Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-05-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/9/2/110 |
| _version_ | 1798025387048960000 |
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| author | Meng-Leong HOW Wei Loong David HUNG |
| author_facet | Meng-Leong HOW Wei Loong David HUNG |
| author_sort | Meng-Leong HOW |
| collection | DOAJ |
| description | Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students. |
| first_indexed | 2024-04-11T18:18:00Z |
| format | Article |
| id | doaj.art-c21130fe568b4953b4cec10d0f29cc46 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-04-11T18:18:00Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-c21130fe568b4953b4cec10d0f29cc462022-12-22T04:09:51ZengMDPI AGEducation Sciences2227-71022019-05-019211010.3390/educsci9020110educsci9020110Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive SimulationsMeng-Leong HOW0Wei Loong David HUNG1National Institute of Education, Nanyang Technological University Singapore, Singapore 639798, SingaporeNational Institute of Education, Nanyang Technological University Singapore, Singapore 639798, SingaporeArtificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.https://www.mdpi.com/2227-7102/9/2/110evaluation of artificial intelligence educational systemsintelligent adaptive learningintelligent tutoring systemsBayesiannonparametric data |
| spellingShingle | Meng-Leong HOW Wei Loong David HUNG Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations Education Sciences evaluation of artificial intelligence educational systems intelligent adaptive learning intelligent tutoring systems Bayesian nonparametric data |
| title | Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations |
| title_full | Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations |
| title_fullStr | Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations |
| title_full_unstemmed | Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations |
| title_short | Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations |
| title_sort | educational stakeholders independent evaluation of an artificial intelligence enabled adaptive learning system using bayesian network predictive simulations |
| topic | evaluation of artificial intelligence educational systems intelligent adaptive learning intelligent tutoring systems Bayesian nonparametric data |
| url | https://www.mdpi.com/2227-7102/9/2/110 |
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