arXiv AI September 17, 2026 By Lydia Manikonda, Mei Si, Sirajam Munira, Oshani Seneviratne, Kristin Bennett

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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.

By Shahin Hossain

llms

arXiv AI

Jun 16

Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build

arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?

By Sina Rismanchian, Hasan Uzun, Jeffrey Matayoshi, Eric Cosyn, Eyad Kurd-Misto

llms

arXiv AI

Jul 20

A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation

arXiv:2505. 00100v2 Announce Type: replace-cross Abstract: Background and Context.

By Ethan Dickey, Andres Bejarano, Rhianna Kuperus, B\'arbara Fagundes

llms

arXiv AI

Jul 29

Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course

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 AI

Aug 25

A Survey Instrument to Assess Students' AI and Generative AI Knowledge

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),...

By Aditya Johri, Cory Brozina, Akriti Bagale

arXiv AI

Jul 17

Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)

arXiv:2607. 14301v1 Announce Type: new Abstract: As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity.

By Shahin Hossain, Tukhbita Afroz Nawmi

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Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University

Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build

A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation

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Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)