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The purpose of this study is to examine how young people imagine civic futures through speculative fiction writing about artificial intelligence (AI) technologies. The authors argue that young people’s speculative fiction writing about AI not only helps make visible the ways they imagine the impacts of emerging technologies and the modes of collective action available for leveraging, resisting or countering them but also the frictions and fissures between the two.

This practitioner research study used data from student artifacts (speculative fiction stories, prewriting and relevant unit work) as well as classroom fieldnotes. The authors used inductive coding to identify emergent patterns in the ways young people wrote about AI and civics, as well as deductive coding using digital civic ecologies framework.

The findings of this study spotlight both the breadth of intractable civic concerns that young people associate with AI, as well as the limitations of the civic frameworks for imagining political interventions to these challenges. Importantly, they also indicate that the process of speculative writing itself can help reconcile this disjuncture by opening space to dwell in, rather than resolve, the tensions between “the speculative” and the “civic.”

Teachers might use speculative fiction writing and the digital civic ecologies framework to support students in critically examining possible AI futures and effective civic actions within them.

Speculative fiction writing offers an avenue for students to analyze the growing civic concerns posed by emerging platform technologies like AI.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

This study aims to determine college/university students’ mobile phone practices to understand key user preferences and set a baseline understanding for the development of prospective library informational mobile services.

This study is based on the results of an online-survey method. The authors recruited students majoring in the Information Science program in School of Information at Pratt Institute (USA) (74 participants) and in Kyiv National University of Cultural and Arts (Ukraine) (89 participants).

The general trends in the use of mobile technologies by American and Ukrainian students were identified. Key components of library mobile services are offered. Such services may include information and reference service via messengers; development of library mobile applications of audio and video content; access to educational, scientific, popular science literature and fiction; and supplementing online services with library chatbots.

This study was limited to only students majoring in information science at university; however, the data obtained helps outline several general trends common to student youth who are actively working with information.

The obtained results will be useful in the practical activities of libraries and other information institutions for the development of a comprehensive information service based on mobile technology.

This study contributes to understanding how to improve library informational service considering users habits and preferences. Libraries management and librarians can use the findings as a basis for prioritizing the improvement of library informational mobile service.

This study aims to focus on publications that jointly address robots in the tourism field and the technology acceptance model (TAM).

This study adopts bibliometric analysis. Publications listed in the Web of Science database constitute the scope of this research. 51 publications were analyzed within the scope of the research.

Between the years 2017 and 2023, an upward trend in the number and citations of publications was identified. It has been observed that article studies are more prevalent compared to other types of publications. When considering the indexes of the publications, a significant majority were found to be in Social Sciences Citation Index (SSCI) and Science Citation Index (SCI)-EXPANDED. The status of the keywords identified within the scope of the research in the abstracts of the publications has been presented. The keyword “robot” was found to be the most frequently occurring in the abstracts. The abstracts were also analyzed, and the publications were accordingly clustered into five distinct themes.

This study offers a comprehensive evaluation of publications concerning the use of robots in the tourism sector, framed within the context of the TAM. Within the scope of the study, the findings were interpreted using bibliometric analysis. The publications have been categorized into themes. The results presented provide insights into the necessity for further publications in this field.

This study aims to answer two significant questions: What are the relative consumer and firm-level effects of marketing through metaverse compared to conventional marketing endeavours? What are the current trends in utilizing the metaverse as reported in the recent literature?

This study uses a systematic literature review methodology, using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart to synthesize existing research. A total of 35 articles written in English were selected and analysed from two databases, Web of Science and EBSCO Host.

The findings indicate that consumer-level effects of the metaverse include consumer loyalty and brand attachment. The firm-level benefits are decentralization and cost reductions. The paper proposes a framework indicating variables that could attenuate or enhance the association between immersive components of the metaverse and their resultant effects.

This study contributes to understanding the role of metaverse in marketing practices related to the marketing mix components. The study conceptualizes a novel framework for the metaverse and its resultant effects.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.