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How Students Use Generative AI for Software Testing: An Observational Study
Baris Ardic, Quentin Le Dilavrec, Andy Zaidman
https://arxiv.org/abs/2510.10551 https://

How Students Use Generative AI for Software Testing: An Observational Study
The integration of generative AI tools like ChatGPT into software engineering workflows opens up new opportunities to boost productivity in tasks such as unit test engineering. However, these AI-assisted workflows can also significantly alter the developer's role, raising concerns about control, output quality, and learning, particularly for novice developers. This study investigates how novice software developers with foundational knowledge in software testing interact with generative AI for e…

What Generative Search Engines Like and How to Optimize Web Content Cooperatively
Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong
https://arxiv.org/abs/2510.11438 https://

What Generative Search Engines Like and How to Optimize Web Content Cooperatively
By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them. In this paper, we introduce AutoGEO, a framework to automatically learn generative engine …

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e
https://arxiv.org/abs/2510.11269 https://…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Generative AI (GenAI) chatbots are now pervasive in digital ecosystems, yet their network traffic remains largely underexplored. This study presents an in-depth investigation of traffic generated by three leading chatbots (ChatGPT, Copilot, and Gemini) when accessed via Android mobile apps for both text and image generation. Using a dedicated capture architecture, we collect and label two complementary workloads: a 60-hour generic dataset with unconstrained prompts, and a controlled dataset bui…

An analysis of over 1M words of conversation between a ChatGPT user and the generative AI chatbot shows how ordinarily rational people can spiral into delusion (New York Times)
https://www.

Chatbots Can Go Into a Delusional Spiral. Here’s How It Happens.
Over 21 days of talking with ChatGPT, an otherwise perfectly sane man became convinced that he was a real-life superhero. We analyzed the conversation.

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

"So if ChatGPT destroys literacy and law, then ChatGPT is going to kill God. By this I don’t mean that the bearded guy in the sky is going to be found dead of a gunshot wound to the chest, but that monotheistic religion as we have traditionally understood it will not be able to function in a post-literacy regime."
(Original title: ChatGPT is going to kill God)

ChatGPT is going to kill God
I hate generative AI. I hate how it’s destroying writing pedagogy and giving students even more excuses not to read (because they can just read a “summary”). I hate how whiny and …

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
Cheonsu Jeong, Seunghyun Lee, Sunny Jeong, Sungsu Kim
https://arxiv.org/abs/2509.00398 ht…

A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
This study provides an in_depth analysis of the ethical and trustworthiness challenges emerging alongside the rapid advancement of generative artificial intelligence (AI) technologies and proposes a comprehensive framework for their systematic evaluation. While generative AI, such as ChatGPT, demonstrates remarkable innovative potential, it simultaneously raises ethical and social concerns, including bias, harmfulness, copyright infringement, privacy violations, and hallucination. Current AI ev…

Generative Engine Optimization: How to Dominate AI Search
Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas
https://arxiv.org/abs/2509.08919 https://arxiv…

Generative Engine Optimization: How to Dominate AI Search
The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lists to synthesized, citation-backed answers. This shift challenges established Search Engine Optimization (SEO) practices and necessitates a new paradigm, which we term Generative Engine Optimization (GEO). This paper presents a comprehensive comparative analysis of AI Search and traditional web search (Google). Thro…

Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice
Yongsu Ahn, Nam Wook Kim
https://arxiv.org/abs/2508.01547 https://arxiv.org/pdf…

Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice
This paper investigates why recent generative AI models outperform humans in data visualization knowledge tasks. Through systematic comparative analysis of responses to visualization questions, we find that differences exist between two ChatGPT models and human outputs over rhetorical structure, knowledge breadth, and perceptual quality. Our findings reveal that ChatGPT-4, as a more advanced model, displays a hybrid of characteristics from both humans and ChatGPT-3.5. The two models were genera…

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
Jiayu Zheng, Lingxin Hao, Kelun Lu, Ashi Garg, Mike Reese, Melo-Jean Yap, I-Jeng Wang, Xingyun Wu, Wenrui Huang, Jenna Hoffman, Ariane Kelly, My Le, Ryan Zhang, Yanyu Lin, Muhammad Faayez, Anqi Liu
https://arxiv.org/abs/2508.20244

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing A…

Good article, regardless your opinion about GenAI , it will change society.
#ai

The Third Algorithm
ChatGPT and other generative AI isn't about to create superintelligence, but even if what we see now is it, the impact is on the scale of Google search and algorithmic social media, except maybe worse

We have no idea how this #AI cycle is affecting jobs. The numbers are all over the place.
Relatedly, I've found myself regularly explaining the concept of an "AI winter" and that most academics expect that we're coming up on the next one. Nobody is connecting these two concepts because they're both so…

OpenAI launches study mode in ChatGPT, designed to encourage students to engage with learning material, available to logged-in Free, Plus, Pro, and Team users (Reece Rogers/Wired)
https://www.wired.com/story/chatgpt-study-mode/

ChatGPT’s Study Mode Is Here. It Won’t Fix Education’s AI Problems
OpenAI’s new study mode for ChatGPT throws questions back at students, but the learning feature doesn’t address generative AI’s underlying disruption of education.

