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The role of AI characteristics and their influence on higher education students’ continuance intention to use GenAI tools

Sindhu Singh & Jerónimo Paiva

Information Discovery and Delivery2025https://doi.org/10.1108/idd-03-2025-0060article
ABDC C
Weight
0.48

What the paper says

Purpose The purpose of this study is to identify the factors that contribute to the continued intention and actual use of GenAI tools among Indian university students. Generative artificial intelligence (GenAI) tools significantly transform and disrupt the education sector, offering substantial opportunities for educators and learners. Previous research has predominantly concentrated on adopting ChatGPT within the education sector; however, the effects of other generative artificial intelligence (AI) tools in education remain insufficiently explored. Furthermore, it examines how AI characteristics such as perceived anthropomorphism, perceived intelligence and perceived technology novelty (TN) influence perceived usefulness and perceived ease of use, which are the predictors of continuance intention. Design/methodology/approach This study develops a theoretical model incorporating AI features as antecedents to the Technology Continuance Theory (TCT). This study uses a cross-sectional survey to collect responses from Indian higher education students. The authors use the PLS-SEM model with SmartPLS 4.0 for analysis. Findings This study tested the applicability of TCT in GenAI usage in education settings. This research empirically proved that perceived intelligence and TN are the antecedents to perceived use and ease of use. From the TCT model, satisfaction and attitude positively influenced continuance intention to use GenAI tools, and continuance intention positively influenced the actual use of GenAI in education. Originality/value This study empirically validated the use of various GenAI tools in the teaching and learning process, contributing to the existing research. This study is one of the earliest to examine the human–AI interaction in an educational setting. This study empirically tested TCT from the perspective of higher education students’ actual use of GenAI tools. The findings of this study offer many practical insights for stakeholders, such as EdTech companies, AI companies, GenAI developers, educators and academic institutions, to implement GenAI tools successfully.

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https://doi.org/https://doi.org/10.1108/idd-03-2025-0060

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@article{sindhu2025,
  title        = {{The role of AI characteristics and their influence on higher education students’ continuance intention to use GenAI tools}},
  author       = {Sindhu Singh & Jerónimo Paiva},
  journal      = {Information Discovery and Delivery},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/idd-03-2025-0060},
}

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.