Affective and Explainable AI-Driven Human-in-the-Loop Adaptive Learning Model to Enhance Cognitive and Innovation Competencies of Professional Development Learners

Chinnapat Charoenrat

ผู้แต่ง

Chinnapat Charoenrat Faculty of Industrial Education, King Mongkut’s University of Technology North Bangkok

คำสำคัญ:

Affective Artificial Intelligence, Explainable Artificial Intelligence, Human-in-the-Loop, Adaptive Learning, Cognitive Competency, Innovation Competency

บทคัดย่อ

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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