Professional Development in Education, Год журнала: 2024, Номер unknown, С. 1 - 16
Опубликована: Сен. 27, 2024
Язык: Английский
Sustainability, Год журнала: 2024, Номер 16(3), С. 978 - 978
Опубликована: Янв. 23, 2024
The profound impact of artificial intelligence (AI) on the modes teaching and learning necessitates a reexamination interrelationships among technology, pedagogy, subject matter. Given this context, we endeavor to construct framework for integrating Technological Pedagogical Content Knowledge Artificial Intelligence Technology (Artificial Intelligence—Technological Knowledge, AI-TPACK) aimed at elucidating complex interrelations synergistic effects AI pedagogical methods, subject-specific content in field education. AI-TPACK comprises seven components: (PK), (CK), AI-Technological (AI-TK), (PCK), (AI-TCK), (AI-TPK), itself. We developed an effective structural equation modeling (SEM) approach explore relationships teachers’ knowledge elements through utilization exploratory factor analysis (EFA) confirmatory (CFA). result showed that six all serve as predictive factors variables. However, different varying levels explanatory power relation AI-TPACK. influence core (PK, CK, AI-TK) is indirect, mediated by composite (PCK, AI-TCK, AI-TPK), each playing unique roles. Non-technical have significantly lower teachers compared related technology. Notably, (C) diminishes PCK AI-TCK. This study investigates within its constituent elements. serves comprehensive guide large-scale assessment AI-TPACK, nuanced comprehension interplay contributes deeper understanding generative mechanisms underlying Such insights bear significant implications sustainable development era intelligence.
Язык: Английский
Процитировано
41
Smart Learning Environments, Год журнала: 2023, Номер 10(1)
Опубликована: Ноя. 6, 2023
Abstract Artificial Intelligence (AI) and Machine Learning (ML) technologies offer the potential to support digital content creation media production, providing opportunities for individuals from diverse sociodemographic backgrounds engage in creative activities enhance their multimedia video content. However, less attention has been paid recent research exploring any possible relationships between AI-generated variables of undergraduate students. This study aims investigate multifaceted relationship sociodemographics by examining its implications inclusivity, equity, representation landscape. An empirical about use AI was conducted with a cohort three hundred ninety-eighth ( n = 398) Participants voluntarily took part were tasked conceiving crafting All instruments used combined into single web-based self-report questionnaire that delivered all participants via email. Key findings demonstrate students have favorable disposition when it comes incorporating AI-supported learning tasks. The factors fostering this attitude among include age, number devices they use, time dedicate utilizing technological resources, level experience. Nevertheless, is student’s participation training courses exerts direct impact on students’ ML attitudes, along contentment reliability these technologies. contributes more comprehensive understanding transformative power underscores importance considering instructional contexts policies ensure fair equitable platform backgrounds.
Язык: Английский
Процитировано
31
Computers and Education Open, Год журнала: 2024, Номер 6, С. 100191 - 100191
Опубликована: Май 21, 2024
This study investigates the acceptance and utilization of artificial intelligence (AI) among in-service teachers in Lesotho, focusing on mediating role school support resources (SSR). In Lesotho's educational landscape, which is characterized by a growing interest technology integration, this fills an essential gap existing literature exploring teachers' perspectives AI adoption influence SSR. Using Unified Theory Acceptance Use Technology (UTAUT) as theoretical framework, adopts cross-sectional design, collecting data from sample 315 through online surveys. The was analyzed using maximum likelihood estimation. results reveal substantial positive relationship between perceived usefulness, ease use, attitude towards AI, with SSR playing pivotal complementary mediator these connections. However, identifies non-significant technical proficiency behavioral intention, suggesting need for further investigation into skills effective integration. highlight critical shaping intentions to use their teaching practices. As result, recommends tailored continuous professional development programs collaborative learning communities enhance skills. Additionally, it emphasizes importance advocating policies that integration education underscores ethical considerations related use. We discuss implications our concerning integrating practices schools outline future directions.
