Evidence from various domains underlines the critical role that human factors, and especially trust, play in adopting technology by practitioners. In the case of Artificial Intelligence (AI) powered tools, the issue is even more complex due to practitioners' AI‐specific misconceptions, myths and fears (e.g., mass unemployment and privacy violations). In recent years, AI has been incorporated increasingly into K‐12 education. However, little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology (AI‐EdTech). This paper sheds light on teachers' trust in AI‐EdTech and presents effective professional development strategies to increase teachers' trust and willingness to apply AI‐EdTech in their classrooms. Our experiments with K‐12 science teachers were conducted around their interactions with a specific AI‐powered assessment tool (termed AI‐Grader) using both synthetic and real data. The results indicate that presenting teachers with some explanations of (i) how AI makes decisions, particularly compared to the human experts, and (ii) how AI can complement and give additional strengths to teachers, rather than replacing them, can reduce teachers' concerns and improve their trust in AI‐EdTech. The contribution of this research is threefold. First, it emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. Second, it presents a teacher professional development program (PDP), as well as the discourse analysis of teachers who completed it. Third, based on the results observed, it presents clear suggestions for future PDPs aiming to improve teachers' trust in AI‐EdTech. What is already known about this topic Human factors, and especially trust, play a critical role in practitioners' adoption of technology. In recent years, AI has been incorporated increasingly into K‐12 education. Little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology. What this paper adds This research emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. It presents a teacher professional development program (PDP) to increase teachers' trust in AI‐EdTech, as well as the discourse analysis of teachers who completed it. It presents clear suggestions for future PDPs aiming at improving teachers' trust in AI‐EdTech. Implications for practice and/or policy Pre‐ and in‐service teacher education programs that aim to increase teachers' trust in AI‐EdTech should include a section providing teachers with a basic understanding of AI. PDPs aimed to increase teachers' trust in AI‐EdTech should focus on concrete pedagogical tasks and specific AI‐powered tools that are considered by teachers as helpful and worth the effort to learn. AI‐EdTech should not restr...
Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of AI-based technology by professionals. As AI-based educational technology is increasingly entering K-12 education, it is expected that issues of trust would influence the acceptance of such technology by educators as well, but little is known about this matter. In this work, we bring the opinions and attitudes of science teachers that interacted with several types of AI-based technology for K-12. Among other things, our findings indicate that teachers are reluctant to accept AI-based recommendations when it contradicts their previous knowledge about their students and that they expect AI to be absolutely correct even in situations that absolute truth may not exist (e.g., grading open-ended questions). The purpose of this paper is to provide initial findings and start mapping the terrain of this aspect of teacher-AI interaction, which is critical for the wide and effective deployment of AIED technologies in K-12 education.
As scientific writing is an important 21st century skill, its development is a major goal in high school science education. Research shows that developing scientific writing skills requires frequent and tailored feedback, which teachers, who face large classes and limited time for personalized instruction, struggle to give. Natural Language Processing (NLP) technologies offer great promise to assist teachers in thisprocess by automating some of the analysis. However, in Hebrew, the use of NLP in computer-supported writing instruction was until recently hindered by the lack of publicly available resources. In this paper, we present initial results from a study that aims to develop NLP-based techniques to assist teachers in providing personalized feedback in scientific writing in Hebrew, which might be applicable to otherlanguages as well. We focus on writing inquiry reports in Biology, and specifically, on the task of automatically identifying whether the report contains a properly de?ned research question. This serves as a proof-of-concept of whether we can build a pipeline that identifies major components of the report and match them to a predefined grading rubric. To achieve this, we collected several hundreds of reports, annotated them according to a grading rubric to create a supervised data set, and built a machine-learning algorithm that uses NLP-based features. The results show that our model can accurately identify the research question or its absence.To the best of our knowledge, this is the first paper to report on the application of Hebrew NLP for formative assessment in K-12 science education.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.