Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact

  • Candace WalkingtonEmail author
  • Matthew L. Bernacki


Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students’ individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of personalization may be moderated by students’ depth of quantitative engagement with their out-of-school interests. We also examine whether math problems designed to draw upon students’ knowledge of their individual interests at a deep level (i.e., actual quantitative experiences) or surface level (i.e., superficial changes to problem topic) have differential effects. Results suggest that connecting math instruction to students’ out-of-school interests can be beneficial for learning in an ITS and reduces gaming the system. However, benefits may only be realized when students’ degree of quantitative engagement with their out-of-school interests matches the depth at which the personalized problems are written. Students whose quantitative engagement with their interests is minimal may benefit most when problems draw upon superficial aspects of their interest areas. Students who report significant quantitative engagement with their interests may benefit most when individual interests are deeply incorporated into the quantitative structure of math problems. We also find that problems with deeper personalization may spur positive affective states and ward off negative ones for all students. Findings suggest depth is a critical feature of personalized learning with implications for theory and AI instructional design.


Personalization Personalized learning Individual interest Intelligent tutoring systems 



This work was supported by the Pittsburgh Science of Learning Center, which was supported by the National Science Foundation, Award #0836012. Thank you to Gail Kusbit, Ryan Baker, Steve Ritter, Steve Fancsali, Vincent Aleven, and Timothy Nokes-Malach for their generous assistance with this research. Thank you to Carnegie Learning for their collaboration and support of this project. Thank you to Elizabeth Howell and Alyssa Holland for their assistance with data transcription and coding. And finally, thank you to the classroom teachers involved in this study.


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Copyright information

© International Artificial Intelligence in Education Society 2018

Authors and Affiliations

  1. 1.Southern Methodist UniversityDallasUSA
  2. 2.University of Nevada, Las VegasLas VegasUSA

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