Technological Competence, Training and Support, Attitude Towards AI, and Teachers’ Acceptance DOI Creative Commons

Bob Lourence Silagan,

Teresita T Tumapon

Published: May 5, 2025

The presence of artificial intelligence (AI) in the digital world offers innovative solutions to persistent challenges education. However, teachers' willingness embrace AI is often hindered by concerns about maintaining professional autonomy, data privacy, adequate training, and ensuring authentic interactions with students. This study examined levels technological competence, training support, attitude towards among teachers, how these factors influence teachers’ acceptance AI. A quantitative research design was employed, incorporating descriptive, correlational, causal elements. Data were collected through surveys administered 100 teachers from Senior High School Junior departments Liceo de Cagayan University during 2024–2025 academic year. Descriptive statistics, Pearson’s r correlation, multiple linear regression techniques used analyze data. Findings revealed that demonstrated high competence (M = 4.12), support 3.92), a positive 4.24), which corresponded 4.12). Significant correlations found between key influencing factors: (r 0.738, p < .05), 0.899, 0.851, .05). Remarkably, emerged as strongest predictor (β 0.669, concludes significantly influenced their they receive, and, most notably, attitude. To enhance integration, educational institutions may prioritize comprehensive teacher provide supportive environments, address related AI’s reliability accuracy. Since predictor, promoting reliable, beneficial, pedagogically relevant tool could boost integrate it into practices.

Language: Английский

Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM DOI
Linlin Hu, Hao Wang, Yan Xin

et al.

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Language: Английский

Citations

3
Can student accurately identify artificial intelligence generated content? an exploration of AIGC credibility from user perspective in education DOI
Yulu Cui, Zhang Hai

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Language: Английский

Citations

1
Exploring the usage demands of AIGC functions among Chinese researchers: A study based on the KANO model DOI
Zehang Xie, Wu Li,

Wen Bin Yu

et al.

Information Development, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

This study delves into the utilization demands of Artificial Intelligence-Generated Content (AIGC) tools among Chinese researchers, guided by KANO model to understand their varying demands. By administering a comprehensive online survey (N = 1025), we collected data reflecting researchers’ preferences for different AIGC functions. Our findings reveal multifaceted perspective on user satisfaction: literature research emerged as reverse quality, indicating decline in satisfaction when provided, suggesting concerns over authenticity sources. Must-be qualities—data analysis and interpretation, statistical guidance, citation checks, review response assistance—form backbone essential tools. Attractive qualities such text writing, language services, charting assistance, generation significantly boost satisfaction, highlighting AIGC's strength content creation formatting. Indifferent qualities, including concept clarification viewpoint research, show preference personal efforts, while diagram optimization reference sorting are viewed trivial tasks, comfortably managed with existing software The underscores critical discretionary functions from academics, providing insights tool development need future evolving role global practices.

Language: Английский

Citations

0
Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach DOI Creative Commons
Musa Adekunle Ayanwale, Owolabi Paul Adelana, Nurudeen Babatunde Bamiro

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 14, P. 258 - 258

Published: March 4, 2025

Background The rapid integration of Artificial Intelligence (AI) in education offers transformative opportunities to enhance teaching and learning. Among these innovations, Large Language Models (LLMs) like ChatGPT hold immense potential for instructional design, personalized learning, administrative efficiency. However, integrating tools into resource-constrained settings such as Nigeria presents significant challenges, including inadequate infrastructure, digital inequities, teacher readiness. Despite the growing research on AI adoption, limited studies focus developing regions, leaving a critical gap understanding how educators perceive adopt technologies. Methods We adopted hybrid approach, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) Neural Networks (ANN) uncover both linear nonlinear dynamics influencing behavioral intention (BI) 260 Nigerian in-service teachers regarding after participating structured training. Key predictors examined include Perceived Ease Use (PEU), Usefulness (PUC), Attitude Towards (ATC), Your Colleagues (YCC), Technology Anxiety (TA), Teachers’ Trust (TTC), Privacy Issues (PIU). Results Our PLS-SEM results highlight PUC, TA, YCC, PEU, that order importance, predictors, explaining 15.8% variance BI. Complementing these, ANN analysis identified ATC, PUC most factors, demonstrating substantial predictive accuracy with an RMSE 0.87. This suggests while drives PEU positive attitudes are foundational fostering engagement Conclusion need targeted professional development initiatives teachers’ competencies, reduce technology-related anxiety, build trust ChatGPT. study actionable insights policymakers educational stakeholders, emphasizing importance inclusive ethical ecosystem. aim empower support AI-driven transformation resource-limited environments by addressing contextual barriers.

Language: Английский

Citations

0
Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: the mediating role of confidence and AI readiness DOI Creative Commons
Nannan Liu

BMC Psychology, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 27, 2025

This study aims to explore the mediating role of confidence and artificial intelligence (AI) readiness in university teachers' behavioral intention adopt AI technology, providing empirical support for enhancing willingness use technology from both theoretical practical perspectives. used a random sampling method conduct an online survey 504 teachers, assessing impact subjective norms on intention. The included scales norms, confidence, readiness, Data analysis was performed using AMOS 26, SPSS Statistics 27 software Model 6 PROCESS 4.0 plugin, aiming investigate between Subjective were found have significant positive correlation with indirectly influenced through or readiness. Confidence played chain-mediating relationship (β = 0.0324, 95% CI: [0.0129, 0.0551]), accounting 12.87% total effect. reveals indicating that not only directly enhance but also exert indirect effects single chain mediation findings highlight critical intention, suggesting effectively increase it is important focus improving their thereby strengthening norms.

Language: Английский

Citations

0
Technological Competence, Training and Support, Attitude Towards AI, and Teachers’ Acceptance DOI Creative Commons

Bob Lourence Silagan,

Teresita T Tumapon

Published: May 5, 2025

The presence of artificial intelligence (AI) in the digital world offers innovative solutions to persistent challenges education. However, teachers' willingness embrace AI is often hindered by concerns about maintaining professional autonomy, data privacy, adequate training, and ensuring authentic interactions with students. This study examined levels technological competence, training support, attitude towards among teachers, how these factors influence teachers’ acceptance AI. A quantitative research design was employed, incorporating descriptive, correlational, causal elements. Data were collected through surveys administered 100 teachers from Senior High School Junior departments Liceo de Cagayan University during 2024–2025 academic year. Descriptive statistics, Pearson’s r correlation, multiple linear regression techniques used analyze data. Findings revealed that demonstrated high competence (M = 4.12), support 3.92), a positive 4.24), which corresponded 4.12). Significant correlations found between key influencing factors: (r 0.738, p < .05), 0.899, 0.851, .05). Remarkably, emerged as strongest predictor (β 0.669, concludes significantly influenced their they receive, and, most notably, attitude. To enhance integration, educational institutions may prioritize comprehensive teacher provide supportive environments, address related AI’s reliability accuracy. Since predictor, promoting reliable, beneficial, pedagogically relevant tool could boost integrate it into practices.

Language: Английский

Citations

0