Data · dataset · 2023
Adoption of Artificial Intelligence-based e-Learning Platforms in Institutes of Excellence: extending UTUAT with Technophobia, Technophilia, Content Quality, and Functional Quality.
Listed in Teesside University Research Data Repository
This research study aims to enhance our understanding of the factors influencing the adoption of AI-ELP by extending the Unified Theory of Acceptance and Use of Technology (UTAUT).
Description
The extended model incorporates technophobia, technophilia, content quality, and functional quality. Through a comprehensive review of the existing literature, this study proposes an augmented conceptual framework that considers the unique psychological tendencies of users toward technology (technophobia and technophilia) and the quality aspects of the AI-ELP platform (content quality and functional quality).
By incorporating these additional factors, this research provides a more holistic understanding of users’ acceptance and use of AI-ELP. This study employed quantitative research methods, including surveys and statistical analysis, to collect and analyze data from research scholars at IIT, Kharagpur. These findings will contribute to theoretical advancements and practical implications by shedding light on the complex dynamics of adopting AI-ELP.
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The research outcomes are expected to guide educational practitioners and policymakers in developing strategies to address user concerns, enhance the quality of content and platform functionality, and promote the effective adoption and use of AI-ELP in educational settings.
Links
Where it is published
- DOI doi.org/10.17632/n69rdbph73.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/n69rdbph73.1 | 9 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |