Teaching Reform of a Data Structures Course Oriented Toward the Cultivation of Complex Engineering Problem-Solving Ability

Abstract

The Data Structures course is a core foundational component in the curriculum system of computer science education, characterized by high levels of abstraction, strong logical interconnections, and intensive practical requirements. To address the limitations of traditional teaching—such as the insufficient integration between theoretical instruction and programming practice, weak knowledge transfer ability among students, and inadequate support for formative assessment—this paper proposes a teaching reform framework for the Data Structures course aimed at cultivating the ability to solve complex engineering problems with the assistance of artificial intelligence. The proposed framework is structured around progressive competency development, supported by modular reconstruction of teaching content. It adopts a case-driven approach, hierarchical task design, and integrated practical training as its primary implementation pathways. Artificial intelligence technologies are embedded into multiple stages of the teaching process, including pre-class diagnostics, in-class support, programming practice, and learning feedback. As a result, a four-stage progressive teaching system is established, encompassing “conceptual understanding – structural implementation – algorithm analysis – comprehensive application.”