Teachers’ readiness and intention to teach artificial intelligence in schools

Ayanwale, Musa Adekunle; Sanusi, Ismaila Temitayo; Adelana, Owolabi Paul; Aruleba, Kehinde; Oyelere, Solomon Sunday

doi:10.1016/j.caeai.2022.100099

Cited by 75 publications

(70 citation statements)

References 35 publications

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“…Overall, the teachers from the study seemed to have a basic level of digital skills but low AI-related skills [31]. Another study with Nigerian teachers emphasized that the willingness and readiness of teachers to promote AI are key prerequisites for the integration of AI-based technologies in education [32]. Along the same lines, the results of a study with teachers from South Korea indicate that teachers with constructivist beliefs are more likely to integrate educational AI-based tools than teachers with transmissive orientations [33].…”

Section: Review Of Research Applying Large Languagementioning

confidence: 89%

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci¹,

Seßler²,

Küchemann³

et al. 2023

Preprint

123

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Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

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Section: Review Of Research Applying Large Languagementioning

confidence: 89%

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci¹,

Seßler²,

Küchemann³

et al. 2023

Preprint

123

View full text Add to dashboard Cite

show abstract

“…Exploring teachers' readiness to teach ML in classrooms is imperative since teachers' acceptance and disposition could be a pointer to their interest in teaching technology and impact their teaching practices (Nikolopoulou et al, 2021). This assumption is corroborated by a recent study of Ayanwale et al (2022) who assert that the success of AI education is closely dependent on teachers' readiness and appreciation towards the subject. Based on the above information, teachers cooperation can be ascertained when they are empowerd with the knowledge and approaches to be an advocate of ML in schools.…”

Section: Discussionmentioning

confidence: 96%

A systematic review of teaching and learning machine learning in K-12 education

et al. 2022

Self Cite

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The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

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“…A composite-based structural equation model was used for the analysis. This was chosen because it can handle complex models with numerous indicators, constructs, and relationship models without identification problems (Ayanwale et al, 2022). Partial least square structural equation modeling (PLS-SEM) includes both a measurement model and a structural model analysis.…”

Section: Discussionmentioning

confidence: 99%

Untitled

2023

PROSOC

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Electronic learning (e-learning) is a broader approach to learning that brings new opportunities for teaching and learning in many fields of education compared to the face-to-face classroom environment. The purpose of this study is to explore high school teachers' acceptance to continue teaching through e-learning platforms after COVID-19. Based on the framework of a unified theory of technology acceptance and use of technology, this study investigates the research questions in the context of Lesotho. The study adopted a quantitative approach, with a total number of 300 questionnaires collected from high school teachers. The result was analyzed using composite structural equation modeling. According to the study, instructors' acceptance of e-learning is influenced by several factors, which in turn has a significant impact on teachers' behavioral intentions in Lesotho. As a result of the study, individual challenges associated with using e-learning platforms were identified.

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Teachers’ readiness and intention to teach artificial intelligence in schools

Cited by 75 publications

References 35 publications

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

A systematic review of teaching and learning machine learning in K-12 education

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