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Affective and Explainable Artificial Intelligence-Driven Human-in-the-Loop Adaptive Learning Model to Enhance Cognitive and Innovation Competencies |
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| รหัสดีโอไอ | |
| Creator | Chinnapat Charoenrat |
| Title | Affective and Explainable Artificial Intelligence-Driven Human-in-the-Loop Adaptive Learning Model to Enhance Cognitive and Innovation Competencies |
| Publisher | Faculty of Industrial Education, King Mongkut’s University of Technology North Bangkok |
| Publication Year | 2568 |
| Journal Title | Journal of Research and Innovation in Industrial Education |
| Journal Vol. | 1 |
| Journal No. | 3 |
| Page no. | 40-55 |
| Keyword | Affective Artificial Intelligence, Explainable Artificial Intelligence, Human-in-the-Loop, Adaptive Learning, Cognitive Competency, Innovation Competency |
| URL Website | https://so14.tci-thaijo.org/index.php/jriie |
| Website title | Journal of Research and Innovation in Industrial Education |
| ISSN | ISSN 3088-1455 (Online) |
| Abstract | This study aimed to develop and evaluate an Artificial Intelligence (AI)-driven adaptive learning model that integrates Affective AI (AAI), Explainable AI (XAI), and Human-in-the-Loop (HITL) to enhance participants’ competencies in critical thinking and innovation. The research was conducted in three phases: (1) reviewing relevant concepts, theories, and empirical studies to determine the components of the adaptive learning model; (2) developing the model and validating its appropriateness through expert review; and (3) implementing the model with a sample of 30 professional development participants. Data were collected using standardized instruments measuring critical thinking and innovation competencies, and were analyzed using Paired Samples t-test and Repeated Measures ANOVA. The findings revealed that the developed adaptive learning model demonstrated a high level of appropriateness, both in terms of comprehensiveness of its components and practical feasibility. Furthermore, participants’ post-test mean scores in critical thinking and innovation competencies were significantly higher than their pre-test scores (p < 0.001). This indicates that the model effectively enhanced personalized, transparent, and learner-centered processes. In conclusion, the integration of Affective AI, Explainable AI, and Human-in-the-Loop shows strong potential in establishing adaptive learning systems that foster key 21st-century competencies among professionals, with implications for both theoretical advancement and practical application. |
