Information Access in the Era of Generative AI

This study investigated science teachers' perceptions of the pedagogical value and challenges of incorporating generative AI into SSI instruction. Using a collaborative action research design, six secondary science teachers with prior SSI teaching experience co-developed and implemented AI-supported SSI lessons. Data were collected through regular meetings, semi-structured interviews, and classroom artifacts, and analysed using both inductive and deductive content analysis. Findings indicated that generative AI served as an effective scaffolding tool that supported teachers in instructional design and reduced the preparation burden typically associated with SSI lessons. It also helped students – especially those with lower academic achievement – access information more easily, engage in discussions, and better understand multiple stakeholder perspectives. However, the study also revealed concerns, including students' uncritical acceptance of AI-generated responses, disparities in digital literacy and access, and the need for improved questioning skills and instructional guidance. This study suggests that while generative AI holds significant promise for supporting SSI instruction, its implementation must be accompanied by ethical guidance, critical AI literacy, and professional development for teachers.

As generative AI (GenAI) becomes increasingly embedded in higher education, students face contradictory narratives about its use as it is often presented either as a risk to academic integrity or as a valuable educational resource (Luo, 2025). Navigating these tensions can be particularly complex for students from equity groups, who already contend with challenges in adapting to university learning environments (Stokes, 2024). While institutional policies attempt to regulate GenAI use, these often lack the nuance needed to guide context-sensitive decision-making (Corbin et al., 2025). This presentation outlines an action research project that investigated how dialogic teaching approaches can support students in developing critical GenAI literacies through collaborative exploration and reflection. The research took place within an Australian university’s Enabling Education program and was grounded in the principles of Dialogic Pedagogy which is a critical framework rooted in reciprocal communication, shared meaning-making, and the co-construction of knowledge (Shor & Freire, 1987). Rather than imposing rules, this pedagogical model encourages transparency, inquiry, and inclusive negotiation of acceptable practices. Encouraging students, who are new to university, to become active agents supports them to build their critical AI literacy (Toncelli, et al., 2025; Wang & Wang, 2025). This approach shifts the emphasis from compliance to empowerment, allowing students and educators to engage in meaningful dialogue about the ethical and effective use of GenAI in their learning. Using a critical participatory action research methodology (Kemmis et al., 2014), the study collected data across three foundational courses: language and literacy, digital literacy, and information literacy. In the language and literacy unit, students explored the use of GenAI tools in developing their academic writing. Through classroom activities and reflective discussions, students examined GenAI-generated feedback on their work, considering how such input could enhance their language development while identifying its limitations and ethical implications. In the digital and information literacy courses, activities focused on the broader societal and epistemological impacts of GenAI. Students investigated how AI technologies influence knowledge creation, information access, and authorship. Educators facilitated discussions that encouraged students to evaluate when and how GenAI might be used responsibly in their coursework, particularly in creative and research-based projects, without compromising academic ownership. Findings from this project highlight the value of dialogic approaches in Enabling Education programs to help students take ownership of their learning. Students’ initial uncertainty shifted to confidence in sharing their experiences of GenAI. Further, this approach created a safe space for open discussion enabling co-construction of knowledge about critical AI literacy. By centering student voice and agency, this model offers a compelling alternative to top-down enforcement of AI-related policies. It suggests that fostering critical engagement with GenAI builds students’ confidence and skills and strengthens trust between educators and learners. This work contributes to emerging conversations around AI literacy in higher education by proposing a framework that is both inclusive and responsive to the real-world complexities of student experience. It underscores the importance of pedagogical strategies that are participatory, flexible, and grounded in shared inquiry, particularly for students navigating the margins of academic culture.

This study explored students’ perspectives on the benefits and challenges of integrating Generative AI (GenAI) tools in teaching, learning, and assessment within five colleges under the Royal University of Bhutan (RUB). The study is timely and relevant as educators and students in Bhutan increasingly adopt GenAI tools, reflecting a broader global trend. The study outlines three primary objectives. First, it seeks students’ perspectives regarding the integration of GenAI tools in teaching, learning, and assessment in higher education, highlighting the GenAI tools used, the benefits they offer, and the challenges they may impose. Second, the study aspires to provide practical recommendations for educators and academics in higher education institutions for the effective integration of GenAI tools. Finally, the study aims to provide recommendations to relevant stakeholders and policymakers for the adoption or expansion of AI initiatives within the precincts of RUB colleges. This study employed a qualitative approach as qualitative research allows for a deeper understanding of human behaviour and experiences. A purposive sampling technique was used to ensure that participants were selected based on their potential to provide valuable insights relevant to the research objectives. Data were collected through semi-structured focus group interviews, with each group comprising six members (three male and three female). A total of 180 students participated across 30 focus group interviews conducted in five colleges, with six focus group interviews in each college. The data collected were transcribed, coded, and categorised into themes for analysis. To maintain confidentiality, the researchers used a systematic labelling system for both focus groups and individual students during the data analysis process, enabling a comprehensive and structured interpretation of the findings. The findings revealed key benefits of GenAI, such as providing quick and accessible information, fostering personalised learning, and offering emotional support. However, the findings also revealed challenges in terms of the reliability of AI-generated content, the potential hindrance to creativity and critical thinking, and the risk of social and emotional disconnect between students and their learning communities due to overreliance on GenAI. The study therefore recommends raising awareness and improving understanding of GenAI tools among students and establishing comprehensive policy frameworks to promote their ethical use across RUB colleges while upholding high standards of academic quality.

