Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Июль 29, 2024

Generative artificial intelligence (GAI) advancements have ignited new expectations for (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze influencing factors Chinese university students' GAI technology usage intention. Regarding AI literacy, cognitive literacy in ethics scored highest (M = 5.740), while awareness lowest 4.578). Students' attitudes toward significantly positively influenced their intention, with combined TPB framework explaining 59.3% variance. subjective norms perceived behavioral control, attitude mediated impact Further, provide insights management leadership regarding construction an ecosystem under application technology.

Язык: Английский

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Risks of AI-Assisted Learning on Student Critical Thinking

International Journal of Risk and Contingency Management, Год журнала: 2024, Номер 12(1), С. 1 - 19

Опубликована: Авг. 5, 2024

Artificial Intelligence (AI) has increasingly become a transformative force in the education sector, offering unprecedented opportunities to enhance learning experiences and outcomes. This study examines potential adverse effects of AI-assisted on critical cognitive skills, particularly thinking problem-solving, within context Albania's educational landscape. Employing quantitative methodology, survey 53 students was conducted across various institutions Albania gather data their perceptions regarding learning. The findings indicate no significant difference skills between with prior exposure AI tools those without. However, there is statistically negative correlation reliance for assignments students' problem-solving suggesting that excessive dependence can hinder development independent abilities. Conversely, strong positive found frequency tool usage academic performance assignment efficiency, highlighting benefits enhancing these aspects experience. These results emphasize need balanced integration ensure they complement rather than replace traditional methods. study's have implications educators policymakers, while certain outcomes, it essential address its risks promote skills. Future research should focus larger, more diverse samples, incorporate objective measures explore long-term impacts

Язык: Английский

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Acceptance of Educational Artificial Intelligence by Teachers and Its Relationship with Some Variables and Pedagogical Beliefs

Education Sciences, Год журнала: 2024, Номер 14(7), С. 740 - 740

Опубликована: Июль 6, 2024

This study explores teachers’ acceptance of artificial intelligence in education (AIEd) and its relationship with various variables pedagogical beliefs. Conducted at the Universidad Técnica Particular de Loja (UTPL, Ecuador), research surveyed 425 teachers across different disciplines teaching modalities. The UTAUT2 model analyzed dimensions like performance expectations, effort social influence, facilitating conditions, hedonic motivation, usage behavior, intention to use AIEd. Results showed a high level among teachers, influenced by factors age, gender, modality. Additionally, it was found that constructivist beliefs correlated positively AIEd adoption. These insights are valuable for understanding integration educational settings.

Язык: Английский

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Exploring the use of artificial intelligence in Indonesian accounting classes

Cogent Education, Год журнала: 2025, Номер 12(1)

Опубликована: Янв. 4, 2025

This study aims to reveal the use of artificial intelligence (AI) in accounting classes, analyze factors that influence educators AI continuously learning, and describe challenges ethics developing AI. The research population is (teachers lecturers) Indonesia who are members Professional Alliance Accounting Educators throughout Indonesia. sampling method used was purposive sampling. data collection a questionnaire distributed online via Google form platform, which gathered 230 responses, including 146 teachers 84 lecturers. descriptive analysis structural equation model were data. findings show Canva most widely tool, followed by ChatGPT. Teachers lecturers primarily create learning materials write academic articles. results only performance expectancy gender significantly impact intention education. Conversely, competence key affecting actual usage behavior learning. In addition, various exist using AI, issues related effectiveness efficiency, IT ethics, fostering student engagement interaction.

Язык: Английский

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Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency

Education and Information Technologies, Год журнала: 2024, Номер unknown

Опубликована: Авг. 19, 2024

Abstract In recent years, there has been a growing emphasis on integrating Artificial Intelligence (AI) applications in educational settings. As result, it is essential to assess teachers’ competencies Technological, Pedagogical, and Content Knowledge (TPACK) as pertains AI examine the factors that influence these competencies. This study aims analyze impact of digital proficiency AI-TPACK The utilized correlational survey model involved 401 teachers from various provinces departments Turkey. data collection tools included personal information form, an scale, scale. collected were analyzed using structural equation modeling. research findings revealed below average, whereas their levels above average. Furthermore, significant relationship between was identified, with predictor Based findings, recommendations for future studies are provided.

