Exploring opportunities to standardize adaptive instructional systems (AISs)
This goal of the proposed workshop is to explore opportunities to standardize components and/or processes within a class of technologies called adaptive instructional systems (AISs) which include Intelligent Tutoring Systems (ITSs), intelligent media, and other learning tools and methods used to guide/optimize instruction for individual learners and teams. AISs use human variability (e.g., performance, preferences, affect) and other learner/team attributes along with instructional conditions to develop/select appropriate learning strategies (domain-independent policies) and tactics (tutor actions). Within AISs, the relationship of the learner(s) states/traits, environmental conditions (context within a learning experience), and AIS decisions is usually described by a machine learning algorithm which is used to select an action or set of actions to optimize one or more learning outcomes. Outcomes include: knowledge acquisition, skill development, retention, performance, and transfer of skills between the instructional environment and the work or operational environment where the skills learned during instruction may be applied. This workshop will present standardization opportunities and strategies with the goal of reducing the entry skills/time required to develop AISs, and promoting opportunities for reuse.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sottilare, R., Robson, R., Barr, A., Graesser, A., & Hu, X. (2018). Exploring opportunities to standardize adaptive instructional systems (AISs). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10948 LNAI, 567-569. Retrieved from https://digitalcommons.memphis.edu/facpubs/7893