This essay introduces the history of the “generative AI” paradigm, including its underlying political economy, key technical developments, and sociocultural and environmental effects. In concert with this framing it discusses the articles, thinkpieces, and reviews that make up part 1 of this two-part special issue (along with some of the content for part 2). Although large language models (LLMs) are marketed as scientific wonders, they were not designed to function as either reliable interactive systems or robust tools for supporting human communication or information access. Their development and deployment as commercial tools in a climate of reductive data positivism and underregulated corporate power overturned a long history in which researchers regarded chatbots as “misaligned” affordances for safe or reliable public use. While the technical underpinnings of these much-hyped systems are guarded as proprietary secrets that cannot be shared with researchers, regulators, or the public at large, there is ample evidence to show that their development depends on the expropriation and privatization of human-generated content (much of it under copyright); the expenditure of enormous computing resources (including energy, water, and scarce materials); and the hidden exploitation of armies of human workers whose low-paid and high-stress labor makes “AI” seem more like human “intelligence” or communication. At the same time, the marketing of chatbots propagates a deceptive ideology of “frictionless knowing” that conflates a person's ability to leverage a tool for producing an output with that person's active understanding and awareness of the relevant information or truth claims therein. By contrast, the best digital infrastructures for human writing enable human users by amplifying and concretizing their interactive role in crafting trains of contemplation and rendering this situated experience in shareable form. The essay concludes with reflections on alternative pathways for developing AI—including communicative tools—in the public interest.

Abstract: The rapid development of generative AI is transforming university information literacy education by reshaping how students access and process information. This study systematically reviews 49 research papers published between 2020 and 2024, using the PRISMA framework and thematic analysis to explore the applications, impacts, and pedagogical changes associated with generative AI in the field of information literacy education. Results show that generative AI has a wide range of applications in information literacy education, mainly in student learning support, learner-oriented personalized learning, academic research assistants, academic writing assistance, information literacy skills development, and curriculum design and teaching assistance. Generative AI has promoted students’ information retrieval, evaluation skills and critical thinking, but also brought the challenge that over-reliance on AI may weaken students’ critical thinking and information evaluation skills. Important changes in curriculum design and teaching methods are needed to introduce instruction in prompt engineering and computational thinking. The role of the teacher has shifted from knowledge transmitter to learning facilitator, emphasizing the importance of professional basic knowledge and ethical education. Through the results it is find that Generative AI can significantly enhance student learning outcomes and skills development in university information literacy education. However, its application requires caution and must fully consider potential challenges and risks. Through reasonable curriculum design, innovative teaching methods, and policy support, educators can leverage the advantages of Generative AI to cultivate high-quality talent with critical thinking, innovation, and a sense of moral responsibility. As AI technology continues to develop, information literacy education will usher in more innovations and opportunities, bringing new vitality and possibilities to higher education.

Information access systems such as search engines and generative AI are central to how people seek, evaluate, and interpret information. Yet most systems are designed to optimise retrieval rather than to help users develop better search strategies or critical awareness. This paper introduces a pedagogical perspective on information access, conceptualising search and conversational systems as instructive interfaces that can teach, guide, and scaffold users’ learning. We draw on seven didactic frameworks from education and behavioural science to analyse how existing and emerging system features, including query suggestions, source labels, and conversational or agentic AI, support or limit user learning. Using two illustrative search tasks, we demonstrate how different design choices promote skills such as critical evaluation, metacognitive reflection, and strategy transfer. The paper contributes a conceptual lens for evaluating the instructional value of information access systems and outlines design implications for technologies that foster more effective, reflective, and resilient information seekers.

Since the arrival of ChatGPT in November 2022, many Generative AI chatbots have appeared in the marketplace. Some of these tools like Consensus, Scholar GPT and Scholar AI are specifically designed to facilitate access to research and scholarly information. Also, research database aggregators and vendors like Scopus, Clarivate and JSTOR have introduced their version of Gen AI applications for use on their databases. Will the widespread use of these tools change the ways people seek, access and use information? How can the contemporary, and future, information science research contribute to the sustainability of the Gen AI-augmented information systems and services, especially in the context of research and scholarly information? By critically analysing a diverse range of research papers, and industry and institutional reports and documents, this article discusses various issues associated with the social, economic and environmental sustainability of research and scholarly information systems and services in the era of Gen AI. It proposes a model for sustainability of the information ecosystem in the era of Gen AI, focusing particularly on access to research and scholarly information using LLM-based chatbots, and proposes a research agenda to achieve the social, economic and environmental sustainability of information.

