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

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

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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 suede-chinks-with-basketweave-accents 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 Coffee Supplies 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.