A Conceptual Model for Hybrid Adaptive Instructional and Assessment Systems

Adaptive instructional systems (AIS) hold great promise for increasing the effectiveness and efficiency of educational systems due to their ability to tailor instruction to the specific needs of learners. In addition, because AIS necessarily elicit evidence of learning from students to drive adaptive decisions, there has long been interest in mining data from these systems for broader assessment purposes such as program evaluation and policy making. Recently, this desire was accelerated by the novel Coronavirus pandemic. It became difficult and in some cases impossible to bring groups of students together to take standardized assessments capable of providing comparable data points suitable for aggregation at district and state levels. Simultaneously, online AIS became a part of daily instruction for many students. This paper examines theories of change that have been proposed for AIS and theories of change commonly associated with assessment and accountability in K-12 education in the United States. It then proposes a conceptual model for hybrid adaptive instructional and assessment systems (AIAS) with the unique contribution of explicitly representing the role of the AIS within the broader assessment eco-system. Finally, the paper reflects on research that will be necessary to realize the benefits of the hybrid AIAS.