Artificial Intelligence/Robots

“OpenAI’s New Image Generator Aims to Be Practical Enough for Designers and Advertisers”

Example images from OpenAI show progress here. The model is able to generate 12 discrete graphics within a single image—like a cat emoji or a lightning bolt—and place them in proper order. Another shows four cocktails accompanied by recipe cards with accurate, legible text. More images show comic strips with text bubbles, mock advertisements, and instructional diagrams. The model also allows you to upload images to be modified, and it will be available in the video generator Sora as well as in GPT-4o.

https://tinyurl.com/msnch7z5

“Evaluating AI Language Models for Reference Services: A Comparative Study of ChatGPT, Gemini, and Copilot”

The descriptive statistics indicate that Google Gemini outperformed the other GenAI chatbots, by scoring high on “accuracy,” relevancy,” “friendliness” and “instruction” resulting in a higher mean score followed by public ChatGPT, commercial ChatGPT-4.0, and Microsoft Copilot.

https://doi.org/10.1080/10875301.2025.2478861

“Scaffolding AI Literacy: An Instructional Model for Academic Librarianship”

As artificial intelligence (AI) becomes integral to academic, professional, and societal contexts, the demand for AI literacy in higher education is growing. Academic librarians, with their expertise in information literacy and critical pedagogy, are well-equipped to address this need. This article introduces a scaffolded model to advance AI literacy through progressive skill development across four tiers: foundational awareness, applied problem-solving, critical evaluation, and ethical advocacy. Each tier builds on the previous, fostering a comprehensive understanding of AI concepts, tools, and societal implications. Adapted from traditional information literacy workshops, this instructional model empowers students to navigate, critique, and responsibly engage with AI technologies. Tier 1 introduces basic AI concepts and tools. Tier 2 examines AI’s role in research and problem-solving, addressing practical applications and limitations. Tier 3 emphasizes the critical evaluation of AI-generated content and tools. Tier 4 focuses on ethical decision-making and advocacy, encouraging students to consider AI’s broader societal impacts. This article discusses the proposed model’s pedagogical design, details its application through workshop plans, and explores its implications for academic librarians’ roles in fostering AI literacy. By implementing this approach, librarians can equip students to become critical consumers of AI technologies.

https://doi.org/10.1016/j.acalib.2025.103041

“NVIDIA Announces DGX Spark and DGX Station Personal AI Computers”

DGX Spark — formerly Project DIGITS — and DGX Station™, a new high-performance NVIDIA Grace Blackwell desktop supercomputer powered by the NVIDIA Blackwell Ultra platform, enable AI developers, researchers, data scientists and students to prototype, fine-tune and inference large models on desktops. Users can run these models locally or deploy them on NVIDIA DGX Cloud or any other accelerated cloud or data center infrastructure. . . .

NVIDIA DGX Station brings data-center-level performance to desktops for AI development. The first desktop system to be built with the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, DGX Station features a massive 784GB of coherent memory space to accelerate large-scale training and inferencing workloads. The GB300 Desktop Superchip features an NVIDIA Blackwell Ultra GPU with latest-generation Tensor Cores and FP4 precision — connected to a high-performance NVIDIA Grace™ CPU via NVLink-C2C — delivering best-in-class system communication and performance.

https://tinyurl.com/2r66z523

Ars Technica reports that: “Since the systems will be manufactured by different companies, Nvidia did not mention pricing for the units. However, in January, Nvidia mentioned that the base-level configuration for a DGX Spark-like computer would retail for around $3,000.”

Paywall: “Bridging the AI Gap: Comparative Analysis of AI Integration, Education, and Outreach in Academic Libraries”

This study examines AI integration, education, and outreach in academic libraries across Europe, North America (Canada and USA), Sub-Saharan Africa, Latin America and the Caribbean. An environmental scan of 40 academic library websites from the Times Higher Education 10 highest-ranked libraries in each region was conducted. Results show that more than 50% of the libraries offered educational materials and 42.5% conducted educational activities, while only 12.5% included AI policies.

https://doi.org/10.1177/03400352251325274

“Elsevier Launches ScienceDirect AI to Transform Research with Rapid Mission-Critical Insights from Trusted Content”

Researchers grapple with an ever-growing and overwhelming volume of information and need to quickly get accurate insights they can rely on. Studies show that they spend 25%-35% of their time sifting through literature. ScienceDirect AI helps address this challenge by drawing on the broadest and deepest content set of millions of peer-reviewed full-text research articles and book chapters to generate instant accurate summaries and highlight key findings, while providing references to support reproducibility and integrity of research.

https://tinyurl.com/2s3m2hwp

“Can LLMs Categorize the Specialized Documents from Web Archives in a Better Way?”

