Intelligent Support for All?: A Literature Review of the (In)equitable Design & Evaluation of Adaptive Pedagogical Systems for CS Education

The computer science education community has created many adaptive feedback tools and intelligent tutoring systems to improve students’ experience in computing-related courses. However, the extent to which these systems—which we collectively refer to as adaptive pedagogical systems—support equitable outcomes for learners of all genders and racial identities is not known. We conducted a systematic literature review of SIGCSE, ITiCSE, and ICER publications on adaptive pedagogical systems in computing courses from the last five years. The results reveal that not only is there little to no data on the effectiveness of adaptive pedagogical systems for CS education by gender or race, the vast majority of published papers reporting on these systems do not even include the demographics of their users. Based on these findings, this position paper makes a call to action: we must include the voices of historically marginalized students in the design and evaluation of our software, lest we continue to perpetuate that marginalization. We highlight key ideas that every CS education researcher should consider when designing and evaluating technologies to support learners. We argue that this community must hold ourselves and each other accountable to create technologies that support learners equitably.