Aug 2026· Journal of Learning Theory and Methodology· Vol 7, pp. 126-137· 0 citations· 42 references
TL;DR
These correlational findings point to the primacy of perceived usefulness in technology adoption in science instruction, and teacher education programs may benefit from demonstrating the practical value of generative AI in science instruction.
Abstract
Background. Generative artificial intelligence (GenAI) tools are increasingly integrated into higher education, yet the factors driving their adoption among pre-service teachers remain underexplored in Southeast Asian contexts.
Objectives. This study investigated predictors of generative AI use among 118 pre-service science teachers at a state college in Masbate, Philippines. Drawing on the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and the Theory of Planned Behavior, five constructs were examined: Perceived Usefulness, Attitude Toward AI, Effort Expectancy, Facilitating Conditions, and Subjective Norms.
Materials and method. A quantitative descriptive-predictive design with stratified random sampling was used. Data were collected using a validated 32-item instrument and analyzed using four multiple linear regression models.
Results. Generative AI use was rated high (M = 3.67, SD = 0.72). Perceived Usefulness (M = 3.79), Attitude Toward AI (M = 3.71), and Effort Expectancy (M = 3.75) were rated high, while Facilitating Conditions (M = 3.44) and Subjective Norms (M = 3.35) were rated moderate. Across all four models (F = 50.47 to 67.14, p < .001), Perceived Usefulness was the sole predictor consistently significant at p < .001 (β = 0.443 to 0.479), explaining 63.9 to 64.6% of the variance (R² = 0.639 to 0.646). Attitude Toward AI and Effort Expectancy reached significance in selected model configurations. Multicollinearity was within acceptable limits (VIF = 1.40 to 4.76).
Conclusion. These correlational findings, drawn from a single institutional sample, point to the primacy of perceived usefulness in technology adoption. As a hypothesis for future confirmation, teacher education programs may benefit from demonstrating the practical value of generative AI in science instruction.
Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.
Examination of college students’ continuance intention to use AI educational tools in local undergraduate universities across five northwestern Chinese provinces and a capability-centric extension of the unified theory of acceptance and use of technology (UTAUT) are developed.
Yao Wan, Kuan-Min Lu· Frontiers in Psychology· 0 citations
Artificial Intelligence (AI) is transforming global education by enhancing teaching effectiveness and supporting personalized learning. Among its applications, generative AI has gained increasing attention for its ability to create instructional content, assist assessment, and streamline lesson preparation. This study aimed to examine secondary school teachers’ acceptance of generative AI in teaching using the Technology Acceptance Model (TAM). A quantitative survey design was employed, involving 367 teachers from secondary schools in Negeri Sembilan, Malaysia, who were selected through simple random sampling. Data were collected via a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and multiple regression. Results indicated high acceptance levels for perceived usefulness, perceived ease of use, and behavioral intention. The correlation analysis revealed strong and positive relationships among the three constructs, while regression results showed that both perceived usefulness and perceived ease of use significantly predicted behavioral intention, explaining 72.1% of its variance. Notably, perceived ease of use emerged as the stronger predictor. These findings confirm the applicability of the TAM framework in educational contexts and highlight the importance of training, user-friendly AI systems, and supportive policies to enhance teachers’ readiness. Future studies are recommended to explore qualitative insights and extended models to better understand sustainable AI integration in education.
Hasrul Hafiz Hamdan, Intan Farahana Binti Kamsin· International Journal of Inf...· 0 citations
This study examined the attitudes and utilization of generative artificial intelligence (AI) as a learning tool among pre-service mathematics teachers. Employing a descriptive-correlational research design, data were collected from 71 respondents through a structured survey instrument to determine their perceptions of generative AI and the frequency of its use in learning mathematics. Findings revealed that respondents demonstrated a moderately positive attitude toward generative AI across cognitive, affective, and behavioral dimensions. In terms of utilization, participants commonly used generative AI tools for step-by-step explanations, clarification of mathematical concepts, and assistance in solving mathematical problems. However, integration of these tools into regular study routines remained limited. Correlation analysis showed a significant moderate positive relationship between attitudes and frequency of use, indicating that more favorable perceptions are associated with higher levels of engagement with generative AI in academic tasks. Respondents also perceived the curriculum as moderately supportive in developing competencies for effective AI utilization, particularly in enhancing problem-solving skills, critical thinking, and conceptual understanding. Nevertheless, some hesitation was noted in encouraging peer use, reflecting concerns about appropriate and responsible application. In general, the study highlights the need for structured integration of generative AI in mathematics education. It is recommended that teacher education institutions provide targeted training, clear guidelines, and pedagogical strategies to strengthen technological competence, critical thinking, and ethical use of AI. Future research may further examine its long-term effects on learning outcomes and professional readiness.
