A Structural Model of Distance Education Teachers’ Digital Competencies for Artificial Intelligence
Bibliographic Data
| ID | 22045169 |
|---|---|
| Authors | Julio Cabero-Almenara (0000-0002-1133-6031, Universidad de Sevilla), Antonio Palacios-Rodríguez (0000-0002-0689-6317, Universidad de Sevilla, corresponding author), María Isabel Loaiza (0000-0002-5217-1325, Universidad Técnica Particular de Loja), Dhamar Rafaela Pugla-Quirola (Universidad Técnica Particular de Loja) |
| Year | 2025 |
| Volume | 15 |
| Issue | 10 |
| Pages | 1271 |
| Publication date | 2025-09-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci15101271 |
| OpenAlex | W4414426124 |
| Language | EN |
| References cited | 17 |
Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low
Cognition · Distance education · Higher education · Perception · Structural equation modeling · Educational Innovations and Challenges
Reliability and Validity Assessment
A SWOT analysis of ChatGPT
A meta systematic review of artificial intelligence in higher education
Large language models in education
On the evaluation of structural equation models
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Acceptance of Educational Artificial Intelligence by Teachers and Its Relationship with Some Variables and Pedagogical Beliefs
The impact of Generative AI (GenAI) on practices, policies and research direction in education
Preparing for AI-enhanced education
The Promises and Challenges of Artificial Intelligence for Teachers
| Citation velocity | historical |
|---|---|
| Highly cited | No |