The explosive growth of web archives presents a significant challenge: manually curating specialized document collections from this vast data. Existing approaches rely on supervised techniques, but recent advancements in Large Language Models (LLMs) offer new possibilities for automating collection creation. Large Language Models (LLMs) are demonstrating impressive performance on various tasks even without fine-tuning. This paper investigates the effectiveness of prompt design in achieving results comparable to fine-tuned models. We explore different prompting techniques for collecting specialized documents from web archives like UNT.edu, Michigan.gov, and Texas.gov. We then analyze the performance of LLMs under various prompt configurations. Our findings highlight the significant impact of incorporating task descriptions within prompts. Additionally, including the document type as justification for the search scope leads to demonstrably better results. This research suggests that well-crafted prompts can unlock the potential of LLMs for specialized tasks, potentially reducing reliance on resource-intensive fine-tuning. This research paves the way for automating specialized collection creation using LLMs and prompt engineering.

https://dl.acm.org/doi/10.1145/3677389.3702591

“AI Search Engines Cite Incorrect Sources at an Alarming 60% Rate, Study Says”

A new study from Columbia Journalism Review’s Tow Center for Digital Journalism finds serious accuracy issues with generative AI models used for news searches. The research tested eight AI-driven search tools equipped with live search functionality and discovered that the AI models incorrectly answered more than 60 percent of queries about news sources.

https://tinyurl.com/5ym7mc92

“Elsevier Launches Sciencedirect AI to Transform Research with Rapid Mission-Critical Insights from Trusted Content”

ScienceDirect AI includes the following features:

Ask ScienceDirect AI – search and summaries of full-text articles and book chapters

Users can search and get answers from within the full-text of 14 million articles and book chapters, using their own words to describe what they need and why. ScienceDirect AI will search across the millions of documents in its index to provide a Summary Response with references, Source Snippets for each reference, and short Related Insights summaries while linking back to the original document.

Reading Assistant – chat with a document in ScienceDirect

This conversational feature answers questions about the content of a specific full-text article or book chapter and allows researchers to ask further questions of the document. Users can click on references within the summaries to jump to locations in the article where the answer comes from, it also suggests research questions.

Compare Experiments – experiment summary table

Comparing and synthesizing literature can be very time-consuming. ScienceDirect AI’s unique Compare Experiments tool takes a set of articles and creates a table breaking down each experiment within them, drawing out the key aspects of each including goals, methods and results.

https://tinyurl.com/mwnkar8u

“Tracking the AI Evolution in Research Libraries: Findings from ARL’s Third AI Quick Poll”

Optimism about generative AI is evident among respondents. Over a quarter (28%) described their outlook as “very positive,” envisioning significant enhancements to library services in the next year. The majority (63%) expressed a “somewhat positive” view, acknowledging the potential of AI while being mindful of challenges. Only 10% maintained a neutral stance, reflecting a general trend toward growing confidence in AI’s role within libraries. . . .

Engagement with AI technologies shows steady growth. Nearly one-third of respondents (28%) reported that their libraries are actively implementing AI solutions. The largest group (53%) is in the exploratory phase, investigating potential applications, while 19% indicated plans to consider AI in the near future.

https://tinyurl.com/y7js666m

Top 100 Gen AI Consumer Apps

This is the fourth installment of the Top 100 Gen AI Consumer Apps, our bi-annual ranking of the top 50 AI-first web products (by unique monthly visits, per Similarweb) and top 50 AI-first mobile apps (by monthly active users, per Sensor Tower). Since our last report in August 2024, 17 new companies have entered the rankings of top AI-first web products

https://tinyurl.com/n3hvc8wz

“AI Literacy: A Guide For Academic Libraries”

By embracing AI literacy, libraries can lead efforts to demystify AI, offer targeted programs, and foster interdisciplinary collaborations to explore AI’s influence on research and learning. Through partnerships with faculty and campus technology units, librarians can integrate AI literacy into courses, create learning communities, and provide practical training on AI-driven tools. In doing so, academic libraries position ourselves as key players in shaping critical conversations about AI and guiding the next generation of scholars to engage thoughtfully and ethically with these technologies.

https://tinyurl.com/5hap9t7v

Ex Libris: “Summon Research Assistant Is Now Live”

Summon Research Assistant is a new AI-enhanced discovery tool that saves researchers time by summarizing the query response and citing the most relevant resources. Users will benefit from detailed, natural language search capabilities to uncover trusted library materials. Summon Research Assistant only offers content that is vetted by libraries and the Ex Libris via the Central Discovery Index. Searching the Summon Research Assistant generates in real-time a topical overview based on information from the CDI. The most significant resources are cited in the summary, enabling researchers to instantly view context. From here they can elect to explore more results, refine their original query, or pursue a suggested related research question.