Nora V. Marasigan, Ericka A. Onte, Althea P. Matanguihan et al.· Community and Social Develop...· 0 citations
Generative artificial intelligence (AI) is rapidly transforming important areas of human activity, yet adults’ acceptance of generative AI applications remains insufficiently understood. This cross-sectional study examined the acceptance of generative AI applications among 460 adults recruited through online convenience sampling. Data were collected using the generative artificial intelligence acceptance scale (GAIAS), a validated instrument based on dimensions derived from the unified theory of acceptance and use of technology (UTAUT). The scale comprised four dimensions: performance expectancy, effort expectancy, facilitating conditions, and social influence. Participants reported moderate to high overall acceptance of generative AI. Effort expectancy, reflecting perceived ease of use (PEOU), received the highest mean score, followed by performance expectancy, reflecting perceived usefulness (PU), whereas social influence received the lowest mean score. This descriptive pattern indicates that perceived usability and usefulness were rated more positively than perceived social influence within the present sample. After Holm-Bonferroni correction across the 25 primary comparisons, statistically significant group differences remained for age and educational level in overall generative AI acceptance, performance expectancy, and effort expectancy. No multiplicity-adjusted differences were identified according to gender, personal computer ownership, or previous computer training. The age- and education-related findings should not be interpreted as evidence of linear relationships, independent effects, or causation. Within this digitally connected convenience sample, the findings indicate that PEOU and PU were more prominent dimensions of generative AI acceptance than social influence. Age- and education-related group differences were also observed. However, the cross-sectional design, online convenience sampling, and marked subgroup imbalances limit causal interpretation and generalizability to the wider adult population. The findings may nevertheless inform the development of accessible generative AI tools and targeted digital-literacy initiatives.
Eugenia Konti, K. Kotsis· International Journal of Cha...· 0 citations
This study examines the adoption of generative artificial intelligence (GenAI) in instructional practices among K-12 computer science teachers (CSTs) in China, addressing a gap in understanding how subject-specific teachers engage with emerging technologies. Grounded in Expectancy-Value Theory (EVT), the research extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating Innovation Expectation (IE), Cost-benefit (CB), and Perceived Risk (PR), thereby capturing both motivational drivers and potential constraints. A mixed-methods design was employed: in the quantitative phase, survey data from 338 CSTs across 20 provinces were analyzed using structural equation modeling (SEM). The results showed that Performance Expectancy (PE), Effort Expectancy (EE), and IE significantly strengthened teachers’ Attitude to Use (AU), while Social Influence (SI) was not significant. Intention to Use (IU) was shaped by IE, CB, AU, and PR, with PR exerting a negative effect. IU further served as a significant predictor of Behavioral Intention to Use (BI). In the qualitative phase, 12 CSTs participated in concept map-supported interviews. Thematic analysis highlighted barriers including perceived erosion of teacher authority, student overreliance, fragmented understanding of GenAI functions, and ethical data security concerns, while underscoring the importance of targeted professional development and discipline-specific tools. Findings illustrate the multidimensional factors shaping the adoption of GenAI and emphasize the value of theoretically grounded, context-sensitive approaches in supporting teachers’ integration of emerging technologies. These findings suggest that schools and education authorities can promote the effective use of GenAI by teachers through tailored training, tiered support resources, and clear ethical guidelines.
S. Zhao, Chunchen Kang, Wanshan Hu et al.· Humanities and Social Scienc...· 0 citations
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