In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education

Abramowitz, Brian; Antonenko, Pavlo D.

doi:10.1080/15391523.2022.2119450

Cited by 30 publications

(7 citation statements)

References 20 publications

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“…AI is already an important part of science learning environments and a tool being used in many different ways by learners and teachers (e.g., Cross, 2023). While there are certainly some science education researchers responding to the AI revolution (e.g., Antonenko & Bramowitz, 2023), we think, that as a whole, the science education research community is not as far along as it needs to be in terms of understanding, theorizing, and studying the intersections of AI and science education.…”

Section: Advancing Discourse About Ai In Science Educationmentioning

confidence: 98%

Artificial intelligence and the Journal of Research in Science Teaching

Sadler,

Mensah,

Tam

2024

J Res Sci Teach

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Artificial Intelligence (AI) is a transformative technology that promises to impact many aspects of society including research, education, and publishing. We, the editors of the Journal of Research in Science Teaching (JRST), think that the journal has a responsibility to contribute to the ongoing dialogues about the use of AI in research and publishing with particular attention to the field of science education. We use this editorial to share our current ideas about the opportunities and challenges associated with AI in science education research and to sketch out new journal guidelines related to the use of AI for the production of JRST articles. We also extend an invitation to scholars to submit research articles and commentaries that advance the field's understanding of the intersections of AI and science education.Establishing foundations for an AI revolution has been in progress since the mid-twentieth century (Adamopoulou & Moussiades, 2020), and a giant step in public engagement with AI was taken in November 2022 when OpenAI released ChatGPT. This tool along with other large language models (LLM) such as Google Bard, and Microsoft's Copilot, provide platforms that are easy to use and can generate content such as text, images, computer code, audio, and video. It has quickly become apparent that these generative AI tools have the potential to change education in substantial ways. There is already evidence that students and teachers are actively using AI in ways that will push the field of education to reconsider what it means to construct learning artifacts, how to assess the work of learners, and the nature of learner-technology interactions (e.g., Prather et al., 2023). Of course, generative AI will not just impact the work of students, teachers, and other educational practitioners, it will affect how research is conducted and reported. As journal editors, we are particularly interested in the use of AI in the sharing of research and publication processes.Across the field of education research, and science education research more specifically, scholars use a host of technologies to support their work. For example, researchers regularly use statistical packages to derive quantitative patterns in data, qualitative software to organize and represent coded themes in data, grammar, and spelling check software embedded in word processors and online (i.e., Grammarly), and reference managers to find and cite literature. Technologies such as these examples are ubiquitous across our field, and new generative AI presents another set of tools that researchers might leverage for the sharing of their scholarship. However, the now widely available LLMs seem, to us, to represent a fundamental shift in technological capacity for producing research publications. The users of software for data analysis, reference management, and grammar checks exert levels of control and supervision over these technologies, which is not the case when using an LLM. There is a much greater degree of opaqueness and uncertainty when it comes to...

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Section: Advancing Discourse About Ai In Science Educationmentioning

confidence: 98%

Artificial intelligence and the Journal of Research in Science Teaching

Sadler,

Mensah,

Tam

2024

J Res Sci Teach

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“…But arguably, it is the text generating capacity of GPT models that has attracted the most attention so far for their ability to mimic human writing. In academia, this attention has caused considerable anxiety, concern, and prognostication about the potential, often negatively conceived, impacts of these tools on knowledge creation, academic work, and the integrity of the academic enterprise [9]. These fears make the sector vulnerable to the claims of the technology snake oil industry, which offers techno-solutionism targeted at those fears, but which often does not live up to its promises.…”

Section: What Is Generative Artificial Intelligence?mentioning

confidence: 99%

Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice

Farrelly,

Baker

2023

Education Sciences

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Generative Artificial Intelligence (GAI) has emerged as a transformative force in higher education, offering both challenges and opportunities. This paper explores the multifaceted impact of GAI on academic work, with a focus on student life and, in particular, the implications for international students. While GAI, exemplified by models like ChatGPT, has the potential to revolutionize education, concerns about academic integrity have arisen, leading to debates on the use of AI detection tools. This essay highlights the difficulties in reliably detecting AI-generated content, raising concerns about potential false accusations against students. It also discusses biases within AI models, emphasizing the need for fairness and equity in AI-based assessments with a particular emphasis on the disproportionate impact of GAI on international students, who already face biases and discrimination. It also highlights the potential for AI to mitigate some of these challenges by providing language support and accessibility features. Finally, this essay acknowledges the disruptive potential of GAI in higher education and calls for a balanced approach that addresses both the challenges and opportunities it presents by emphasizing the importance of AI literacy and ethical considerations in adopting AI technologies to ensure equitable access and positive outcomes for all students. We offer a coda to Ng et al.’s AI competency framework, mapped to the Revised Bloom’s Taxonomy, through a lens of cultural competence with AI as a means of supporting educators to use these tools equitably in their teaching.

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“…There is often confusion with AI benefits and what it can be used for (Antonenko & Abramowitz, 2023). Not only could they save time and effort with planning and administration, but it will have an impact on the teaching, learning and assessment going forward, including new creative ways to use the technology (Nerantzi et al, 2023).…”

Section: Reflection On Implicationsmentioning

confidence: 99%

Leveraging Generative AI Tools for Enhanced Lesson Planning in Initial Teacher Education at Post Primary

Kehoe

2023

telji

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The rapid development of generative AI (artificial intelligence) tools such as ChatGPT and Google Bard has opened new possibilities for enhancing lesson planning in initial teacher education (ITE). These tools have the capability to generate tailored educational content, alleviating time constraints while concurrently enhancing the quality of teaching. By simply providing specific requirements and objectives, teachers can obtain comprehensive and well-structured lesson plans and subject plans. This paper explores the potential of generative AI tools to revolutionise lesson planning in initial teacher education. It begins by reviewing lesson planning using a generative AI tool, highlighting the challenges and opportunities that exist. A sample lesson plan and a sample scheme of work are further created. While these tools are revolutionising the way teachers work, these tools will not replace real human teachers. Teachers will always need to supplement generative AI content with their own insights and experience, allowing them to make informed pedagogical decisions.

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In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education

Cited by 30 publications

References 20 publications

Artificial intelligence and the Journal of Research in Science Teaching

Artificial intelligence and the Journal of Research in Science Teaching

Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice

Leveraging Generative AI Tools for Enhanced Lesson Planning in Initial Teacher Education at Post Primary

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