49 Back to Table of Contents Exploring the Opportunities and Benefits of Standards for Adaptive Instructional Systems (AISs) Robert Sottilare 1 [0000-0002-5278-2441] , Avron Barr 2 , Robby Robson 3 , Xiangen Hu 4 , & Arthur Graesser 4 1 U.S. Army Research Laboratory, 2 IEEE Learning Technologies Standards Committee, 3 Eduworks, Inc., 4 University of Memphis robert.a.sottilare.civ@mail.mil avron@aldo.com, robby.robson@eduworks.com, {art.graesser; xiangenhu} @gmail.com Abstract. This paper describes the purpose, goals, and guiding questions for the Adaptive Instructional System (AIS) standards workshop within the 2018 Intel- ligent Tutoring Systems (ITS) Conference Industry Track. Adaptive instructional systems (AISs) use human variability, learner goals and preferences, and other learner/team attributes along with instructional conditions to develop/select ap- propriate strategies (domain-independent policies) and tactics (actions). The goal of adaptive instruction is to optimize learning, performance, retention, and the transfer of skills between training environments and the work or operational en- vironment where the skills learned during training are to be applied. The Institute for Electrical and Electronics Engineers (IEEE) Learning Technologies Stand- ards Committee (LTSC) established a study group in December 2017 to evaluate the efficacy of AIS standards and the authors of this paper proposed this work- shop (and several others) to inform stakeholders and solicit their participation. The interaction with stakeholders at the ITS conference will be through the ideas presented in paper presentations and via an expert panel composed of the authors of this paper and other authors in this workshop. Keywords: Adaptive Instructional Systems (AISs), Intelligent Tutoring Sys- tems (ITSs), IEEE standards, Learning Technologies Standards Committee (LTSC) 1 Introduction Adaptive instructional systems (AIS) are defined as: “computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each learner in the context of domain learning objec- tives” [1]. Examples of adaptive instructional systems include, but may not be limited to: intelligent tutoring systems (ITSs), intelligent mentors, recommender systems, per- sonal assistants for learning (PALs), and intelligent media (e.g., webpages) where the