Scopus AI Among Novice Lecturers: Perceptions, Influencing Factors, Institutional Support

Abstract

Artificial intelligence is reshaping academic work. However, little is known about how Scopus AI supports novice lecturers who must both teach and publish. This study therefore explored how novice lecturers adopted, used and planned to integrate Scopus AI into their research and teaching. Using the Enhanced Technology Acceptance Model as a guide, we conducted a qualitative multiple case study with five lecturers who already used Scopus AI for academic writing. Data from semi-structured online interviews and screen-captured interactions were analysed using reflexive thematic analysis. Novice lecturers perceived Scopus AI as a time-saving knowledge aggregator and a catalyst for deeper literature engagement, with a low entry barrier and manageable technical friction. They saw it as directly relevant to teaching, supervision and research, and valued one-stop access to Scopus-indexed sources. Trust rested less on understanding the algorithm than on the curated nature of the database, producing “confidence with caution” supported by systematic cross-checking. Collegial recommendations acted as a gentle nudge, but sustained use was shaped more by performance targets, perceived usefulness and professional image. Institutional licenses and brief webinars enabled initial uptake. Nevertheless, uneven training, unclear AI-use policies and access frictions led to varied levels of integration. Overall, this study adds to the Enhanced Technology Acceptance Model by showing that trust can come from the reliability of the underlying database, even when the algorithm itself is not fully transparent. It also suggests that, once basic AI skills are in place, confidence in one’s own judgement and the tool’s relevance to the job may become more important than peer pressure in supporting long-term adoption.

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