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Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes

National Institute of Education, Nanyang Technological University Singapore, Singapore 639798, Singapore
Big Data Cogn. Comput. 2019, 3(3), 46; https://doi.org/10.3390/bdcc3030046
Received: 31 May 2019 / Revised: 17 July 2019 / Accepted: 23 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Artificial Superintelligence: Coordination & Strategy)
Artificial intelligence-enabled adaptive learning systems (AI-ALS) have been increasingly utilized in education. Schools are usually afforded the freedom to deploy the AI-ALS that they prefer. However, even before artificial intelligence autonomously develops into artificial superintelligence in the future, it would be remiss to entirely leave the students to the AI-ALS without any independent oversight of the potential issues. For example, if the students score well in formative assessments within the AI-ALS but subsequently perform badly in paper-based post-tests, or if the relentless algorithm of a particular AI-ALS is suspected of causing undue stress for the students, they should be addressed by educational stakeholders. Policy makers and educational stakeholders should collaborate to analyze the data from multiple AI-ALS deployed in different schools to achieve strategic oversight. The current paper provides exemplars to illustrate how this future-ready strategic oversight could be implemented using an artificial intelligence-based Bayesian network software to analyze the data from five dissimilar AI-ALS, each deployed in a different school. Besides using descriptive analytics to reveal potential issues experienced by students within each AI-ALS, this human-centric AI-empowered approach also enables explainable predictive analytics of the students’ learning outcomes in paper-based summative assessments after training is completed in each AI-ALS. View Full-Text
Keywords: future-ready; strategic oversight; artificial superintelligence; artificial intelligence; forecasting AI behavior; predictive optimization; simulations; Bayesian networks; adaptive learning systems; pedagogical motif; explainable AI; AI Thinking; human-in-the-loop; human-centric reasoning; policy making on AI future-ready; strategic oversight; artificial superintelligence; artificial intelligence; forecasting AI behavior; predictive optimization; simulations; Bayesian networks; adaptive learning systems; pedagogical motif; explainable AI; AI Thinking; human-in-the-loop; human-centric reasoning; policy making on AI
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HOW, M.-L. Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes. Big Data Cogn. Comput. 2019, 3, 46.

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  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.6084/m9.figshare.8206976
    Link: https://doi.org/10.6084/m9.figshare.8206976
    Description: Datasets of the five artificial intelligence-enabled adaptive learning systems, as well as the students' pre-test and post-test scores
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