Язык: Английский

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Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

British Journal of Educational Technology, Год журнала: 2024, Номер 55(6), С. 2530 - 2556

Опубликована: Апрель 23, 2024

Abstract Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt valuable feedback on their teaching practices. However, these deep learning models often have intricate structures numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding analysis likely leads distrust underutilize AI‐powered models. To tackle this issue, we leveraged explainable AI unravel conducted an experiment evaluate the effects explanations. Fifty‐nine pre‐service were recruited randomly assigned either a treatment ( n = 30) or control 29) group. Initially, both groups learned analyse using without Subsequently, group received explanations, while continued receive only predictions. results demonstrated that in exhibited significantly higher levels trust technology acceptance for compared those Notably, there no significant differences cognitive load between two groups. Furthermore, expressed high satisfaction During interviews, they also elucidated how changed perceptions features attitudes towards This study is among pioneering works propose validate use address interpretability challenges within learning‐based context analysis. Practitioner notes What already known about topic Classroom recognized crucial element process. Researchers utilized techniques, particularly methods, dialogue. models, characterized by structures, function boxes, lacking ability transparent limitation can result harbouring underutilizing paper adds highlights importance incorporating approaches issues associated Through experimental study, demonstrates providing enhances teachers' increasing load. Teachers express provided AI. Implications practice and/or policy integration effectively challenge complex used analysing Intelligent systems designed benefit from advanced approaches, which offer users automated By enabling understand underlying rationale behind analysis, contribute fostering users.

Язык: Английский

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Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed Learning in AI-Enhanced Educational Environments

Systems, Год журнала: 2024, Номер 12(9), С. 332 - 332

Опубликована: Авг. 29, 2024

This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive environments. The advent of AI has revolutionized environments, offering unprecedented opportunities for personalized and adaptive education. Generative supports teachers delivering smart education, enhancing acceptance technology, providing personalized, experiences. Nevertheless, application higher education is underexplored. explores how these AI-driven platforms impact abilities, focusing on key teacher support, strategies, technology acceptance. Through a quantitative approach involving surveys 306 undergraduates, we identified motivation, technological familiarity, quality interaction. findings reveal mediating roles self-efficacy motivation. Also, confirmed that improvements support strategies within AI-enhanced environments contribute to increasing self-efficacy, acceptance, contributes uncovering can inform design more effective educational technologies enhance student autonomy outcomes. Our theoretical model research deepen understanding applying while important contributions managerial implications.

Язык: Английский

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Exploring ChatGPT literacy as predictor and moderator in enhancing informal digital learning of English (IDLE): a partial least square analysis

Quality Assurance in Education, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Purpose This study aims to explore the role of ChatGPT literacy within technology acceptance model (TAM) framework and its potential for learners English as a foreign language (EFL), whereby is integrated into their informal digital learning activities. Design/methodology/approach Data from 543 Chinese EFL were collected using cross-sectional quantitative method. The relationships between six factors, namely, literacy, perceived ease use, usefulness, attitude, behavioral intention actual conceptualized tested based on TAM framework. was verified partial least squares structural equation modeling. Findings findings indicated that significant predictor use which two core variables with impact attitude. Perceived positively influenced indicating mediating usefulness in this path. Attitude significantly intention, predicted use. Moreover, moderated relationship Originality/value extends by incorporating moderator Empirical evidence further offered including language-learning instrument great extramural learner settings.

Язык: Английский

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Fostering AI literacy in pre-service physics teachers: inputs from training and co-variables

Frontiers in Education, Год журнала: 2025, Номер 10

Опубликована: Фев. 20, 2025

Background While the transformative potential of artificial intelligence (AI) in education is widely recognized, rapid evolution these technologies necessitates a corresponding teacher education. This research sought to investigate impact targeted training program on pre-service physics teachers’ AI literacy levels and their subsequent attitudes intentions toward adoption future teaching. Methods A pre-post-test control group quasi-experimental study was implemented among students. 5 weeks long out-of-curriculum intervention designed that combined theoretical grounding with practical, problem-based learning activities, focus use various tools. Results There significant upswing performance post-intervention, showcasing effective facilitating participants’ understanding application educational contexts. Additionally, perceived usefulness found be partial mediator link between scores behavioral intention embed generative solutions into Conclusion The concludes incorporating comprehensive programs curricula essential for fostering technologically adept pedagogically innovatively minded teaching workforce. Further needed explore long-term effects practice student outcomes.

Язык: Английский

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Embracing or rejecting AI? A mixed-method study on undergraduate students’ perceptions of artificial intelligence at a private university in China

Frontiers in Education, Год журнала: 2025, Номер 10

Опубликована: Фев. 21, 2025

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education ‘Pact for Future’ propose that AI can support UNESCO in achieving development goals, especially focusing SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with attitudes toward tools, as well their perceived risks benefits using tools at a private university China. An explanatory sequential mixed-method design was employed an online survey 167 students, followed by qualitative analysis open-ended responses. Data were analyzed one-sample Wilcoxon signed-rank test thematic analysis, supported SPSS ATLAS.ti 25. findings revealed students demonstrated moderate ChatGPT willingness to use them coursework. Positive AI’s value education evident, although concerns such dependence reduced independent thinking, algorithmic bias ethical concerns, accuracy information quality, data security, privacy observed among students. generally viewed positively integration inevitable becoming common academic settings. Students concerned misuse teachers minimal trusted effectively teaching. also benefits, personalized learning, efficiency convenience, career skill development, learning. This contributes discourse higher highlighting nuanced perceptions balancing potential risks. limited small sample size institution. Future research should explore diverse contexts develop comprehensive implementation frameworks

Язык: Английский

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