How AI could advance computer-based tutors—and student success

Key points:

Benjamin Bloom explained the 2 sigma problem in his seminal 1984 article of the same title. Briefly stated, the problem consists of the following:

  • The average student who is taught by a tutor using mastery learning techniques outperforms 98 percent of students taught in a typical classroom.
  • Society cannot afford to provide full-time tutors for every student.
  • As a result, the majority of students fail to reach their potential due to the way we teach them.

Because there appeared to be no viable path to providing each student with their own personal tutor, Bloom called on educational researchers to “find methods of group instruction as effective as one-on-one tutoring.” Educational technologists weren’t so quick to give up on the idea of providing every student with their own individual tutor, however, and have made laudable progress toward the 2 sigma goal by designing what are called intelligent tutoring systems (ITS). Unfortunately, these systems are both difficult and expensive to design and build, and typically work only in a single domain of knowledge (such as algebra).

The recent development of large language models (LLMs) like ChatGPT has opened entirely new possibilities for the design and implementation of computer-based tutors with the potential to help all students achieve Bloom’s two sigma performance threshold.

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