work, we designed an intuitive, game-based task that allowed people to ask natural language questions to resolve their uncertainty. Question quality was measured through Bayesian ideal observer models that considered large spaces of possible game states. During free-form question generation, participants asked a creative variety of useful and goal-directed questions, yet they rarely asked the best questions as identified by the Bayesian ideal observers (Experiment 1). In subsequent experiments, participants strongly preferred the best questions when evaluating questions that they did not generate themselves (Experiments 2 and 3). On one hand, our results show that people can accurately evaluate question quality, even when the set of questions is diverse and an ideal observer analysis has large computational requirements. On the other hand, people have a limited ability to synthesize maximally informative questions from scratch, suggesting a bottleneck in the question asking process.
The Interview Question Bank for Engineering Managers
Organised by theme, and every question comes with a short thinking prompt
Why Some Students Learn Faster
A hypothesis about teaching and learning
Why Some Students Learn Faster
A hypothesis about teaching and learning
Terence Tao (@tao@mathstodon.xyz)
In basic research, such as pure mathematics, one might naively expect that the natural question to ask with regards to a given problem X in a field is "What is the answer to X?". But in many cases the more valuable question is "What can be learned from studying X?" The answer to X itself can of course be one of the things learned in this process of study; but one can learn far more useful information besides, such as * What are the main difficulties to overcome to resolve X? * What new techniques can one discover in order to solve X? * Why are existing techniques insufficient to solve the problem by itself? * How does X relate to results in prior literature? * Can one uncover new connections between X and other topics Y, Z, ...? * What are some natural related or followup questions X', X'', ... to study? Nevertheless, until recently the two questions were closely aligned, to the point where it was not really necessary to distinguish the two: the only practical route to solving a difficult problem was to first address many of the subquestions listed above. (1/3)
The Despair of the Professor in the Age of A.I.
“Was it always the case that half of our students would cheat if it were easy enough?”
Why Grades Shouldn't Exist - Alfie Kohn
Teachers Are Not OK
AI, ChatGPT, and LLMs "have absolutely blown up what I try to accomplish with my teaching."
How Learning Happens | Seminal Works in Educational Psychology and Wha
How Learning Happens introduces 28 giants of educational research and their findings on how we learn and what we need to learn effectively, efficiently, and
Methodologies for Improving the Quality of AI Tutoring in K-12 Education
Many AI tutors leverage large language models (LLMs) today. Given that LLMs are opaque black boxes, robust evaluation and live experimentation to measure the impact of every change are essential. We pioneered AI-powered tutoring for K-12 with the launch of Khanmigo (Khan Academy 2023). We describe the metrics we use to measure AI tutoring quality and student engagement as well as various experiments we have run. We highlight the changes that have moved our metrics, including models, prompting, personalization and agents.
College professors face an emerging challenge: We know students are no longer reading entire books
Some teachers increasingly are shifting their approach to reading, assigning fewer books than they once did.
The Missing Discipline in Computer Science
On Teaching Computer Scientists to Ask “Why”
Just a professor standing in front of BlueSky demoralized because exams my students used to get a mean of 83% on prior to 2020 are now failed in large numbers. It seems that their ability to APPLY concepts to new contexts/domains has all but disappeared. I love these students & I am worried.
