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

Chinnapat Charoenrat

Authors

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

Keywords:

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

Abstract

This study aimed to develop and evaluate an 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.

Downloads

Download data is not yet available.

References

Johnson, L., Becker, S. A., Cummins, M., Estrada, V., & Freeman, A. (2022). The NMC Horizon report: 2022 higher education edition. EDUCAUSE.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Siemens, G., & Long, P. (2021). Learning analytics and adaptive learning. EDUCAUSE Review, 56(1), 23–34.

Calvo, R. A., & D’Mello, S. K. (2022). Affective computing and intelligent interaction in education. MIT Press.

Woolf, B. P., Arroyo, I., & Cooper, D. G. (2021). Affective learning companions: Strategies for empathetic agents. International Journal of Artificial Intelligence in Education, 31(2), 245–270. https://doi.org/10.1007/s40593-020-00227-3

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Lindsay, S. (2022). Ethics of AI in education: Towards a community-wide framework. British Journal of Educational Technology, 53(4), 675–692. https://doi.org/10.1111/bjet.13191

Shen, J., Li, Y., & Yang, Q. (2023). Explainable artificial intelligence for education: A systematic review. Computers & Education, 193, 104662. https://doi.org/10.1016/j.compedu.2022.104662

Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2019). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. International Conference on Information Systems (ICIS), 1–17.

Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2020). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376447

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995

Luckin, R. (2021). The implications of artificial intelligence for education. Oxford Review of Education, 47(5), 584–602. https://doi.org/10.1080/03054985.2021.1916900

D’Mello, S., & Graesser, A. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford Handbook of Affective Computing (pp. 419–434). Oxford University Press.

Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38.

World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278.

https://doi.org/10.1109/ACCESS.2020.2988510

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman.

Calvo, R. A., & D’Mello, S. (2019). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 10(1), 18–37.

https://doi.org/10.1109/TAFFC.2017.2740923

Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120.

Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16–25.

OECD. (2018). OECD learning framework 2030: The future of education and skills. Paris: OECD Publishing.

Dyer, J., Gregersen, H. B., & Christensen, C. M. (2011). The innovator’s DNA: Mastering the five skills of disruptive innovators. Boston, MA: Harvard Business Review Press.

Wu, Y., Wu, B., Chen, N. S., & Gao, M. (2022). Human-in-the-loop machine learning in education: A review and perspectives. Computers and Education: Artificial Intelligence, 3, 100066.

Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York, NY: Longmans, Green.