Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants

被引:0
|
作者
Jawaher Hamad Al Shamsi
Mostafa Al-Emran
Khaled Shaalan
机构
[1] The British University in Dubai,Faculty of Engineering & IT
来源
Education and Information Technologies | 2022年 / 27卷
关键词
Artificial intelligence; Voice assistant; Human-AI interaction; Technology acceptance; Drivers; Education;
D O I
暂无
中图分类号
学科分类号
摘要
Artificial intelligence (AI)-based voice assistants have become an essential part of our daily lives. Yet, little is known concerning what motivates students to use them in educational activities. Therefore, this research develops a theoretical model by extending the technology acceptance model (TAM) with subjective norm, enjoyment, facilitating conditions, trust, and security to examine students’ use of AI-based voice assistants for instructional purposes. The developed model was then validated based on data collected from 300 university students using the PLS-SEM technique. The results supported the role of enjoyment, trust, and perceived ease of use (PEOU) in affecting the perceived usefulness (PU) of voice assistants. The empirical results also showed that facilitating conditions and trust in technology strongly influence the PEOU. Contrary to the extant literature, the results indicated that subjective norm, facilitating conditions, and security did not impact PU. Similarly, subjective norm and enjoyment did not affect PEOU. This research is believed to add a holistic understanding of the key drivers affecting students’ use of voice assistants for educational purposes. It offers several theoretical contributions and practical implications on how to successfully employ these assistants.
引用
收藏
页码:8071 / 8091
页数:20
相关论文
共 50 条
  • [41] An evaluation of artificial intelligence-based clinical decision supports use in Brazil.
    Rocha, Hermano Alexandre Lima
    Dankwa-Mullan, Irene
    Juacaba, Sergio Ferreira
    Preininger, Anita
    Felix, Winnie
    Thompson, Julie V.
    Bright, Tiffani
    Jackson, Gretchen Purcell
    Meneleu, Pedro
    JOURNAL OF CLINICAL ONCOLOGY, 2019, 37 (15)
  • [42] Towards artificial intelligence-based assessment systems
    Rose Luckin
    Nature Human Behaviour, 1
  • [43] Conceptualizing Artificial Intelligence-Based Service Ecosystems
    Zimmermann, Alfred
    Schmidt, Rainer
    Sandkuhl, Kurt
    Jugel, Dierk
    Schweda, Christian
    Mohring, Michael
    Keller, Barbara
    ADVANCES IN THE HUMAN SIDE OF SERVICE ENGINEERING (AHFE 2021), 2021, 266 : 377 - 384
  • [44] Comparing generative artificial intelligence tools to voice assistants using reference interactions
    Wheatley, Amanda
    Hervieux, Sandy
    JOURNAL OF ACADEMIC LIBRARIANSHIP, 2024, 50 (05):
  • [45] Artificial Intelligence-Based Optimal Grasping Control
    Kim, Dongeon
    Lee, Jonghak
    Chung, Wan-Young
    Lee, Jangmyung
    SENSORS, 2020, 20 (21) : 1 - 17
  • [46] Artificial Intelligence-Based Smart Engineering Education
    Ouyang, Fan
    Jiao, Pengcheng
    Alavi, Amir H.
    SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020, 2020, 11379
  • [47] Artificial Intelligence-Based Cognitive Radar Architecture
    Czuba, Arkadiusz
    2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2021), 2021, : 116 - 120
  • [48] REUSE SYSTEM - AN ARTIFICIAL INTELLIGENCE-BASED APPROACH
    PRASAD, A
    PARK, EK
    JOURNAL OF SYSTEMS AND SOFTWARE, 1994, 27 (03) : 207 - 221
  • [49] Artificial Intelligence-Based New Material Design
    Babanli, M. B.
    10TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTIONS - ICSCCW-2019, 2020, 1095 : 24 - 32
  • [50] Artificial Intelligence-Based Detection of Smoke Plume
    Jeong, Yemin
    Youn, Youjeong
    Kim, Seoyeon
    Kang, Jonggu
    Choi, Soyeon
    Im, Yungyo
    Seo, Youngmin
    Yu, Jeong-Ah
    Sung, Kyoung-Hee
    Kim, Sang-Min
    Lee, Yangwon
    KOREAN JOURNAL OF REMOTE SENSING, 2023, 39 (02) : 859 - 873