ABSTRACTThe purpose of the study is to inform LIS curriculum development by understanding student perceptions of generative AI tools. Assignments with a generative AI component for two courses (n = 65) were de‐identified and analyzed after the end of the semester using a grounded qualitative approach. Students recognize the need for caution and critical evaluation of generative AI tool output, while mentioning areas of utility in practice, including information retrieval, reference services, teaching, and information access. Responses highlight the importance of information literacy in the use of these tools, and potential implications for practice in information professions. The study contributes to the literature about the need for curriculum development to address this disruptive technology by identifying the professional competencies that are affected.

Objectives/Goals: Translational researchers often struggle to navigate a complex constellation of institutional resources spanning the IRB to bioinformatics units. We had two aims 1) Systematically map all institution-wide research support units and 2) leverage this database within a generative AI virtual concierge tailored to local investigator queries and needs. Methods/Study Population: This study leveraged mixed methods approach. First, we conducted needs assessments of local study teams to identify barriers to translation, revealing that research resources are often unknown to study teams. Second, we identified all investigators, institutional units, and offices offering such resources that we call research support units (RSUs). RSUs were surveyed, collecting contact information (leadership, website, physical location), services provided, type of research supported, and performance metrics. Third, the resource database was integrated into a large language model (LLM, e.g., ChatGPT4o) using a retrieval augmented generation (RAG) system within an R Shiny application called virtual concierge. Queries and responses are recorded for quality improvement. Results/Anticipated Results: Needs assessment focus groups consisted of clinical and basic science investigators, study team members (e.g., clinical research assistants), core directors, and administrators (n = 26). Six sessions were conducted in Spring 2024. A major resultant theme was difficulty finding RSUs “by trial and error” and lacking a “clear defined pathway” for accessing RSUs. This prompted a survey-based environmental scan to identify institutional research resources. There were 122 diverse RSUs ranging from the IRB, to grant writing, to single cell sequencing. Each research unit offered a median of 6 service types, totaling 410 service types overall. The resultant Virtual Concierge meaningfully responds to investigator resource queries with appropriate contact and access information. Usability testing is underway. Discussion/Significance of Impact: Linking researchers with translational resources requires mutual understanding, timely communication, and coordination across teams. We systematically filled these information gaps between investigators and institutional resources. Our Virtual Concierge AI bot can help researchers navigate resources through the translational process.

CVPCorp, a provider of container visibility and life-cycle management services, had always relied heavily on information technology and access to near real-time data from worldwide supply chain sources, such as ports and shipping companies, to drive its business. In 2023, they began experimenting with Generative AI (GenAI) and Large Language Models (LLM). The case first describes CVPCorp’s business and products. It then explores how the company embarked on a bottom-up approach for weaving AI quickly into the fabric of its products, operations, and management while leaving us to question how these technologies, as they evolve, will continue to transform CVPCorp and the customers it serves. The case ends with a need to respond to a challenge from an investor—to look at AI from a top-down and strategic perspective.

Introduction Simplifying medication information, using plain language, and improving layout structure can enhance patient comprehension.[1] Generative artificial intelligence (Gen AI) has growing applications in pharmaceutical care, yet the quality and efficiency of its output varies with the chosen models and prompting strategies.[2] Aim To compare multiple large language models (LLMs) and prompting strategies for generating patient drug information and determine an optimal Gen-AI–based workflow. Methods Three medications—pregabalin capsules, acetaminophen extended-release tablets, and levofloxacin tablets—were selected. Eight LLMs (ChatGPT-4o, DeepSeek-R1, Grok 3, and Gemini 2.5 Flash, Doubao, Doubao Deep Thinking, Kimi, Kimi Long Thinking k1.5,) with five prompt strategies (Zero-Shot, Zero-Chain-of-Thought [Zero-CoT], Tree-of-Thought [ToT], Zero-Shot & Few-Shot [ZS&FS], and Zero-CoT & Few-Shot [ZC&FS] yielding 40 combinations. Two pharmacists independently evaluated each text across six dimensions selected aligned with patient-education frameworks and Gen-AI–specific risk considerations: scientific accuracy, comprehensiveness, hallucination, readability, reading time, and accessibility. The first five used a five-point Likert scale using predefined rules or adapted from validated assessment tools; accessibility was scored by the number of human-AI interactions (maximum 3). Quality was defined as the mean total score across dimensions of nine outputs per combination, and stability as the standard deviation (SD). Inter-rater reliability was assessed using a two-way random-effects intraclass correlation coefficient (ICC). Discrepancies were resolved through discussion. The optimal combination was identified through integrated analysis of quality and stability. Results A total of 360 outputs were generated. Inter-rater reliability was excellent (ICC = 0.93; 95% CI: 0.88–0.97; p < 0.001). There are significant differences in the quality and stability. Quality scores ranged from 19.16 (Grok 3 + ZC&FS) to 26.15 (DeepSeek-R1 + ZS&FS). Stability (SD) ranged from 0.31 (Gemini 2.5 Flash + ZC&FS) to 7.42 (ChatGPT + Zero-Shot). DeepSeek-R1 consistently ranked among the highest-performing models, and its combination with ZS&FS was identified as the optimal workflow when balanced between quality and stability. Rankings are shown in Fig. 1. Conclusion This study provides a structured, multidimensional evaluation of LLMs and prompting strategies and has identified an optimal framework for generating patient drug information using generative AI. With pharmacist review, AI-generated materials can support accurate, readable, and accessible medication information, promoting rational drug use. Limitations include the lack of patient usability testing and the evolving nature of LLM capabilities.