https://tinyurl.com/43ast26b

“ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI”

For the sake of this discussion, let’s assume that GAI ligation is successful. How would concepts of attribution and distribution work under existing copy- right rules of compensation? Should every author whose work is present in the dataset have an equivalent claim over every single output? How would such an outcome work in practice? Here, consider again the Stable Diffusion example. The model’s training dataset, LAION 5B, is composed of “5.85 billion CLIP-fil- tered image-text pairs.”¹⁵¹ Given the massive size of the training set, it is difficult to imagine how one could trace the attribution and weight of a single work into the final end result. To do so would be like proposing that a given output image is attributable to 5.85 billion copyright interests.

https://dx.doi.org/10.2139/ssrn.5090127

“Decoding Virtual Chats: NLP Insights Into Academic Library Services.”

This research applies a machine learning (ML) tool to the complete set of transcripts from a research university’s chat reference service (2017–2022) to examine evolving trends and patron needs in the library reference service. The study has two key objectives: 1) demonstrating ML’s effectiveness in the academic library setting, and 2) assessing the impact of COVID-19 on chat reference needs. A text classification model, trained on 1.5 % of the sample, achieves a 75 % accuracy match with human annotations

https://doi.org/10.1016/j.lisr.2025.101344

“Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs”

Paywalls, licenses and copyright rules often restrict the broad dissemination and reuse of scientific knowledge. We take the position that it is both legally and technically feasible to extract the scientific knowledge in scholarly texts. Current methods, like text embeddings, fail to reliably preserve factual content, and simple paraphrasing may not be legally sound. We urge the community to adopt a new idea: convert scholarly documents into Knowledge Units using LLMs. These units use structured data capturing entities, attributes and relationships without stylistic content. We provide evidence that Knowledge Units: (1) form a legally defensible framework for sharing knowledge from copyrighted research texts, based on legal analyses of German copyright law and U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from original text, measured by MCQ performance on facts from the original copyrighted text across four research domains. Freeing scientific knowledge from copyright promises transformative benefits for scientific research and education by allowing language models to reuse important facts from copyrighted text. To support this, we share open-source tools for converting research documents into Knowledge Units. Overall, our work posits the feasibility of democratizing access to scientific knowledge while respecting copyright.

https://arxiv.org/abs/2502.19413

“Building Trustworthy AI Solutions: Integrating Artificial Intelligence Literacy into Records Management and Archival Systems”

This paper explores the essential role of Artificial Intelligence (AI) competencies and literacy in the fields of records management and archival practices, within the framework of the InterPARES Trust AI project. . . . The study employs two complementary approaches: (1) a detailed competency framework developed through literature reviews, interviews with archival professionals who have applied AI to the processing of records, and validation workshops with practitioners; and (2) a comprehensive AI literacy framework derived from multiple case studies and theoretical discussions. . . . Findings indicate that archival professionals can leverage AI in their work practices by acquiring basic AI literacy, practical AI skills, data-related skills, tool-testing and evaluation, adaptation of AI to their workflows, and by actively engaging in collaborative projects with information technology (IT) developers.

https://doi.org/10.48550/arXiv.2307.14852

Pew Research Center: “U.S. Workers Are More Worried Than Hopeful About Future AI Use in the Workplace”

About half of workers (52%) say they’re worried about the future impact of AI use in the workplace, and 32% think it will lead to fewer job opportunities for them in the long run, according to a new Pew Research Center survey.

And while 36% of workers also say they feel hopeful about how AI may be used in the workplace in the future, a similar share (33%) say they feel overwhelmed.

https://tinyurl.com/3kcnwbnu

Paywall: “Gemini, & Copilot: Using Generative AI as a Tool for Information Literacy Instruction”

In this paper, the author demonstrates their experiences using generative AI to both assist in developing class activity ideas and in facilitating appropriate student use of generative AI in an information literacy course. Attention is given to emphasizing improper uses of generative AI, specifically within the research process, and how the tools may instead be used in an ethical and useful manner to assist with brainstorming research topics. . . The author describes the activities in detail, including how generative AI was used to assist in forming ideas for an interactive lesson to demonstrate various applications of the technology.

https://doi.org/10.1080/02763877.2025.2465416

“Challenges of Responsible AI in Practice: Scoping Review and Recommended Actions”

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. Afterwards, we introduce a number of approaches to RAI from a range of disciplines, exploring their potential as solutions to the identified challenges. We anchor these solutions in practice through concrete examples, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built. Our work considers the socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions.

