🤖 AI Summary
This study addresses the misalignment between traditional software engineering education—still reliant on manual coding assessments—and contemporary industry practices shaped by the widespread adoption of large language models (LLMs). To bridge this gap, the authors propose a theoretical framework for LLM-era software engineering education that shifts the focus from code production to critical validation, human-AI collaboration governance, and process transparency. The framework introduces a novel pedagogical model integrating LLMs into curricula, specifically designed for high-control, large-enrollment, exam-oriented educational contexts such as computer engineering programs in Turkey. It redefines academic integrity by prioritizing transparent development processes over conventional plagiarism detection and offers a foundational theory for curriculum reform, while calling for longitudinal empirical studies to validate its efficacy.
📝 Abstract
The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into professional workflows is increasingly reshaping software engineering practices. These tools have lowered the cost of code generation, explanation, and testing, while introducing new forms of automation into routine development tasks. In contrast, most of the software engineering and computer engineering curricula remain closely aligned with pedagogical models that equate manual syntax production with technical competence. This growing misalignment raises concerns regarding assessment validity, learning outcomes, and the development of foundational skills. Adopting a conceptual research approach, this paper proposes a theoretical framework for analyzing how generative AI alters core software engineering competencies and introduces a pedagogical design model for LLM-integrated education. Attention is given to computer engineering programs in Turkey, where centralized regulation, large class sizes, and exam-oriented assessment practices amplify these challenges. The framework delineates how problem analysis, design, implementation, and testing increasingly shift from construction toward critique, validation, and human-AI stewardship. In addition, the paper argues that traditional plagiarism-centric integrity mechanisms are becoming insufficient, motivating a transition toward a process transparency model. While this work provides a structured proposal for curriculum adaptation, it remains a theoretical contribution; the paper concludes by outlining the need for longitudinal empirical studies to evaluate these interventions and their long-term impacts on learning.