Introduction. This paper uses the affordances framework to investigate how Generation Z (GenZ) students in higher education use generative AI (GenAI). There is an increasing need to gain a deeper understanding of GenZ’s interaction with artificial intelligence tools to better support their integration into higher education and the workforce. Method. Data was collated from semi-structured interviews with 34 GenZ students in higher education. Analysis. Thematic analysis was conducted on the qualitative data collected from the semi-structured interviews. Results. The findings suggest GenZ students have seamlessly integrated GenAI into diverse aspects of their lives. This study highlighted three main GenAI affordances that resonate with GenZ students: a) content searching and curation b) content generation and ideation, and c) content enhancement and refinement, revealing new opportunities for information access. Conclusions. This study shed light on the perceived affordances of GenAI for GenZs, addressing a gap in the current literature on GenAI. The findings underscore the significant extent to which GenAI has been integrated into GenZ students’ daily lives. Our study contributes to a better understanding of how GenAI’s affordances facilitate and support GenZ students, providing invaluable insights that can inform future policies on developing literacy for AI use tailored to this group.

This study examines how freely accessible generative AI tools can assist solopreneurs, especially in the initial business formation phase as well as for marketing operations. The research combines both qualitative and quantitative methods through a pilot research and survey study. The research uses multiple methods to gather complete insights about AI tool implementation. Our study focuses on Chinese residents in Germany who are solopreneurs or plan to become solopreneurs to understand their views on generative AI tools and their expected advantages from these tools with ChatGPT as the main example. The study demonstrates through statistical analysis and findings from both survey and pilot phases that ChatGPT shows strong potential to serve as an affordable efficient solution for multiple early-stage entrepreneurial requirements identified by participants. The research demonstrates that generative AI technology offers significant support to new business owners during their initial business formation period.

The rapid rise of generative AI (GenAI) tools presents both opportunities and challenges for transforming the teaching, learning and assessment (TLA) of STEM subjects. This mixed-methods study examined the use of GenAI at Samtse College of Education (SCE), Bhutan, drawing on survey responses from 147 STEM students (ICT: n = 96; Science: n = 51) and four focus group interviews. The study investigated the integration, purposes, comfort and frequency of GenAI use, as well as the associated impacts, challenges and limitations. Findings indicate that SCE STEM students are rapidly integrating GenAI into their academic practices, with ChatGPT serving as the primary tool for assignment support, academic writing and information access. The results highlight the versatility and perceived usefulness of GenAI, while also pointing to risks such as overdependence, reduced tutor-student interaction and ethical concerns. Subject discipline and academic level, rather than gender, emerged as the strongest predictors of comfort and frequency of use. The study recommends establishing clear policies on academic integrity, acceptable use of GenAI, and data privacy and security, while providing students and faculty with clear guidelines to navigate both opportunities and risks.

This paper explores the evolving nature of information-seeking behavior in the era of Generative AI (GenAI), questioning whether we are witnessing a generational shift in how people search for information. We propose a methodology for the first phase of our research focusing specifically on user interfaces for information access with Large Language Models (LLMs). We plan to utilize eye-tracking to analyze user interactions with search engines displaying AI-generated content before the traditional ten-blue links, followed by an engagement scale to capture the user experience. The aim of conducting the first phase is to understand how users interact with new search engine interfaces that incorporate GenAI content, assess their willingness to scroll past GenAI content to view the traditional ''10 blue links' and explore how these interactions differ from existing literature on scanning search engine interfaces. Finally, we outline future directions to deepen our understanding of search behavior in the age of GenAI.