https://doi.org/10.1007/s00146-024-01880-9

“Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a Time of AI”

Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles. The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems. The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship.

https://arxiv.org/abs/2502.10399

“Copyright’s Big Win in the First Decided US Artificial Intelligence Case”

Back in March of 2023, when there were only a handful of cases alleging copyright infringement for training purposes by AI companies, I predicted that we would soon have some guidance from the court in Thomson Reuters Enterprise Center GMBH and West Publishing Corp. V Ross Intelligence, Inc. Predicting the timing of court decisions is a fool’s errand, and this fool was repeatedly wrong in his predictions on timing. Nonetheless, on February 11, the Ross case did in fact become the first US decision on the merits to directly address copying to train AI. Now we have a clear decision, and it is favorable for rightsholders.

https://tinyurl.com/4amunsmf

U.S. Copyright Office: Identifying the Economic Implications of Artificial Intelligence for Copyright Policy

The Copyright Office released Identifying the Economic Implications of Artificial Intelligence for Copyright Policy, produced by a group of economic scholars discussing the economic issues at the intersection of artificial intelligence and copyright policy.

The group engaged in several months of substantive discussions, consultation with technical experts, and research, culminating in a daylong roundtable event.

The group’s goal was identifying the most consequential economic characteristics of AI and copyright and what factors may inform policy decisions. The roundtable discussion aimed to provide a structured and rigorous framework for considering economic evidence so that the broader economic research community can effectively answer specific questions and identify optimal policy choices.

This publication serves as a platform for articulating the ideas expressed by participants as part of the roundtable. All principal contributors submitted written materials summarizing the group’s prior discussions on a particular topic, with editorial support provided by the Office of the Chief Economist. The many ideas and views discussed in this project do not necessarily represent the views of every roundtable participant or their respective institutions. The Copyright Office does not take a position on these ideas for the purposes of this project.

https://tinyurl.com/5n7yd36r

Generative AI: “Do We Trust Ourselves? Is the Human the Weak Link?”

Generative artificial intelligence tools are becoming ubiquitous in applications across personal, professional and educational contexts. Similar to the rise of social media technologies, this means they are becoming an embedded part of people’s lives, and individuals are using these tools for a variety of benign purposes. This article examines how existing information literacy understandings will not work for artificial intelligence literacy, and provides an example of artificial intelligence searching, demonstrating its shortcomings. Present approaches may fall short of the answer required to navigate these new information tools, and this begs the question of what comes next. The current scope of information literacy and technology necessitates a multidisciplinary approach to solving the question of ‘what to do with artificial intelligence’ and arguably most impactfully requires one to acknowledge that what has worked may no longer suffice.

https://doi.org/10.1177/03400352251315845

2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide

Key Findings

Strategy and Leadership

A larger proportion of respondents to this year’s survey agreed that “we view AI as a strategic priority” compared with last year’s respondents, at 57% and 49%, respectively.

“Training for faculty” (63%) and “training for staff” (56%) topped the list of the most commonly selected elements in institutions’ AI-related strategic planning efforts.

A mere 2% of respondents said that their institution is accommodating new AI-related costs through new sources of funding, and a plurality of executive leaders (34%) said that their institution has tended to underestimate AI-related costs.

Policies and Guidelines

The proportion of respondents reporting that their institution has AI-related AUPs increased from 23% last year to 39% this year, and only 13% of respondents reported that institution-wide policies have not been impacted by the emergence of AI.

Only 9% of respondents reported that their institution’s cybersecurity and privacy policies are adequate for addressing AI-related risks to the institution.

Use Cases

Teaching and learning is the functional area at the institution most focused on using AI, with particular focus on the areas of academic integrity (74%), coursework (65%), assessment practices (54%), and curriculum design (54%).

Two-thirds (68%) of respondents reported that students use AI “somewhat more” or “a lot more” than faculty, while only 2% reported that faculty use AI more than students, despite institutions’ strategically emphasizing faculty training over student training.

Workforce

A plurality of respondents reported that their institution is supporting needed AI skills by upskilling or reskilling existing faculty or staff (37%) rather than by hiring new staff (1%).

Asked about the AI-related skills needed among their faculty and staff, respondents highlighted “AI literacy” for both staff and faculty, as well as “boosting productivity” for staff and “best practices for teaching” for faculty.

The Digital AI Divide between Institutions

Respondents from smaller institutions are remarkably similar to respondents from larger institutions in their personal use of AI tools, their motivations for institutional use of AI, and their expectations and optimism about the future of AI.

Respondents from small and larger institutions differ notably, however, in the resources, capabilities, and practices they’re able to marshal for AI adoption.

https://tinyurl.com/yc8zpjtu