Designing AI Tutors for Interest-Based Learning: Insights from Human Instructors

TL;DR

This study analyzes how human instructors implement interest-based learning in one-to-one online tutoring to inform the design of AI tutors capable of scalable, personalized instruction using large language models.

Contribution

It provides an in-depth analysis of human IBL teaching practices to guide the development of AI tutors for interest-based learning.

Findings

Tutors integrate student interests through personalized content.

Interest integration enhances student engagement and relevance.

Design implications support scalable AI tutor development.

Abstract

Interest-based learning (IBL) is a paradigm of instruction in which educational content is contextualized using learners' interests to enhance content relevance. IBL has been shown to result in improved learning outcomes. Unfortunately, high effort is needed for instructors to design and deliver IBL content for individual students. LLMs in the form of AI tutors may allow for IBL to scale across many students. Designing an AI tutor for IBL, however, first requires an understanding of how IBL is implemented in teaching scenarios. This paper presents a study that seeks to derive this understanding from an analysis of how human instructors design and deliver IBL content. We studied 14 one-to-one online tutoring sessions (28 participants) in which tutors designed and delivered a lesson tailored to a student's self-identified interest. Using lesson artifacts, tutoring transcripts, interviews, and questionnaires, findings include themes on how tutors integrate interests during instruction and why. Finally, actionable design implications are presented for LLM-powered AI tutors that aim to deliver IBL at scale.