Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes

Author:

Lavidas Konstantinos1ORCID,Voulgari Iro2ORCID,Papadakis Stamatios3ORCID,Athanassopoulos Stavros4,Anastasiou Antigoni1,Filippidi Andromachi1,Komis Vassilis1ORCID,Karacapilidis Nikos5ORCID

Affiliation:

1. Department of Educational Sciences and Early Childhood Education, University of Patras Greece, 265 04 Rio, Greece

2. Department of Early Childhood Education, National and Kapodistrian University of Athens, Greece13a Navarinou, 10 680 Athens, Greece

3. Department of Preschool Education, Faculty of Education, University of Crete, 741 00 Crete, Greece

4. Department of Philosophy, University of Patras Greece, 265 04 Rio, Greece

5. Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Rio, Greece

Abstract

Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Publisher

MDPI AG

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5. Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., and Wu, Y. (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. arXiv.

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