<story><title>Making AI better at math tutoring</title><url>https://blog.khanacademy.org/why-were-deeply-invested-in-making-ai-better-at-math-tutoring-and-what-weve-been-up-to-lately/</url></story><parent_chain><item><author>JustinSkycak</author><text>Conversational dialogue seems like a fascinating distraction.&lt;p&gt;Many people who have (unsuccessfully) attempted to apply AI to education have focused too much on the &amp;quot;explanation&amp;quot; part and not enough on scaffolding, navigating, and managing the entire learning process. It’s easy to go on a wild goose chase building an explanation AI.&lt;p&gt;You fall in love with the idea of AI having conversational dialogue with students, and then you get lost in the weeds of complexity. You solve just enough of the problem to produce a cool demo, yet you&amp;#x27;re still hopelessly far away from self-service learning in real life.&lt;p&gt;I don&amp;#x27;t think conversational dialogue is even necessary.&lt;p&gt;What we do at mathacademy.com is hard-code explanations and break them up into bite-size pieces that are served at just the right moment. And we close the feedback loop by having students solve problems, which they need to do anyway. (The student&amp;#x27;s &amp;quot;response&amp;quot; is whether they got the problem correct.)&lt;p&gt;Sure, hard-coding explanations feels tedious, takes a lot of work, and isn&amp;#x27;t &amp;quot;sexy&amp;quot; like an AI that generates responses from scratch – but at least it&amp;#x27;s not a pipe dream. It&amp;#x27;s a practical solution that lets us move on to other components of the AI that are just as important.&lt;p&gt;What are those other components? A handful off the top of my head:&lt;p&gt;* After a minimum effective dose of explanation, the AI needs to switch over to active problem-solving. Students should begin with simple cases and then climb up the ladder of difficulty, covering all cases that they could reasonably be expected to solve on a future assessment.&lt;p&gt;* Assessments should be frequent and broad in coverage, and students should be assigned personalized remedial reviews based on what they answered incorrectly.&lt;p&gt;* Students should progress through the curriculum in a personalized mastery-based manner, only being presented with new topics when they have (as individuals, not just as a group) demonstrated mastery of the prerequisite material.&lt;p&gt;* After a student has learned a topic, they should periodically review it using spaced repetition, a systematic way of reviewing previously-learned material to retain it indefinitely into the future.&lt;p&gt;* If a student ever struggles, the system should not lower the bar for success on the learning task (e.g., by giving away hints). Rather, it should strengthen a student’s area of weakness so that they can clear the bar fully and independently on their next attempt.</text></item></parent_chain><comment><author>chongli</author><text>I find myself endlessly frustrated by these discussions around the teaching of mathematics. People seem to want to make it more conversational, more interactive, more engaging. It doesn&amp;#x27;t work that way! Learning mathematics is like learning to play a musical instrument. No amount of 1-on-1 discussion with a teacher will get you to mastery.&lt;p&gt;You just need to practice. For hours and hours and hours.&lt;p&gt;The teacher&amp;#x27;s job is to help guide you to what you might want to study next. The teacher cannot replace individual practice time.</text></comment>
<story><title>Making AI better at math tutoring</title><url>https://blog.khanacademy.org/why-were-deeply-invested-in-making-ai-better-at-math-tutoring-and-what-weve-been-up-to-lately/</url></story><parent_chain><item><author>JustinSkycak</author><text>Conversational dialogue seems like a fascinating distraction.&lt;p&gt;Many people who have (unsuccessfully) attempted to apply AI to education have focused too much on the &amp;quot;explanation&amp;quot; part and not enough on scaffolding, navigating, and managing the entire learning process. It’s easy to go on a wild goose chase building an explanation AI.&lt;p&gt;You fall in love with the idea of AI having conversational dialogue with students, and then you get lost in the weeds of complexity. You solve just enough of the problem to produce a cool demo, yet you&amp;#x27;re still hopelessly far away from self-service learning in real life.&lt;p&gt;I don&amp;#x27;t think conversational dialogue is even necessary.&lt;p&gt;What we do at mathacademy.com is hard-code explanations and break them up into bite-size pieces that are served at just the right moment. And we close the feedback loop by having students solve problems, which they need to do anyway. (The student&amp;#x27;s &amp;quot;response&amp;quot; is whether they got the problem correct.)&lt;p&gt;Sure, hard-coding explanations feels tedious, takes a lot of work, and isn&amp;#x27;t &amp;quot;sexy&amp;quot; like an AI that generates responses from scratch – but at least it&amp;#x27;s not a pipe dream. It&amp;#x27;s a practical solution that lets us move on to other components of the AI that are just as important.&lt;p&gt;What are those other components? A handful off the top of my head:&lt;p&gt;* After a minimum effective dose of explanation, the AI needs to switch over to active problem-solving. Students should begin with simple cases and then climb up the ladder of difficulty, covering all cases that they could reasonably be expected to solve on a future assessment.&lt;p&gt;* Assessments should be frequent and broad in coverage, and students should be assigned personalized remedial reviews based on what they answered incorrectly.&lt;p&gt;* Students should progress through the curriculum in a personalized mastery-based manner, only being presented with new topics when they have (as individuals, not just as a group) demonstrated mastery of the prerequisite material.&lt;p&gt;* After a student has learned a topic, they should periodically review it using spaced repetition, a systematic way of reviewing previously-learned material to retain it indefinitely into the future.&lt;p&gt;* If a student ever struggles, the system should not lower the bar for success on the learning task (e.g., by giving away hints). Rather, it should strengthen a student’s area of weakness so that they can clear the bar fully and independently on their next attempt.</text></item></parent_chain><comment><author>JustinSkycak</author><text>Should mention that I recently wrote an essay that elaborates on this argument with plenty more detail: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.justinmath.com&amp;#x2F;the-situation-with-ai-in-education&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.justinmath.com&amp;#x2F;the-situation-with-ai-in-educatio...&lt;/a&gt;</text></comment>