Commencement season is here and, as many students are closing one chapter and stepping into the next, it's a nice moment to ask: what did learning really look like for these students, and how might it change for the next generation? With those questions in mind, we’re re-releasing a conversation with Computer Science Professor Chris Piech on the future of computer-aided education. Chris studies how computers can and will help students learn. His message isn't that teachers are obsolete — far from it. He shares that the future of education certainly involves AI, but that we must never lose the human element. Whether you're a new grad, a lifelong learner, or an educator wondering what's coming next, this one is well worth another listen. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to [email protected]. Episode Reference Links: Stanford Profile: Chris Piech Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Chris Piech, a professor of computer science from Stanford University. (00:01:44) Teaching People to Code What programming is and why learning to code can be challenging. (00:02:54) Motivation in Learning Why joy and motivation are central challenges in education. (00:03:54) Recent Learners as Teachers How near-peer teachers helped scale a Stanford coding course to thousands  (00:07:10) AI and Computer Programming How generative AI is changing coding for students and professionals. (00:09:24) The Joy of Programming How AI tools can expand what learners are able to create. (00:12:41) Experiments with Teaching What experiments reveal about one-on-one teaching & AI support. (00:14:39) Rethinking Assessment The value Piech sees in computational assessment. (00:16:38) Fairness in Grading Why AI grading raises questions about bias, context, and real-world use. (00:20:59) Feedback & Assessment How computers can evaluate creative and less structured assignments. (00:22:21) Dream Grader A system that interacts with student projects to understand and assess them. (00:25:30) Beyond the Classroom How assessment tools can also support medical testing. (00:26:52) Measuring Vision More Precisely Using adaptive testing to improve eye exams and track subtle changes. (00:27:57) Generative Grading What is generative grading and how can it actually function and be useful? (00:29:44) Teachers and AI Together Why the future of grading may depend on combining teacher insight with AI support. (00:31:33) Conclusion   Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.