arXiv AI

A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

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 AI
Sep 21

Reducing Barriers to Academic Support: Evaluating a Course-Specific RAG System for Addressing Help-Seeking Disparities in Higher Education

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.

By Andy Gray, Jake Hobbs
arXiv AI
Aug 21

A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors

arXiv:2606. 12428v2 Announce Type: replace-cross Abstract: In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creating a historic record at a time of great change in this area.

By Felix Muzny, Carolyn Jones, Carter Ithier, Hasnain Sikora, Hrutika Harshadbhai Patel, Carla E. Brodley
arXiv AI
Sep 17

The Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance, Evaluation Literacy, and Course Policy in AI-related Courses

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.

By Lydia Manikonda, Mei Si, Sirajam Munira, Oshani Seneviratne, Kristin Bennett