Beyond Correctness: A Competency-Driven Framework for Designing Autograder Test Suites

Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis

This work analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering, and characterized courses'learning objectives, assessments, topics, and documented AI tools.

Comparative Evaluation of Large Language Models in Computer Programming Education

A comparative analysis of six LLMs for generating formative feedback on introductory Java programs containing predefined defects under controlled conditions reveals substantial cross-model variation, particularly in multi-defect scenarios.

CodeOwl: Automatic Generation of Tiered Parsons Problems for Introductory Programming

This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems automatically, and evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies.

Software Engineer Competency Framework in the Era of Generative AI: A Literature Review

Generative artificial intelligence (GenAI) technologies such as Claude Code, ChatGPT, and GitHub Copilot are fundamentally reshaping software development practices, shifting the core activities of software engineers from direct code authoring toward validation, orchestration, and architectural reasoning. This paradigm shift raises fundamental questions: what competencies do software engineers require to collaborate effectively alongside GenAI, and how do these requirements vary across career stages? A focused literature review informed by the Systematic Literature Review principles of Kitchenham & Charters (2007) and the mapping study guidelines of Petersen et al. (2015) was conducted to address these questions. Findings were synthesized into a three-pillar competency model: foundational technical competencies augmented by AI tool literacy and prompt engineering; cognitive-analytical skills characterized by intensified critical code review, systems thinking, and AI-generated logic verification; and meta-skills encompassing AI governance, ethical judgment, and continuous adaptability, all of which exhibit significant differentiation across junior, mid-level, and senior software engineers. Furthermore, this study identifies a critical research gap: competency evolution at the senior engineering tier remains substantially under-researched compared to junior and mid-level stages. These findings offer practical implications for software organizations and educational institutions in redesigning competency development pathways in the GenAI era

Students' Practices and Skills in the LLM-Era: "You Can't Outsource the Struggle and Still Get the Skill"

Generative AI tools have been rapidly learned in the daily workflow of graduate students in Software Engineering, but little is known about what AI-related skills they actually need for effective use in empirical research. Without this understanding, graduate programs cannot prepare students to conduct rig-orous research in the LLM era, risking creating a generation of researchers who delegate tasks without the necessary expertise. By analyzing 1,383 posts from five research-focused subreddits, we found that students systematically outsource the cognitive effort required to develop research skills and end up with neither the expected results nor the necessary competence. Naming these missing skills is the first step toward curricula that teach graduate students to work \emph{with} LLMs without being replaced by them.

From Repositories to Practice: LLM-Based Personalization in Programming Education