Acceptance of Artificial Intelligence as a Teaching Strategy Among University Professors
The Role of Habit, Hedonic Motivation, and Competence for Technology Integration
Bibliographic Data
| ID | 23039655 |
|---|---|
| Authors | Benicio Gonzalo Acosta Enríquez (0000-0002-9591-861X, National University of Trujillo), Luigi Italo Villena Zapata (0000-0001-9430-0028, Private University of the North), Olger Huamaní Jordan (0000-0001-8377-8018, Universidad Tecnológica del Perú, corresponding author), Carlos López Roca (0000-0003-0751-5755, National University of San Marcos), Betty Margarita Cabrera Cipirán (0000-0003-0923-0315, National University of Trujillo), Willy Saavedra Villacrez (0000-0003-0811-7804, Universidad San Ignacio de Loyola), Carmen Graciela Arbulú Pérez Várgas (0000-0002-8463-6553, Universidad César Vallejo) |
| Editors | Maheswaran S |
| Year | 2025 |
| Volume | 2025 |
| Issue | 1 |
| Publication date | 2025-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Human Behavior and Emerging Technologies (JOURNAL) |
| Journal identifiers | ISSN: 2578-1863 • E-ISSN: 2578-1863 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1155/hbe2/5933157 |
| OpenAlex | W4412846605 |
| Language | EN |
| Citations received | 3 |
| References cited | 128 |
The immersion of artificial intelligence (AI) in higher education presents significant challenges and opportunities. This study examines the acceptance of AI as a teaching strategy among university teachers, following the extended UTAUT2 model with the inclusion of the teacher skills and knowledge for technology integration (SKTI) construct. Employing a quantitative cross‐sectional research design, data were collected from 318 university teachers with prior experience using AI as a learning strategy through nonprobabilistic convenience sampling across 10 universities in northern Peru. Participants completed an online survey, and data were analyzed using descriptive statistics, Kruskal–Wallis tests with Dunn’s post hoc comparisons, and partial least squares structural equation modeling (PLS‐SEM). The results showed that performance expectancy ( β = 0.129 ∗∗ ), hedonic motivation ( β = 0.167 ∗∗ ), habit ( β = 0.405 ∗∗∗ ), and SKTI ( β = 0.263 ∗∗∗ ) had a positive influence on the behavioral intention to adopt AI as a teaching strategy. Additionally, behavioral intention ( β = 0.303 ∗∗∗ ), facilitating conditions ( β = 0.115 ∗ ), and habit ( β = 0.464 ∗∗ ) determine the behavioral use of AI by teachers. The Kruskal–Wallis test revealed significant differences among age groups in the performance expectancy, social influence, habit, and behavioral intention constructs, with the 37‐ to 48‐year‐old age group showing higher average ranks. The discussion highlights that these findings suggest a positive adoption of AI among teachers, driven by individual and contextual factors, and challenges assumptions about the relevance of certain constructs in this specific context. In conclusion, this study represents a significant advancement in understanding the adoption of AI in university teaching and provides valuable guidance for practical implementation efforts.
Competence (human resources) · Habit · Mathematics education · AI in Service Interactions · Organizational and Employee Performance · Psychology · Social Psychology · Technology Adoption and User Behaviour
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| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |