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:2608.30756v1 Announce Type: cross
Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their...
By Annemarie Wittig, Alina Mailach, Janet Siegmund, Norbert Siegmund
arXiv:2606. 30662v1 Announce Type: cross Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption.
By Lorenzo Stacchio, Michele Giordano, Daniele Berardini, Primo Zingaretti, Emanuele Frontoni
The paper introduces CourseChat, an on‑premises, multi‑course retrieval‑augmented generation (RAG) tutor designed for undergraduate business education. It runs behind a campus web gateway, with each of six courses identified by a unique course reference number (CRN) sharing dual AI hosts that provide a FastAPI service, a local vector database, and a local large language model (LLM) served by Ollama. The authors evaluate different model sizes, noting that larger models failed speed requirements while a 12B and 7B model met the classroom speed gate; a mixture‑of‑experts variant improved some corrections but introduced new errors, leading them to retain an 8B model for production pending further improvement. The study highlights that model selection, evidence sourcing, serving compatibility, and product design must be considered together, though it does not demonstrate learning gains and calls for separate evaluation of faculty ratings, peak‑load capacity, and public‑gateway acceptance.
By Sidney Shapiro, Joshua Lindemann
arXiv:2606. 28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them.
By Shahin Hossain
arXiv:2606. 17887v1 Announce Type: cross Abstract: Generative AI (GenAI) deployment in the workplace is accelerating rapidly.
By Dalia Ali, Maria Jos\'e Rodr\'iguez Vel\'azquez, Manoel Horta Ribeiro, Vera Liao, Orestis Papakyriakopoulos
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.
By Amrita Ganguly, Aditya Johri, Nora McDonald, Areej Ali, Umama Dewan, Aayushi Hingle Collier
arXiv:2607. 08748v1 Announce Type: new Abstract: In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education.
By Kristina Schaaff, Quintus Stierstorfer, Valerie Heckel
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.
By Marina Lepp
arXiv:2606. 15766v1 Announce Type: new Abstract: A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution.
By Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer, Rhodri Nelson, Peter B. Johnson
arXiv:2603. 04982v3 Announce Type: replace-cross Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings?
By Benjamin M. Chen, Hong Bao
arXiv:2606. 01375v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly entering students' learning practices, but their educational value depends on whether they support reasoning or enable task completion without engagement.
By Mohammad Amanlou, Yasaman Amou-Jafari, Mehrad Livian, Fatemeh Boloukazari, Fereshte Bagheri, Behnam Bahrak