Modeling behavioral intention toward generative AI use in higher education English language teaching
Bibliographic Data
| ID | 22168452 |
|---|---|
| Authors | Ronnel Victor Kilat (0000-0002-0029-6652, Cebu Normal University, corresponding author) |
| Year | 2026 |
| Volume | 11 |
| Publication date | 2026-02-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1743380 |
| OpenAlex | W7130644176 |
| Language | EN |
| References cited | 69 |
Introduction The rapid emergence of generative artificial intelligence (AI) presents new opportunities and challenges for English language teaching (ELT) in higher education, particularly in developing and resource-diverse contexts. Responding to limited empirical evidence from Philippine higher education, this study examines the behavioral intention of higher education English language teachers in Cebu to adopt generative AI in instructional practice. Guided by an integrated framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT) and Expectancy–Value Theory (EVT), and extended with perceived knowledge of AI and perceived privacy concerns, the study adopts a predictive, theory-guided design. Methods Survey data were collected from 488 higher education teachers across urban and rural state universities and local colleges in Cebu and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Results show that performance expectancy predicts perceived attainment value, effort expectancy predicts perceived utility value, and social influence shapes perceived attainment value, perceived intrinsic value, and perceived cost. Facilitating conditions predict both perceived utility value and perceived cost. Among downstream mechanisms, perceived attainment value and perceived utility value significantly predict perceived knowledge of AI, which emerges as the strongest direct predictor of behavioral intention. Perceived intrinsic value does not significantly predict perceived knowledge of AI, and perceived privacy concerns do not significantly predict behavioral intention after accounting for value-based and knowledge-based predictors. Moderation analysis indicates that age does not significantly moderate any structural relationships, while sex moderates only the relationship between effort expectancy and perceived utility value. Discussion Overall, the findings indicate that behavioral intention to adopt generative AI in higher education ELT is primarily driven by performance-related beliefs, value-based appraisals, and self-assessed AI competence, rather than by demographic characteristics or privacy concerns alone. The study contributes a context-sensitive, theory-integrated explanation of AI adoption intentions and offers implications for AI literacy, professional development, and responsible institutional integration in higher education English language teaching
Educational attainment · Expectancy theory · Generative grammar · Higher education · Moderation · Structural equation modeling · Unified theory of acceptance and use of technology · AI in Service Interactions · E-Learning and COVID-19 · Technology Adoption and User Behaviour
Age Differences in Technology Adoption Decisions
Understanding information systems security policy compliance
Understanding Web-based learning continuance intention
Exploring the impact of artificial intelligence on teaching and learning in higher education
Principles alone cannot guarantee ethical AI
Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education
Artificial Intelligence (AI)
Technology Use for Teacher Professional Development in Low- and Middle-Income Countries
Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior1
Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools
AI-generated feedback on writing
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis
User Acceptance of Information Technology
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Behavioural Intention of Using E-Learning System
Predictors of higher education students’ behavioural intention and usage of ChatGPT
Critical issues of online learning management in higher educational institutions in a developing country context
Interaction during online classes fosters engagement with learning and self-directed study both in the first and second years of the Covid-19 pandemic
Conceptual model to predict Filipino teachers’ adoption of ICT-based instruction in class
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
AI-Driven Digital Storytelling
Are you feeling successful
Through tensions to identity-based motivations
E-learning systems versus instructional communication tools
AI in the Foreign Language Classroom
| Highly cited | No |
|---|