arXiv:2607. 24755v1 Announce Type: cross Abstract: This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses.
arXiv:2608. 16318v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code.
arXiv:2511. 13271v2 Announce Type: replace-cross Abstract: The rise of Generative AI (GenAI) tools like ChatGPT has created new opportunities and challenges for computing education.
The study examined how different designs of AI teaching assistants (AI TAs) affect students in an introductory programming course. Four AI TAs were compared based on pedagogical style (Socratic vs. Direct instruction) and context awareness (no context vs. full context). Results showed that the Socratic AI TA with full context received the lowest favorability ratings, had the highest interaction stress, the most external LLM use, and the lowest comprehension outcomes, though differences were not statistically significant.
arXiv:2505. 00100v2 Announce Type: replace-cross Abstract: Background and Context.
arXiv:2604. 01114v3 Announce Type: replace-cross Abstract: As generative AI systems are integrated into educational settings, students often encounter AI-generated output while working through learning tasks, either by requesting help or through integrated tools.
The paper introduces Beacon, a course‑specific Retrieval‑Augmented Generation (RAG) system that offers private, module‑aligned academic support to students. By grounding responses in approved teaching materials, Beacon aims to lower barriers to help‑seeking and encourage independent learning, especially in computing education where tasks are cumulative and demanding. Evaluation through questionnaires and interviews showed students found Beacon’s responses trustworthy and closely aligned with course content, viewing it as a useful first point of support before consulting lecturers or official resources.
arXiv:2606. 12424v1 Announce Type: cross Abstract: As generative AI and low-code workflow platforms become routine in software practice, a key educational question is whether the next generation of computer engineers will accept these tools as useful, usable, and worthy of sustained engagement.
arXiv:2608.21391v1 Announce Type: cross Abstract: In this research-to-practice paper we present a survey that can be used to assess students' AI knowledge. As the use of artificial intelligence (AI),...
The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.
arXiv:2606. 03288v1 Announce Type: cross Abstract: Introductory programming (CS1) courses often struggle to support students' understanding of program execution.
arXiv:2607. 12296v1 Announce Type: cross Abstract: With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions.
arXiv:2607. 00211v1 Announce Type: new Abstract: Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies.