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The future of computer-aided education

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Manage episode 423572795 series 2712286
Content provided by Stanford Engineering & Russ Altman and Stanford Engineering. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Stanford Engineering & Russ Altman and Stanford Engineering or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Chris Piech is a professor of computer science who studies how computers can help students learn. In comparing human- and computer-aided education, he says humans are great one-on-one, but AI is more consistent at grading and feedback. He and colleagues have created several generative AI grading apps to take advantage of these relative strengths, as he tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

Episode Reference Links:

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Chapters:

(00:00:00) Introduction

Host Russ Altmans introduces guest Chris Piech, a professor of computer science at Stanford University.

(00:01:50) Defining Coding and Its Challenges

What coding entails for beginners and the challenges associated with learning to code.

(00:03:37) Enhancing Learning with Computers

How computers and AI can be used to make learning more enjoyable and effective.

(00:05:12) Human Connection in Education

The significance of teacher-student relationships and how recent learners can be effective teachers.

(00:07:02) AI and Coding Education

The impact of AI on professional coding and how it can enhance the learning experience for new coders.

(00:08:48) Joy of Programming

The creative joy of programming and how AI tools can elevate the creation process.

(00:11:57) Comparing Human and AI Tutors

Results from experiments comparing the effectiveness of human and AI tutors.

(00:14:43) Fair and Effective Assessment

Challenges and strategies for fair and effective computational assessment of students' work.

(00:16:42) Addressing Bias and Fairness in Grading

Demographic fairness in grading algorithms and the potential biases in different subjects.

(00:20:52) Interactive and Unstructured Feedback

Using AI to provide feedback on unstructured and interactive student work, like games and apps.

(00:25:30) Expanding Beyond Academic Tests

Application of AI in non-academic assessments, such as medical tests, to improve accuracy and efficiency.

(00:27:42) Generative Grading

Introduction to generative grading, where AI generates potential misconceptions to help with grading and feedback.

(00:31:37) Conclusion

Connect With Us:

Episode Transcripts >>> The Future of Everything Website

Connect with Russ >>> Threads or Twitter/X

Connect with School of Engineering >>> Twitter/X

  continue reading

274 episodes

Artwork
iconShare
 
Manage episode 423572795 series 2712286
Content provided by Stanford Engineering & Russ Altman and Stanford Engineering. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Stanford Engineering & Russ Altman and Stanford Engineering or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Chris Piech is a professor of computer science who studies how computers can help students learn. In comparing human- and computer-aided education, he says humans are great one-on-one, but AI is more consistent at grading and feedback. He and colleagues have created several generative AI grading apps to take advantage of these relative strengths, as he tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

Episode Reference Links:

Connect With Us:

Chapters:

(00:00:00) Introduction

Host Russ Altmans introduces guest Chris Piech, a professor of computer science at Stanford University.

(00:01:50) Defining Coding and Its Challenges

What coding entails for beginners and the challenges associated with learning to code.

(00:03:37) Enhancing Learning with Computers

How computers and AI can be used to make learning more enjoyable and effective.

(00:05:12) Human Connection in Education

The significance of teacher-student relationships and how recent learners can be effective teachers.

(00:07:02) AI and Coding Education

The impact of AI on professional coding and how it can enhance the learning experience for new coders.

(00:08:48) Joy of Programming

The creative joy of programming and how AI tools can elevate the creation process.

(00:11:57) Comparing Human and AI Tutors

Results from experiments comparing the effectiveness of human and AI tutors.

(00:14:43) Fair and Effective Assessment

Challenges and strategies for fair and effective computational assessment of students' work.

(00:16:42) Addressing Bias and Fairness in Grading

Demographic fairness in grading algorithms and the potential biases in different subjects.

(00:20:52) Interactive and Unstructured Feedback

Using AI to provide feedback on unstructured and interactive student work, like games and apps.

(00:25:30) Expanding Beyond Academic Tests

Application of AI in non-academic assessments, such as medical tests, to improve accuracy and efficiency.

(00:27:42) Generative Grading

Introduction to generative grading, where AI generates potential misconceptions to help with grading and feedback.

(00:31:37) Conclusion

Connect With Us:

Episode Transcripts >>> The Future of Everything Website

Connect with Russ >>> Threads or Twitter/X

Connect with School of Engineering >>> Twitter/X

  continue reading

274 episodes

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