Adaptive Instructional Systems (AIS) Consortium Established!December 2020
The IEEE Learning Technology Standards Committee (LTSC) has spun off an industry and academic alliance called the Adaptive Instructional System (AIS) Consortium. AISs are training and educational technologies that tailor instruction to the needs, preferences and interests of each individual learner or team.
The mission of the AIS Consortium is to is to support the adoption and advancement of products that use artificial intelligence and advanced technologies to help people learn. The AIS Consortium connects professionals and organizations who believe in the power of personalized learning and adaptive instruction to:
enable the development and availability of affordable, practical, effective solutions in the marketplace
improve learning outcomes and transfer of learning opportunities
enable equitable education and training opportunities for motivated learners
accelerate the growth of human capital through personalized learning, adaptive instruction, and learning engineering practices
make it easy for teachers to use AIS technologies as instructional tools to augment their teaching
The AIS Consortium has been founded as a 501.c.6 non-profit alliance under the IEEE Industry Standards & Technology Organization. If you want to join the consortium or know more about what it does, please contact Bob Sottilare at firstname.lastname@example.org.
Understanding the Benefits of Adaptive InstructionDecember 2020
Effectiveness - Adaptive instruction is more effective than corresponding non-adaptive instruction (Aleven etal 2017).
Effectiveness - Adaptive instruction is as effective as human tutoring, but much more scalable and cost effective.
According to VanLehn (2011), the effect size of expert human tutors was about 0.79 (Cohen's d) and the effect size of intelligent tutoring systems (a type of adaptive instruction technology) was about 0.76 (no significant difference).
According to a meta-analysis of digital tutors (intelligent tutoring systems) by Kulik & Fletcher (2016), they found an effect size of 0.75 for 39 properly aligned studies.
The effectiveness studies analyzed compared one-to-one tutoring (human and computer-based) to non-tutoring methods (e.g., traditional classroom instruction).
This means that computer-based adaptive instruction of cognitive tasks has been found to be as effective as expert human tutors.
However, computer-based adaptive instruction offers higher accessibility, flexibility (self-paced learning) and scalability when compared to one-to-one human tutoring methods.
Efficiency, Engagement & Flow - Higher levels of engagement and maintenance of flow ( Csikszentmihalyi, Abuhamdeh & Nakamura, 1990) reduces off-task behaviors (Baker, 2007).
The ability to tailor instruction to the competency level of the individual learner or team maintains a high level of engagement (Vygotsky, 1993).
Adaptive instructional technologies like intelligent tutoring systems have been shown to be more engaging than classroom instruction as evidenced by the higher frequency of learner questions; each student asks about 0.1 questions per hour of classroom instruction while each student asks more than 20 questions per hour of computer-based adaptive instruction.
Efficiency - Adaptive instruction accelerates learning and reduces time to competency
DARPA Educational Dominance program - 16 weeks of adaptive tutoring produced graduates who were superior in knowledge and practical skills to technicians with as many as 9 years of experience in the fleet.
The US military spent about $8.8 B for residential instruction in 2016 ($5.1 B in skill training & $3.7 B in education).
“Individualization is an educational imperative and an economic impossibility” (Michael Scriven, 1975), but times (and technology) change.
We can’t afford a human tutor for every learner, but we can afford a computer, or a smartphone, or tablet with internet access.
The accessibility of online adaptive instruction 24/7 greatly reduces the need for military training infrastructure (e.g., brick and mortar schoolhouses).
Only a 10% reduction in residential instruction without a reduction in instructional effectiveness would result in savings of $880M which includes time, travel and infrastructure savings.
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2017). Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 522-560). New York: Routledge.
Baker, R. S. (2007, April). Modeling and understanding students' off-task behavior in intelligent tutoring systems. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 1059-1068).
Csikszentmihalyi, M., Abuhamdeh, S., & Nakamura, J. (1990). Flow.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
Vygotsky, L. S. (1993). The collected works of L. S. Vygotsky: Vol. 2. The fundamentals of defectology (abnormal psychology and learning disabilities) (J. E. Knox & C. IB. Stevens, Trans.). New York: Plenum.