Generative AI in Higher Education: Evidence from an Elite College
Zara Contractor, Germ\'an Reyes
https://arxiv.org/abs/2508.00717 https://arxiv.org/pd…

Generative AI in Higher Education: Evidence from an Elite College
Generative AI is transforming higher education, yet systematic evidence on student adoption remains limited. Using novel survey data from a selective U.S. college, we document over 80 percent of students using AI academically within two years of ChatGPT's release. Adoption varies across disciplines, demographics, and achievement levels, highlighting AI's potential to reshape educational inequalities. Students predominantly use AI for augmenting learning (e.g., explanations, feedback), but also …

Can We Tell if ChatGPT is a Parasite? Studying Human-AI Symbiosis with Game Theory
Jiejun Hu-Bolz, James Stovold
https://arxiv.org/abs/2508.11359 https://a…

Can We Tell if ChatGPT is a Parasite? Studying Human-AI Symbiosis with Game Theory
This work asks whether a human interacting with a generative AI system can merge into a single individual through iterative, information-driven interactions. We model the interactions between a human, a generative AI system, and the human's wider environment as a three-player stochastic game. We use information-theoretic measures (entropy, mutual information, and transfer entropy) to show that our modelled human and generative AI are able to form an aggregate individual in the sense of Krakauer…

Hallucinating with AI: AI Psychosis as Distributed Delusions
Lucy Osler
https://arxiv.org/abs/2508.19588 https://arxiv.org/pdf/2508.19588

Hallucinating with AI: AI Psychosis as Distributed Delusions
There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed AI hallucinations. However, deeming these AI outputs hallucinations is controversial, with many claiming this is a metaphorical misnomer. Nevertheless, in this paper, I argue that when viewed through the lens of distributed cognition theory, we can better see the dynamic and troubling ways in which inaccurate beliefs,…

From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?
Thomas Serre, Ellie Pavlick
https://arxiv.org/abs/2509.17280 https://a…

From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?
Generative pretraining (the "GPT" in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range of tasks within and across domains, and these models are increasingly applied beyond language to the brain sciences. These …

How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
Golo Henseke, Rhys Davies, Alan Felstead, Duncan Gallie, Francis Green, Ying Zhou
https://arxiv.org/abs/2507.22748

How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
We introduce the Generative AI Susceptibility Index (GAISI), a task-based measure of UK job exposure to large language models (LLMs), such as ChatGPT. GAISI is derived from probabilistic task ratings by LLMs and linked to worker-reported task data from the Skills and Employment Surveys. It reflects the share of job activities where an LLM or LLM-powered system can reduce task completion time by at least 25 per cent beyond existing productivity tools. The index demonstrates high reliability, str…

Culling Misinformation from Gen AI: Toward Ethical Curation and Refinement
Prerana Khatiwada, Grace Donaher, Jasymyn Navarro, Lokesh Bhatta
https://arxiv.org/abs/2507.14242

Culling Misinformation from Gen AI: Toward Ethical Curation and Refinement
While Artificial Intelligence (AI) is not a new field, recent developments, especially with the release of generative tools like ChatGPT, have brought it to the forefront of the minds of industry workers and academic folk alike. There is currently much talk about AI and its ability to reshape many everyday processes as we know them through automation. It also allows users to expand their ideas by suggesting things they may not have thought of on their own and provides easier access to informati…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
Surajit Das, Aleksei Eliseev
https://arxiv.org/abs/2508.12013 https://ar…

Predicting ChatGPT Use in Assignments: Implications for AI-Aware Assessment Design
The rise of generative AI tools like ChatGPT has significantly reshaped education, sparking debates about their impact on learning outcomes and academic integrity. While prior research highlights opportunities and risks, there remains a lack of quantitative analysis of student behavior when completing assignments. Understanding how these tools influence real-world academic practices, particularly assignment preparation, is a pressing and timely research priority. This study addresses this gap…

Sociotechnical Imaginaries of ChatGPT in Higher Education: The Evolving Media Discourse
Yinan Sun, Ali Unlu, Aditya Johri
https://arxiv.org/abs/2508.14692 https://

Sociotechnical Imaginaries of ChatGPT in Higher Education: The Evolving Media Discourse
This study investigates how U.S. news media framed the use of ChatGPT in higher education from November 2022 to October 2024. Employing Framing Theory and combining temporal and sentiment analysis of 198 news articles, we trace the evolving narratives surrounding generative AI. We found that the media discourse largely centered on institutional responses; policy changes and teaching practices showed the most consistent presence and positive sentiment over time. Conversely, coverage of topics su…

The Provenance Problem: LLMs and the Breakdown of Citation Norms
Brian D. Earp, Haotian Yuan, Julian Koplin, Sebastian Porsdam Mann
https://arxiv.org/abs/2509.13365 https://

The Provenance Problem: LLMs and the Breakdown of Citation Norms
The increasing use of generative AI in scientific writing raises urgent questions about attribution and intellectual credit. When a researcher employs ChatGPT to draft a manuscript, the resulting text may echo ideas from sources the author has never encountered. If an AI system reproduces insights from, for example, an obscure 1975 paper without citation, does this constitute plagiarism? We argue that such cases exemplify the 'provenance problem': a systematic breakdown in the chain of scholarl…

Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study
Benjamin J. Walker, Beatriz Navarro Lameda, Ruth A. Reynolds
https://arxiv.org/abs/2509.13359

Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study
Generative artificial intelligence (GenAI) tools such as OpenAI's ChatGPT are transforming the educational landscape, prompting reconsideration of traditional assessment practices. In parallel, universities are exploring alternatives to in-person, closed-book examinations, raising concerns about academic integrity and pedagogical alignment in uninvigilated settings. This study investigates whether traditional closed-book mathematics examinations retain their pedagogical relevance when hypotheti…