Search results

This paper aims to propose an integrating model for creating virtual libraries in Iranian universities of medical sciences.

This study was conducted with an analytic survey method. The statistical population comprised 66 Iranian universities of medical sciences, of which 59 libraries participated in the study. A researcher-made checklist was used for data collection. To ensure the accuracy of data, interviews and, in some cases, observations were also performed. Statistical estimates, including frequency, percentage, cumulative frequency and diagrams, were used for data analysis, and the system analysis method was used for modeling.

Results demonstrated that the library software programs of the studied universities of medical sciences do not have desirable interoperability capabilities. Only Azarsa program can exchange information with other systems. In terms of metadata and its standards, the studied libraries use programs with various standards, with MARC and Dublin Core standards being the most frequently used ones in the studied sample.

The model proposed here for integration is a hybrid model which can translate metadata standards and use the Z39.50 and OEI protocol to transfer data.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.