Working paper exploring ChatGPT. It begins with an introduction to ChatGPT and then proceeds into a transcript of a conversation with the platform about it and related AI technologies’ impact on the future of scholarly publishing, before concluding with some discussion on the further implications.

Correction to article changes the name of one of the authors.

This article proposes monsters are documents and examines the coding and decoding of monster documents through a functional ontology lens. This article was presented at the 2020 Annual Meeting of the Document Academy.

This is an appendix to “What We Can Learn From Each Other: International Archival Pedagogy Conversations and Collaborations,” a manuscript that was inspired by a collaborative workshop at the 2023 institute of the Archival Education and Research Initiative (AERI). The lively workshop discussion revealed both how much the participants all had in common, and how much they had to learn from each other. Their discussion highlighted how disconnected and geographically siloed archival education programs still continue to be, despite opportunities for international collaboration between archival educators. With this issue in mind, workshop participants decided to continue their conversations past the AERI 2023 meeting as part of an investigation of current pedagogical practices, teaching needs, and future goals of archival educators in a variety of settings. They met on Zoom and then corresponded asynchronously for a discussion of the structures of their archival education programs, their students’ needs, their teaching goals, how they respond to challenges in their virtual and physical classrooms, and how they can leverage this and future collaborations to benefit both their students and the archival education field as a whole. Using the transcripts of their conversations as a data set, they then used a grounded theory analysis …

Article describes how, the goal of ITS is to support learning content, activities, and resources, adapted to the specific needs of the individual learner and influenced by learner’s motivation. This research proposes an intentional model that adopts Map formalism to support personalized learning guidance by considering learner’s motivation.

This article is from the 26th ACM Conference on Innovation and Technology in Computer Science Education and discusses the design, effectiveness, and curricular impacts of an introductory data science course focused on practical programming skills and allowing students to concurrently complete the course in Python, R, or MATLAB. Students indicated a preference for the multi-language course design and the course became the recommended first programming course for a newly developed and approved undergraduate data science majors.

This article presents a novel approach of introducing a weighted temporal feature to bias a topic clustering toward articles in a similar time frame, performed over a set of finance journal abstracts from 1974 to 2020 to demonstrate how time can be emphasized in trend detection. The authors detect trending finance topics that are not identifiable when we use a standard clustering approach with no temporal bias.