scieee AI-readable full text Open interactive document viewer

The grand LIS challenge: the adoption of AI for scholarly output by astronomers in South Africa

Mvakade, Zuthobeke; de Young, Theresa

Abstract

Disruptive innovations such as Artificial Intelligence (AI), and Large Language Models (LLM) have emerged to become prominent agents of change across all disciplines. Such innovations have transformed the work of scientific research in astronomy. Astronomers use these tools in many ways, such as to mine the literature and write scientific papers. This has implications for digital information literacy and awakens the need to consider ethical considerations for both the astronomy and librarian professions. Librarians face the grand challenge to adapt their services in order to remain relevant in an environment where technological innovations transform the way in which astronomers interact with information. It is therefore pressing that library staff understand the extent to which astronomers use and perceive AI and LLMS as well as their attitudes towards the use of these tools in mining the literature and writing scientific papers. This research paper is based on an institutional study that aimed to identify the factors surrounding astronomers' use of disruptive innovations such as AI and LLMs in the astronomy facilities of the National Research Foundation (NRF) in South Africa. Quantitative data was collected via an online questionnaire was distributed to 250 researchers and astronomers randomly selected. The responses were received and analysed leading to recommendations for possible measures that can be implemented to empower the two astronomy libraries in the NRF.

Full text

THE GRAND LIS CHALLENGE L I S A 1 0 , S A N T I A G O , C H I L E Zuthobeke Mvakade - South African Astronomical Observatory Theresa de Young - University of Cape Town T H E A D O P T I O N O F A I F O R S C H O L A R L Y O U T P U T B Y A S T R O N O M E R S I N S O U T H A F R I C A L I S A 1 0 , S A N T I A G O , C H I L E PAGE 02 THE CONTEXT SOUTH AFRICAN RADIO ASTRONOMY OBSERVATORY SOUTH AFRICAN ASTRONOMICAL OBSERVATORY SALT TELESCOPE IN SUTHERLAND SOUTH AFRICA Credit: @Chantalfourie THE SUPERSTAR LLM TRANSFORMER 2022 PAGE 03L I S A 1 0 , S A N T I A G O , C H I L E www.sarkaritel.com PAGE 04 Free up time for researchers to conduct more analyses or produce more scientific articles PROMOTE PRODUCTIVITY Reduce the language barrier for scientists around the world and improve the quality of the scientific literature ELEVATE QUALITY Promote inclusiveness for those who lack resources and increase equity in scientific publishing LEVEL THE PLAYING FIELD Democratize the research process by compensating for the lack of traditional resources DEMOCRATISE KNOWLEDGE THE EDGE The Transformative Impact of AI and Language Learning Models on Research. L I S A 1 0 , S A N T I A G O , C H I L E Fecher et al. (2025) Koller (2023); Knapen, Chamba & Black (2021) scitechdaily.com False or misleading information that severely impact credibility of research findings HALLUCINATIONS PAGE 05 INTEGRITY AND ETHICS Generically generated text that undermine the originality of thought ORIGINALITY Inherit the built-in biases, favour mainstream opinions and suppress minority views BIAS Fake scientific reports Paper-mill companies Contested authorship AUTHORSHIP L I S A 1 0 , S A N T I A G O , C H I L E Buriak (2023); Fletcher (2025); Liao et al. (2025) net.com forbes.com amazon.com twoday.com 03 UNDERSTANDING THE CHALLENGE PAGE 06 Are astronomy researchers aware of ethical concerns? ETHICAL CONCERNS How do astronomers perceive the use of AI and LLMs and what is their attitude towards its use in their work? PERCEPTION AND ATTITUDE To what degree do South African astronomers use AI and LLMs and who are using them? DEGREE OF USAGE How can the two astronomy libraries adapt to assist? POSITIONING LIS L I S A 1 0 , S A N T I A G O , C H I L E womeninresearchblog.wordpress.com/2019/11/03/julia-south-africa/ ADDRESSING THE QUESTIONS Institutional research study Quantitative data via online questionnaire 45 responses received PAGE 07L I S A 1 0 , S A N T I A G O , C H I L E PAGE 08 WHO RESPONDED? Under 10 years experience 42% Masters, PhD & Post Doc EARLY-CAREER 50% 10 to 57 years experience ESTABLISHED 50% 17 SARAO (38%) 28 SAAO (62%) RESPONDENTS 45 Training received ACADEMIC WRITING TRAINING 60% Mainly with 10+ years experience DO NOT USE AI AND LLM 51% L I S A 1 0 , S A N T I A G O , C H I L E PAGE 09 Image generation Language translation Taylor content COMMUNICATION & OUTREACH 60% Summary tables Comparative analysis LITERATURE REVIEW & SYNTHESIS 50% Proof-read & edit manuscripts Draft abstract Generate summaries EDITING & REVIEWING 51% DEGREE OF USE Predictive text & auto completion Outline & draft academic papers CONTENT DEVELOPMENT 50% Brainstorming Research gaps Hypothesis generation IDEA GENERATION & RESEARCH DESIGN 45% Data visualisation DATA MANAGEMENT & ANALYSIS 50% L I S A 1 0 , S A N T I A G O , C H I L E Machine learning has helped to identify an unexplained astronomical object. Credit: Michelle Lochner