Students mindset to adapt AI chatbots for the effectiveness

The rapid incorporation of Artificial Intelligence (AI) technologies into higher education is shifting the focus toward understanding students’ perspectives and factors affecting the adoption of AI chatbots to maximise their use in online and virtual educational environments. This study fills an important gap in the literature by examining direct and mediated relationships of key constructs such as AI perceived usefulness, AI perceived ease of use, and AI technical competency toward AI chatbot usage. This study aims to investigate students’ mindsets regarding adopting AI chatbots for the effectiveness of online learning in higher education. Data were collected from 429 university students and analysed using the partial least squares-based structural equation modelling (PLS-SEM) technique. The results revealed that perceived usefulness (PU), perceived ease of use (PEU), and tech competency (TC) have a significant impact on AI capability. Subjective norm (SN) has no significant impact on AI chatbot capability. The capability of AI chatbots significantly influences the adoption of AI chatbots for learning effectiveness. The findings indicated that AI chatbot capability mediates the effect of PU, PEU, and TC on the adoption of AI chatbots; however, there is no mediating effect in the relationship between SN and AI chatbot capability. Facilitating conditions moderate the effect of PU and TC on AI chatbot capability. This research addresses a new insight into AI chatbot adoption within the context of higher education, particularly demonstrating the mediating and moderating function of AI chatbot capability and adoption on students’ PU, PEU, and understanding of tech-competent concepts.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-09
DOI
https://doi.org/10.5281/zenodo.18918564
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Students mindset to adapt AI chatbots for the effectiveness

Jayavel k
Zenodo (CERN European Organization for Nuclear Research)
AI in Service Interactions
article

Students mindset to adapt AI chatbots for the effectiveness

Jayavel k
article en

Abstract

The rapid incorporation of Artificial Intelligence (AI) technologies into higher education is shifting the focus toward understanding students’ perspectives and factors affecting the adoption of AI chatbots to maximise their use in online and virtual educational environments. This study fills an important gap in the literature by examining direct and mediated relationships of key constructs such as AI perceived usefulness, AI perceived ease of use, and AI technical competency toward AI chatbot usage. This study aims to investigate students’ mindsets regarding adopting AI chatbots for the effectiveness of online learning in higher education. Data were collected from 429 university students and analysed using the partial least squares-based structural equation modelling (PLS-SEM) technique. The results revealed that perceived usefulness (PU), perceived ease of use (PEU), and tech competency (TC) have a significant impact on AI capability. Subjective norm (SN) has no significant impact on AI chatbot capability. The capability of AI chatbots significantly influences the adoption of AI chatbots for learning effectiveness. The findings indicated that AI chatbot capability mediates the effect of PU, PEU, and TC on the adoption of AI chatbots; however, there is no mediating effect in the relationship between SN and AI chatbot capability. Facilitating conditions moderate the effect of PU and TC on AI chatbot capability. This research addresses a new insight into AI chatbot adoption within the context of higher education, particularly demonstrating the mediating and moderating function of AI chatbot capability and adoption on students’ PU, PEU, and understanding of tech-competent concepts.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 34%
AI in Service Interactions
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