Язык: Английский
Процитировано
14
Education and Information Technologies, Год журнала: 2024, Номер unknown
Опубликована: Июль 4, 2024
Abstract Rapid technological advancements of recent decades have fueled, among other aspects, a global boom in the utilization artificial intelligence (AI) tools across variety areas. Higher education, like domains, has embraced these innovations, with ChatGPT emerging as one latest additions. Faculty perception, ability, and willingness to adopt new remain fundamental factors understanding their proliferation adoption. However, it’s equally important strike balance between reaping benefits technology safeguarding well-being faculty members. Against this backdrop, study assesses impact series on adoption university members, taking reference Technology Acceptance Model (TAM). Additionally, we analyze well-being. All hypotheses are tested using covariance-based structural equation modeling (CB-SEM). The findings highlight positive influence perceived usefulness, ease use enjoyment Moreover, seems boost faculty’ happiness energy, while diminishing stress levels. Theoretical practical implications discussed last section.
Язык: Английский
Процитировано
14
International Journal of Artificial Intelligence in Education, Год журнала: 2024, Номер unknown
Опубликована: Окт. 15, 2024
Abstract With growing expectations to use AI-based educational technology (AI-EdTech) improve students’ learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech classrooms. Teachers’ willingness accept vulnerability by integrating into their everyday that is, trust AI-EdTech, will depend on how much they expect it benefit them versus many concerns raises for them. In this study, we surveyed 508 K-12 across six countries four continents understand which teacher characteristics shape teachers’ its proposed antecedents, perceived benefits about AI-EdTech. We examined comprehensive set including demographic professional (age, gender, subject, years experience, etc.), cultural values (Hofstede’s dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), psychological factors (self-efficacy understanding). Using multiple regression analysis, found with higher self-efficacy AI understanding perceive more benefits, fewer concerns, report also differences but no emerged based age, or level education. The findings provide comprehensive, international account associated Efforts raise of, while considering are encouraged support
Язык: Английский
Процитировано
8
Computers and Education Artificial Intelligence, Год журнала: 2025, Номер unknown, С. 100363 - 100363
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Asia Pacific Education Review, Год журнала: 2025, Номер unknown
Опубликована: Янв. 10, 2025
Язык: Английский
Процитировано
1
Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 411 - 438
Опубликована: Янв. 31, 2025
Artificial intelligence in education has changed the learning process by enabling personalised instruction, adaptive assessments, and intelligent tutoring systems. Yet AI also raises enormous ethical concerns, particularly around issues of algorithmic bias fairness. This chapter examines implications education, focusing on understanding mitigation bias. The following section gives an overview types sources systems, strategies for detection bias, important aspects concerning fairness educational contexts. It outlines approaches that need to be taken near future order further enhance AI-powered education. possibilities useful biases makes a case shared framework may help achieve responsible implementation higher
Язык: Английский
Процитировано
1
Learning Media and Technology, Год журнала: 2025, Номер unknown, С. 1 - 14
Опубликована: Фев. 12, 2025
Язык: Английский
Процитировано
1
Education and Information Technologies, Год журнала: 2024, Номер unknown
Опубликована: Май 20, 2024
Abstract This study examines the relation between K-12 teachers’ trust in artificial intelligence (TAI), their knowledge of AI (KAI), and digital competence (DC). It further TAI age, sex, teaching experience International Standard Classification Education (ISCED) levels. The employed a comprehensive validated instrument used sample 211 primary secondary school teachers. results show that there is significant positive all three variables KAI robust substantial predictor TAI. In absence KAI, DC ceases to exist. addition, teachers with different levels do not differences attitudes towards AI. Results independent ISCED level this contributes valuable insights into complex interplay TAI, DC, providing practical implications for policy, teacher preparation professional development rapidly evolving landscape integration education.
Язык: Английский
Процитировано
6