Scientometrics is undergoing a methodological transformation driven by the increasing availability of large-scale scientific data and advances in artificial intelligence (AI). Traditional approaches centered on citation analysis and bibliographic coupling are now complemented by methods that leverage semantic representations, structured knowledge, and natural language understanding. However, current generative AI systems pose inherent challenges such as hallucinations, lack of transparency in decision-making and explainability, and issues with source reliability. In this article, we seek to mitigate these challenges by outlining a framework that integrates knowledge graphs (KGs) and large language models (LLMs) for scientometric inquiry. Taking a socio-technical perspective, the article sets out to explore the intersectional space of computing and information science from a human-centered approach. The framework is designed to support both established scientometric tasks, such as trend analysis, topic detection, and collaboration mapping, and more exploratory, insight-generating applications, including question answering, knowledge discovery, and contextual enrichment of scientific content. Drawing on recent developments in the use of KGs and LLMs in scientific domains, we provide a comparative overview of existing work, identify key design principles, and discuss the advantages and limitations of such a framework. Our goal is to chart a pathway toward more interactive, transparent, and generative approaches in information science and cross-disciplinary research. • Multidimensional mapping from large bibliographic datasets remains challenging. • A KG-LLM framework is proposed to support interactive scientometric inquiry. • HITL validation addresses the black-box limitations of KG- and LLM-enabled solutions. • Implications for knowledge-based question answering are derived.

The pervasiveness of technical systems in our lives calls for a broad understanding of the interaction between humans and technology. Affinity for technology interaction (ATI) scale measures the tendency of a person to actively engage or to avoid interaction with technological systems, including both software and physical devices. This research presents a psychometric analysis of a Finnish version of the ATI scale. The data consisted of 796 responses of students in a Finnish university. The data were analyzed utilizing factor analysis and both nonparametric and parametric item response theory. The Finnish version of the ATI scale proved to be essentially unidimensional, showing high reliability estimates, and forming a strong Mokken scale. Hierarchical multiple regression analysis showed that men had a slightly higher affinity for technology than women when controlling for age and field of study; however the effect size was small.

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher's support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.

The purpose of academic advising is to help students with developing educational plans that support their academic career and personal goals, and to provide information and guidance on studies. Planning and management of the students’ study path is the main joint activity in advising. Based on a study log of passed courses, we propose to use robust, prototype-based clustering to identify a set of actual study path profiles. Such profiles identify groups of students with similar progress of studies, whose analysis and interpretation can be used for better institutional awareness and to support evidence-based academic advising. A model of automated academic advising system utilizing the possibility to determine the study profiles is proposed.