Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.

This paper introduces a simple JavaScript-based web application designed to assist educators in detecting AI-generated content in student essays and written assignments. Unlike existing AI detection tools that rely on obfuscated machine learning models, AIDetection.info employs a heuristic-based approach to identify common syntactic traces left by generative AI models, such as ChatGPT, Claude, Grok, DeepSeek, Gemini, Llama/Meta, Microsoft Copilot, Grammarly AI, and other text-generating models and wrapper applications. The tool scans documents in bulk for potential AI artifacts, as well as AI citations and acknowledgments, and provides a visual summary with downloadable Excel and CSV reports. This article details its methodology, functionalities, limitations, and applications within educational settings.

Almost one in eight college students would use AI agents to help with school processes, and 83% of administrators would welcome AI agent support in their roles, according to Salesforce and YouGov research. Also: Employers want workers with AI skills, but what exactly does that mean? The survey of more than 500 college students and 200 administrators highlighted how higher education students are eager for AI assistance in admissions, campus support, coursework, and all other student-related activities. The campus experience must improve given that higher educational institutions face challenges, including fewer college-aged students. Today, one in four college students questions the value of their degree.

Existing power dynamics, social injustices and structural barriers may exacerbate challenges related to support and advocacy, limiting some students' ability to articulate their needs effectively [59]. This disparity highlights an increasing need for alternative approaches to student advocacy that may empower students with disabilities in ways that current practices may not. While human disability support practitioners can play a crucial role in bridging gaps between students and institutions, these efforts are resource-intensive, relying on trained personnel, availability, and sustained institutional commitment. This study explores the feasibility and ethical implications of employing artificial intelligence (AI) and in particular social robots as tools for mediation and advocacy for disabled students in higher education. While the overarching focus regards social robots and LLMs, the study adopts a broader perspective of understanding the use of technology and AI in general for disabled students, to draw insights and identify patterns that can inform the design, implementation, and ethical considerations of AI-driven assistive technologies.

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model, validated on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. This approach achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, our model distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support. The code is available at https://github.com/roycmeghna/I-HOPE.

Pronunciation instruction in foreign language classrooms has often been an overlooked area of focus. With the widespread adoption of Artificial Intelligence (AI) and its potential benefits, investigating how AI is utilized in pronunciation teaching and understanding the beliefs of teachers about this tool is essential for improving learning outcomes. This study aims to examine how AI use for pronunciation instruction varies across different demographic and professional factors among teachers, and how these factors, including AI use, influence the beliefs of teachers about AI. The study involved 117 English as a Foreign Language (EFL) in-service teachers working in Cyprus, who completed an online survey designed to assess their beliefs about the effectiveness of AI, its drawbacks, and their willingness to integrate AI into their teaching practices. The results revealed that teachers were significantly more likely to agree on the perceived effectiveness of AI and their willingness to adopt it, compared to their concerns about its use. Furthermore, teachers working in higher education and adult education, as well as those who had received more extensive training, reported using AI more frequently in their teaching. Teachers who utilized AI more often expressed stronger agreement with its effectiveness, while those who had received more training were less likely to express concerns about its integration. Given the limited training that many teachers currently receive, these findings demonstrate the need for tailored training sessions that address the specific needs and concerns of educators, ultimately fostering the adoption of AI in pronunciation instruction.

Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.

This innovative practice WIP paper describes a research study that explores the integration of ChatGPT into the software testing curriculum and evaluates its effectiveness compared to human-generated testing artifacts. In a Capstone Project course, students were tasked with generating preparatory testing artifacts using ChatGPT prompts, which they had previously created manually. Their understanding and the effectiveness of the Artificial Intelligence generated artifacts were assessed through targeted questions. The results, drawn from this in-class assignment at a North American community college indicate that while ChatGPT can automate many testing preparation tasks, it cannot fully replace human expertise. However, students, already familiar with Information Technology at the postgraduate level, found the integration of ChatGPT into their workflow to be straightforward. The study suggests that AI can be gradually introduced into software testing education to keep pace with technological advancements.

With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors for analyzing time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective performance evaluation method for TAD. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual point are inadequate. Existing evaluation methods for TAD typically employ point-based or event-based metrics to capture the temporal context. However, point-based metrics tend to overestimate detectors that excel only in detecting long anomalies, while event-based metrics are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR, a novel set of TAD evaluation metrics. It models the process of operators receiving detector alarms and handling faults, utilizing area under the operator interest curve to evaluate the performance of TAD algorithms. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluation methods. Through experiments conducted on the special scenario dataset and five real-world datasets, we demonstrate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations.

Polarization is defined as divisive opinions held by two or more groups on substantive issues. As the world's third-largest democracy, Indonesia faces growing concerns about the interplay between political polarization and online toxicity, which is often directed at vulnerable minority groups. Despite the importance of this issue, previous NLP research has not fully explored the relationship between toxicity and polarization. To bridge this gap, we present a novel multi-label Indonesian dataset that incorporates toxicity, polarization, and annotator demographic information. Benchmarking this dataset using BERT-base models and large language models (LLMs) shows that polarization information enhances toxicity classification, and vice versa. Furthermore, providing demographic information significantly improves the performance of polarization classification.