Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes
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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/3/3/46 |
| _version_ | 1819131147009916928 |
|---|---|
| author | Meng-Leong HOW |
| author_facet | Meng-Leong HOW |
| author_sort | Meng-Leong HOW |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T09:10:53Z |
| format | Article |
| id | doaj.art-4ed26a7faf434915b6a675cb84b46374 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-12-22T09:10:53Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-4ed26a7faf434915b6a675cb84b463742022-12-21T18:31:26ZengMDPI AGBig Data and Cognitive Computing2504-22892019-07-01334610.3390/bdcc3030046bdcc3030046Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational OutcomesMeng-Leong HOW0National Institute of Education, Nanyang Technological University Singapore, Singapore 639798, SingaporeArtificial 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.https://www.mdpi.com/2504-2289/3/3/46future-readystrategic oversightartificial superintelligenceartificial intelligenceforecasting AI behaviorpredictive optimizationsimulationsBayesian networksadaptive learning systemspedagogical motifexplainable AIAI Thinkinghuman-in-the-loophuman-centric reasoningpolicy making on AI |
| spellingShingle | Meng-Leong HOW 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 and Cognitive Computing 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 |
| title | Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes |
| title_full | Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes |
| title_fullStr | Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes |
| title_full_unstemmed | Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes |
| title_short | Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes |
| title_sort | future ready strategic oversight of multiple artificial superintelligence enabled adaptive learning systems via human centric explainable ai empowered predictive optimizations of educational outcomes |
| topic | 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 |
| url | https://www.mdpi.com/2504-2289/3/3/46 |
| work_keys_str_mv | AT mengleonghow futurereadystrategicoversightofmultipleartificialsuperintelligenceenabledadaptivelearningsystemsviahumancentricexplainableaiempoweredpredictiveoptimizationsofeducationaloutcomes |