arXiv AI
Accessible, but Not Adopted: Increasing LLM Adoption among First-generation, Low-income (FGLI) College Students beyond Expanding Access
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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.
arXiv AI
Sep 1
On the Prospects of Dynamic LLM Conversations in Software Development
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...
arXiv AI
Jul 1
ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education
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.
arXiv AI
Oct 5
On-Premises Multi-Course RAG Tutoring for Business Education: Hardware-Software Trade-offs in a Campus AI Tutor
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.
arXiv AI
Jun 30
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
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.
arXiv AI
Jun 17
AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources
arXiv:2606. 17887v1 Announce Type: cross Abstract: Generative AI (GenAI) deployment in the workplace is accelerating rapidly.