Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 610 - 620
Published: Jan. 1, 2025
Language: Английский
Heliyon, Journal Year: 2024, Volume and Issue: 10(11), P. e31887 - e31887
Published: May 25, 2024
AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand such technologies, there remains a significant research gap regarding factors influencing users' acceptance adoption of educational contexts. This study aims to address this by investigating that shape attitudes, intentions, behaviors towards adopting ChatGPT smart systems. employed quantitative approach, data were collected from 458 participants through structured questionnaire designed measure various constructs related technology acceptance, including perceived ease use, usefulness, feedback quality, assessment subject norms, attitude behavioral intention use ChatGPT. Structural model analysis (SEM) Statistical techniques then utilized examine relationships between these constructs. The findings revealed Perceived usefulness emerged as predictors attitudes education. Additionally, norms found positively influence intentions purposes. Moreover, significantly proved actual few hypotheses, relationship trust not supported data. contributes existing body information systems applications determining factor context.
Language: Английский
Citations
43
Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(14), P. 18695 - 18744
Published: March 14, 2024
Language: Английский
Citations
38
SAGE Open, Journal Year: 2024, Volume and Issue: 14(1)
Published: Jan. 1, 2024
This systematic review evaluates the application of Unified Theory Acceptance and Use Technology (UTAUT) model in higher education, analyzing 162 SSCI/SCI-E articles from 2008 to 2022. It reveals a predominant focus on student participants Asia North America. Mobile learning tools are most studied technologies. Surveys continue be top data gathering method, while structural equation modeling is preferred for analysis. The Model combined with UTAUT. UTAUT testing shows performance expectancy has strongest sway behavioral intention. Additionally, underscores nuanced variances impact factors between education general contexts. study calls future applications must promote inclusive research spanning diverse groups, mixed methodologies theoretical perspectives. comprehensive approach imperative fully understand technology adoption patterns enable context-specific integration strategies.
Language: Английский
Citations
35
International Journal of Educational Technology in Higher Education, Journal Year: 2024, Volume and Issue: 21(1)
Published: July 30, 2024
Abstract As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, significant importance, particularly education. This study investigates awareness, acceptance, and adoption a state-of-the-art developed higher education institutions across China. applies partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study’s conceptual framework integrates key determinants Technology Acceptance Model (TAM) extends it incorporating perceived as critical factor process. findings reveal that ChatGPT awareness significantly influences intention adopt ChatGPT. Perceived ease use, usefulness, mediate association between Additionally, trust moderates relationship intelligence. Moving forward, order maintain students’ thinking skills inventiveness their assessment writing, assessments must promote safe use Therefore, educators will be crucial ensuring are used ethically suitably providing clear guidelines instructions.
Language: Английский
Citations
34
International Journal of Human-Computer Interaction, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23
Published: March 8, 2024
The study aims to explore the factors that influence university students' behavioral intention (BI) and use behavior (UB) of generative AI products from an ethical perspective. Referring decision-making theory, research model extends UTAUT2 with three influencing factors: awareness (EA), perceived risks (PER), anxiety (AIEA). A sample 226 students was analysed using Partial Least Squares Structural Equation Modelling technique (PLS-SEM). results further validate effectiveness UTAUT2. Furthermore, performance expectancy, hedonistic motivation, price value, social all positively BI products, except for effort expectancy. Facilitating conditions habit show no significant impact on BI, but they can determine UB. extended perspective play roles as well. AIEA PER are not key determinants BI. However, directly inhibit From mediation analysis, although do have a direct UB, it inhibits UB indirectly through AIEA. Ethical Nevertheless, also increase PER. These findings help better accept ethically products.
Language: Английский
Citations
26
Education and Information Technologies, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 24, 2024
Language: Английский
Citations
23
Smart Learning Environments, Journal Year: 2025, Volume and Issue: 12(1)
Published: Jan. 3, 2025
Abstract This review was conducted in order to determine the specific role of intelligent technologies individual learning experience. The research work included consider articles published between 2014 and 2024, found Web Science, Scopus, ERIC databases, selected among 933 мarticles on topic. Materials were checked for compliance with criteria headings, annotations full texts then further analyzed. study includes 38 that based a rigorous evaluation selection process accordance PRISMA methodology AMSTAR2 critical assessment strategy. As result analysis, it scope application education is diverse, results this topic are heterogeneous. article identifies aspects effective use education, emerging difficulties limitations, as well provides examples successful implementation various educational institutions. Although there advantages using smart general, we should not ignore what needs be considered. On point, presents arise when ways prevent them.
Language: Английский
Citations
2
International Journal of Human-Computer Interaction, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22
Published: June 7, 2024
Artificial intelligence generates vibrant characters, encompassing teachers, peer students, and advisors within diverse educational media. However, the impact of perceived embodiment such characters in language learning videos on students' technology acceptance adoption is unclear. Integrating structural equation modeling into thematic analysis, this study analyzes 1042 valid responses from higher education students to bridge research gap. Our reveals that four subdimensions (human-likeness, credibility, facilitation, engagement) significantly positively predict higher-education ease use usefulness artificial intelligence-generated virtual teachers videos. Notably, an exception arises, as human-likeness does not our context. Students' systemic interactivity process emerge pivotal mediators. The qualitative analysis identifies concerns about classroom administration, developmental support, technical issues, deprived interpersonal collaboration, liberal attainment cultivation with teacher presence. This can illuminate designs applications education.
Language: Английский
Citations
13
Education and Information Technologies, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 23, 2024
Language: Английский
Citations
9
BMC Psychology, Journal Year: 2025, Volume and Issue: 13(1)
Published: Jan. 4, 2025
In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates psychosocial factors influencing AI among Peruvian university students and uses an extended UTAUT2 model to examine constructs that may impact acceptance use. employed a quantitative approach with survey-based design. A total 482 from public private universities Peru participated research. The utilized partial least squares structural equation modeling (PLS-SEM) analyze data test hypothesized relationships between constructs. findings revealed three out six significantly influenced students. Performance expectancy (β = 0.274), social influence 0.355), learning self-efficacy 0.431) were found have significant positive effects on adoption. contrast expectations, ethical awareness, perceived playfulness, readiness anxiety did not impacts appropriation this context. highlights importance practical benefits, context, self-confidence within These contribute understanding diverse educational settings provide framework for developing effective implementation strategies education institutions. results can guide policymakers creating targeted approaches enhance integration academic environments, focusing demonstrating value AI, leveraging networks, building students' confidence their ability learn use technologies.
Language: Английский
Citations
1