Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge
This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literatur...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/772 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589174335602688 |
|---|---|
| author | Eloy López-Meneses Luis López-Catalán Noelia Pelícano-Piris Pedro C. Mellado-Moreno |
| author_facet | Eloy López-Meneses Luis López-Catalán Noelia Pelícano-Piris Pedro C. Mellado-Moreno |
| author_sort | Eloy López-Meneses |
| collection | DOAJ |
| description | This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literature was conducted, analyzing 370 articles published between 2006 and 2024. The research examines how AI can support the identification of learning patterns and individual student needs. Through EDM, student data are analyzed to predict student performance and enable timely interventions. HITL-ML ensures that educators remain in control, allowing them to adjust the system according to their pedagogical goals and minimizing potential biases. Machine-assisted teaching allows AI processes to be structured around specific learning criteria, ensuring relevance to educational outcomes. The findings suggest that these AI applications can significantly improve personalized learning, student tracking, and resource optimization in educational institutions. The study highlights ethical considerations, such as the need to protect privacy, ensure the transparency of algorithms, and promote equity, to ensure inclusive and fair learning environments. Responsible implementation of these methods could significantly improve educational quality. |
| format | Article |
| id | doaj-art-31c3e3f05b8e4174b68c95600cea4e8b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-31c3e3f05b8e4174b68c95600cea4e8b2025-01-24T13:20:46ZengMDPI AGApplied Sciences2076-34172025-01-0115277210.3390/app15020772Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific KnowledgeEloy López-Meneses0Luis López-Catalán1Noelia Pelícano-Piris2Pedro C. Mellado-Moreno3Department of Education and Social Psychology, Pablo de Olavide University, 41013 Sevilla, SpainDepartment of Education and Social Psychology, Pablo de Olavide University, 41013 Sevilla, SpainFaculty of Education, International University of La Rioja, Av. de la Paz, 137, 26006 Logroño, SpainDepartment of Education Sciences, Language, Culture and Arts, Rey Juan Carlos University, Paseo Artilleros s/n, 28032 Madrid, SpainThis study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literature was conducted, analyzing 370 articles published between 2006 and 2024. The research examines how AI can support the identification of learning patterns and individual student needs. Through EDM, student data are analyzed to predict student performance and enable timely interventions. HITL-ML ensures that educators remain in control, allowing them to adjust the system according to their pedagogical goals and minimizing potential biases. Machine-assisted teaching allows AI processes to be structured around specific learning criteria, ensuring relevance to educational outcomes. The findings suggest that these AI applications can significantly improve personalized learning, student tracking, and resource optimization in educational institutions. The study highlights ethical considerations, such as the need to protect privacy, ensure the transparency of algorithms, and promote equity, to ensure inclusive and fair learning environments. Responsible implementation of these methods could significantly improve educational quality.https://www.mdpi.com/2076-3417/15/2/772artificial intelligenceeducational data miningmachine learningmachine-assisted teachingscientific production |
| spellingShingle | Eloy López-Meneses Luis López-Catalán Noelia Pelícano-Piris Pedro C. Mellado-Moreno Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge Applied Sciences artificial intelligence educational data mining machine learning machine-assisted teaching scientific production |
| title | Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge |
| title_full | Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge |
| title_fullStr | Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge |
| title_full_unstemmed | Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge |
| title_short | Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge |
| title_sort | artificial intelligence in educational data mining and human in the loop machine learning and machine teaching analysis of scientific knowledge |
| topic | artificial intelligence educational data mining machine learning machine-assisted teaching scientific production |
| url | https://www.mdpi.com/2076-3417/15/2/772 |
| work_keys_str_mv | AT eloylopezmeneses artificialintelligenceineducationaldataminingandhumanintheloopmachinelearningandmachineteachinganalysisofscientificknowledge AT luislopezcatalan artificialintelligenceineducationaldataminingandhumanintheloopmachinelearningandmachineteachinganalysisofscientificknowledge AT noeliapelicanopiris artificialintelligenceineducationaldataminingandhumanintheloopmachinelearningandmachineteachinganalysisofscientificknowledge AT pedrocmelladomoreno artificialintelligenceineducationaldataminingandhumanintheloopmachinelearningandmachineteachinganalysisofscientificknowledge |