Manuscript received April 30, 2025; accepted September 9, 2025; published April 17, 2026.

Abstract—Aiming at the problems of insufficient task suitability and a single evaluation dimension in personalized teaching, this paper proposes an Artificial Intelligence (AI)-empowered competency-oriented interdisciplinary split-track task-driven teaching framework. The framework takes the background of super-discipline integration education, uses AI empowerment and causal analysis to achieve precise matching of task competence and elimination of confounding variables, and realizes the goal of multidimensional competence enhancement through assessment and evaluation optimization. In the case of information security teaching implementation, the F-test results show that the variance of students’ competence is significantly reduced from 20.97 to 10.41, reflecting the effectiveness of the framework. The framework provides a new methodological paradigm and useful reference for AI-enabled personalized teaching.
 
Keywords—personalized teaching, causal analysis, Artificial Intelligence (AI)-enabled, split-track task design, competency-oriented

Cite: Yongxiang Yu*, Bin Duan, and Yao Xiao, "AI-Enabled Research on Competency-Oriented Interdisciplinary Split-Track Task-Driven Instruction," International Journal of Learning and Teaching, Vol. 12, No. 2, pp. 107-112, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).