Using an Intelligent Tutoring System (ITS) to Teach Sequencing and Pattern Recognition

Abstract

Computational thinking skills are being increasingly recognized as critical life skills in the 21st century because they offer people the ability to uniquely approach and solve problems. Currently, CT skills are taught programmatically through activities like coding, block coding, and robotics. While this can be a helpful way to learn, it only teaches CT skills in technical contexts, making the CT skill set not as widely applicable. Most existing research looks at creating frameworks for incorporating CT skills in the classroom, and these frameworks are typically geared towards integrating CT skills in STEM classrooms. To fill this gap an Intelligent Tutoring System (ITS) was designed to help students develop two specific CT skills: sequencing and pattern recognition. OpenAI’s GPT-4.1-nano model was used to create the assistant, and was customized with instructions and sample problems the ITS could use to teach these skills through Language Arts and Math based practice problems. To teach the CT skills mentioned above, the ITS generates practice problems with increasing difficulty to ensure mastery of each topic before moving on. The topics included are Linear Sequencing, Conditional Sequencing, Repetitive Sequencing, Number Patterns, and Input-Output Tables. Each topic is broken into three difficulty levels that the ITS provides five practice problems each for. For future work I hope to test the effectiveness of this tool on elementary students by giving them a pre-test on sequencing and pattern recognition problems, allowing them to work with the tool, and then giving the students a post-test on the same skills to measure growth.