Generative Artificial Intelligence for Science Teacher Education enhancement: Framework Development

DOI https://doi.org/10.54499/2023.13203.PEX

Reference 2023.13203.PEX

Project Start Date 2025-02-01 | Project End Date 2026-07-31

Principal Investigator: Margarida Morais Marques 0000-0002-4325-9122

Knowledge Fields: Social sciences

Funding Program: Concurso de Projetos Exploratórios em Todos os Domínios Científicos 2023

Abstract

Generative Artificial Intelligence (GAI) is widely disseminated in society. In science, GAI accelerates productivity by supporting idea and hypothesis generation, data analysis, experiment design, and research paper writing, despite concerns about the accuracy of information and issues of authorship and ownership. In education, it presents opportunities, such as personalised learning and reducing teacher workload, and challenges, such as balancing the benefits of GAI with students’ independent problem-solving skills or the risk of strengthening the digital divide. Thus, educational stakeholders need to evaluate GAI’s capabilities, without overlooking its possible risks and undesirable impacts on education and society. Technological Pedagogical Content Knowledge (TPACK) is a mature theoretical framework on teacher knowledge for intelligent and intentional technology integration in teaching. It was recently revised to empower teachers to integrate GAI. This technology induces a philosophical shift in TPACK nature, from viewing it as a tool to recognizing it as a non-human unreliable collaborator. Moreover, as GAI transforms society, Contextual Knowledge (XK) is expanded in scope, from immediate school systems to the broader educational system and society, also considering its long-term effects. Yet, this framework is focused on WHAT teachers must know to leverage GAI effectively and not on HOW that can be done, particularly in science education. Other frameworks can be explored to understand GAI integration in educational contexts, such as Technology Acceptance Model (TAM) or Substitution, Augmentation, Modification, Redefinition (SAMR).

The literature highlights a gap in GAI exploration within science teacher initial education, despite teachers’ increasing need to become knowledgeable in this technology. The scarcity of GAI-learning opportunities in teacher initial education makes the development of GAI-supported education a priority. Thus, the GAI-SciTeach project’s main aim is to foster conditions so that GAI empowers and not overpowers educators and learners, through the development of a pedagogical framework tailored for the effective integration of GAI in science teacher initial education. The project is guided by the research question : How can teacher initial education integrate GAI technology towards practice innovation for student learning enhancement, tailored to the specific context and goals of science education?

Under a pragmatic paradigm, mixed methods  are used to conduct the first cycle of design-based research  to achieve a practical solution and a theoretical contribution regarding a pedagogical intervention toward the integration of GAI in science teacher education programs. The framework is literature-funded  and takes 2023 TPACK as a starting point. It is negotiated among several stakeholders from different backgrounds: science education (e.g. future teachers and teacher educators), artificial intelligence (computer science researchers), and educational technology, to guarantee it is tailored to the specificities of science teacher education. Under the first design cycle, the framework is piloted, with data collection through observation, interviews with pilot participants, and document analysis of future teachers’ work under the piloted teacher education intervention leveraging GAI technology. This provides empirical evidence of the effectiveness of the framework and signals elements for future improvement, supporting the construction of a robust reference framework.

GAI-SciTeach embraces research and training, and its feasibility is supported by a multidisciplinary team from three research units with know-how in science education, science teacher education, educational technology, and artificial intelligence.

The project foresees two main results: 1) pedagogic framework – a literature-based pedagogical framework tailored for the effective and ethical integration of GAI into science teacher initial education with guidelines for stakeholders on the procedural dimension (HOW), taking into account contextual factors; and 2) technology-enhanced intervention  – a science teacher initial education intervention integrating GAI towards practices innovation for student learning enhancement. GAI-SciTeach novelty lies in its comprehensive, stakeholder-driven approach to developing a practical and ethical framework towards the integration of GAI into science teacher initial education, thus promoting innovation in science education practices and supporting student performance enhancement. By focusing the framework on procedural knowledge, engaging diverse stakeholders, employing a design-based research approach, addressing a significant gap in the field, and emphasizing ethical considerations, the project sets a new standard for the integration of emergent educational technologies into teacher education.

Institution

Universidade de Aveiro (UA)

Funding