Affective and Explainable Artificial Intelligence-Driven Human-in-the-Loop Adaptive Learning Model to Enhance Cognitive and Innovation Competencies
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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.
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Charoenrat developed an affective and explainable AI-driven model for adaptive learning that considers learner emotional states alongside cognitive factors.
“Charoenrat developed an affective and explainable AI-driven model for adaptive learning that considers learner emotional states alongside cognitive factors [14].”
From Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications (2026) · cites this paper as [14]
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- Authors
- Charoenrat, C.
- Corresponding author
- Chinnapat Charoenrat
- Venue
- Faculty of Industrial Education, King Mongkut’s University of Technology North Bangkok
- Publisher
- Faculty of Industrial Education, King Mongkut’s University of Technology North Bangkok
- Published
- 2025
- ISSN
- 3088-1455 (ISSN-L)
- OpenAlex
- W7118535276
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- so14.tci-thaijo.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Research and Innovation in Industrial Education (publishedVersion, cc-by-nc-nd)
- NRCT Data Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- affective computing, explainable AI, human-in-the-loop learning, adaptive learning, cognitive skills, innovation competencies, personalized learning
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