Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements

Ning, Yimin; Zhang, Cheng; Xu, Binyan; Zhou, Ying; Wijaya, Tommy Tanu

doi:10.3390/su16030978

Cited by 23 publications

(2 citation statements)

References 97 publications

(115 reference statements)

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“…If content knowledge is used to use its PCK and TCK capabilities, then it will impact TPACK. These findings contrast significantly, ly from those of a study by (Ning et al, 2024)which found that teacher TPACK is impacted both directly and indirectly by the CK, PK, and PCK components.…”

Section: Examining Variant Test Of Determination Analysiscontrasting

confidence: 91%

Analyze Variable TPACK (Technological, Pedagogical, and Content Knowledge) in Digital Learning: A Review

Menrisal

2023

J. Dig. Learning and Dist. Edu

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In particular, the Technological Knowledge (TK), Pedagogical Knowledge (PK), Content Knowledge (CK), Pedagogical Content Knowledge (PCK), Technological Pedagogical Knowledge (TPK), and Technological Content Knowledge (TCK) variables are the focus of this study, which attempts to ascertain the relationship between them. and Knowledge of Technological Pedagogical Content (TPACK). The Preferred Reporting Items for A review method used in this study. The study's findings that the TK on TCK, TK on TPK, TK on TPACK, and TPK on TPACK have a positive influence on each other. However, two relationships—the CK on TPACK and the PK variable on TPACK—do not have a positive or significant effect. Ten of the twelve hypothesized variables have a positive influence, while the other two have no discernible effect. It is intended that the research would improve teachers' TPACK skills, particularly in pedagogical and subject knowledge. Tags: Guru skills, TPACK, SmartPls

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Section: Examining Variant Test Of Determination Analysiscontrasting

confidence: 91%

Analyze Variable TPACK (Technological, Pedagogical, and Content Knowledge) in Digital Learning: A Review

Menrisal

2023

J. Dig. Learning and Dist. Edu

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“…This is supported by previous studies, indicating that a profound understanding of education for sustainability, coupled with ICT tools underpinned by transformative teaching and learning, bolsters the creation of contextualized curricula and enriches the teaching and learning experience [75,76], especially through multidisciplinary projects [77]. These results also suggest the need to cultivate teachers' digital competence that reflects the Technological Pedagogical Content Knowledge (TPACK) methodology [78], especially taking into consideration the synergistic effects of recent advances in AI technology, supported by innovative pedagogical methods, and contextualized with multiple subject-specific content in the broader field of education; this is termed AI-TPACK [79].…”

Section: Discussionmentioning

confidence: 98%

Predicting Teacher’s Information and Communication Technology-Enabled Education for Sustainability Self-Efficacy

Ghazali,

Makrakis,

Kostoulas-Makrakis

et al. 2024

Sustainability

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This study focused on the development of a teacher self-efficacy measurement addressing the contextualization of information and communication technologies (ICTs) with education for sustainability (EfS) using principal component analysis. Furthermore, this study, with the participation of 1815 teachers, examined the predictive value of some hypothesized predictors of the ICTeEfS self-efficacy construct such as gender, school setting, years of teaching, knowledge of education for sustainability, knowledge of ICTs, and experience in using ICTs to support the integration of education for sustainability in teaching and school curricula using multiple regression analysis. The research results revealed that gender did not explain any statistically significant variance of teachers’ ICTeEfS self-efficacy; contrary to this, teachers possessing a high level of knowledge on issues about sustainability and ICT competence explained most of the extracted variance. However, a gap remains in utilizing these skills pedagogically. This study also discusses the varying levels of self-efficacy among teachers based on their workplace location, finding that urban teachers demonstrate higher self-efficacy compared to their rural counterparts. This could be attributed to the disparities in resources and support systems, thereby affecting their capacity to employ ICT in EfS effectively. It was also found that novice teachers exhibited higher predictive power to ICTeEfS self-efficacy, possibly due to their recent exposure to ICT training. This study assumes that a profound understanding of EfS, coupled with ICT tools, bolsters the creation of contextualized curricula and enriches the teaching and learning experience towards sustainability.

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Teachers' perceptions, attitudes, and acceptance of artificial intelligence (AI) educational learning tools: An exploratory study on AI literacy for young students

Yim,

Wegerif

2024

Future in Educational Research

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Artificial intelligence (AI) literacy education for young students is gaining traction among researchers and educators. Researchers are developing courses and attempting to teach AI literacy to younger students, using age‐appropriate AI educational learning tools. Although teachers play a crucial role in AI literacy education, their perceptions and attitudes have received little attention. This study explores the perceptions of 60 teachers regarding the use of AI educational learning tools, and examines the factors influencing their attitudes in relation to implementing AI literacy education. The technological acceptance model and the technological, pedagogical, and content knowledge (CK) (TPACK) framework inform the research design, and a mixed method, combining the statistical package for Social Science and thematic analysis, is employed for data analysis. The study reveals that teachers have positive perceptions regarding the usefulness and ease of use of AI educational learning tools in their AI literacy teaching. This paper also reveals that teachers embrace an arts‐based approach to teaching AI literacy. The qualitative data reveal that teachers face challenges such as insufficient CK and experience with AI; and knowledge of TPACK. The five factors affecting their acceptance of AI educational learning tools are: (a) teachers' perceptions of their AI CK and experience in teaching AI literacy (technological content knowledge); (b) technical challenges and stakeholder acceptance; (c) the attributes of AI educational learning tools; (d) school infrastructure and budget constraints; and (e) potential for distraction and negative emotional responses. This study offers insights for policymakers regarding professional development initiatives and technical support mechanisms, thereby facilitating more effective AI literacy implementation.

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Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements

Cited by 23 publications

References 97 publications

Analyze Variable TPACK (Technological, Pedagogical, and Content Knowledge) in Digital Learning: A Review

Analyze Variable TPACK (Technological, Pedagogical, and Content Knowledge) in Digital Learning: A Review

Predicting Teacher’s Information and Communication Technology-Enabled Education for Sustainability Self-Efficacy

Teachers' perceptions, attitudes, and acceptance of artificial intelligence (AI) educational learning tools: An exploratory study on AI literacy for young students

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