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All Journal Schrödinger: Journal of Physics Education Journal of Research in Mathematics, Science, and Technology Education
Kotsis, Konstantinos T.
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Education Mathematics Social Sciences Other

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Inquiry-Based Learning in Science: Mathematical Reasoning’s Support of Critical Thinking Kotsis, Konstantinos T.
Journal of Research in Mathematics, Science, and Technology Education Vol. 2 No. 1 (2025): Journal of Research in Mathematics, Science, and Technology Education
Publisher : Scientia Publica Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70232/jrmste.v2i1.35

Abstract

The evolution of educational paradigms toward inquiry-based learning (IBL) in science education has significantly altered pedagogical practices by emphasizing the necessity of active student participation in the learning process. Through questioning, research, and problem-solving, IBL invites students to investigate scientific phenomena, so promoting a closer knowledge retention and understanding. Integral to this approach is the function of mathematical thinking, which functions not only as a tool for quantitative analysis but also as a basic framework for critical thinking. Mathematical reasoning helps students organize their searches, efficiently analyze data, and come to reasonable conclusions. This interaction improves their ability to link mathematical ideas and scientific concepts, developing more advanced higher-order thinking abilities. Understanding how mathematical reasoning supports critical thinking within IBL will help curriculum development and teaching strategies in science education be much more informed as education progressively prioritizes integrating multidisciplinary approaches. In science education especially, IBL is especially helpful since it fits very nicely with the Next Generation Science Standards, which support student-centered learning environments that advance critical thinking and teamwork. IBL helps students develop critical skills, including analytical thinking and reasoning, by pushing them to create their own questions and search for answers through investigation. Moreover, including mathematical reasoning in IBL improves students’ problem-solving capacity by letting them approach challenging scientific questions with a strong methodological framework. IBL thus not only fosters curiosity but also provides the cognitive tools required for advanced learning in scientific fields.
Artificial Intelligence for Physics Education in STEM Classrooms: A Narrative Review within a Pedagogy Technology Policy Framework Kotsis, Konstantinos T.
Schrödinger: Journal of Physics Education Vol. 6 No. 3 (2025): September
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/sjpe.v6i3.2148

Abstract

Purpose of the study: This study seeks to consolidate existing global research on the incorporation of Artificial Intelligence (AI) into school-level STEM education, with a particular emphasis on physics teaching and learning in primary and secondary settings, to delineate principal trends, recognize emerging opportunities, and underscore ongoing challenges in pedagogy and learning. Methodology: A narrative literature review was performed utilizing Google Scholar and Scopus to identify significant studies published from 2015 to 2025. The selection emphasized peer-reviewed journal articles and conference proceedings that concentrate on the pedagogical, technological, and policy aspects of AI in STEM education. Main Findings: The analysis indicates that artificial intelligence is transforming STEM education via intelligent tutoring systems, adaptive learning platforms, automated assessments, and virtual laboratories. These technologies improve personalization, engagement, and inquiry-based learning, yet they also present ethical dilemmas concerning bias, privacy, and equity. A novel conceptual framework that integrates pedagogy, technology, and policies is proposed to direct future research and practice. Novelty/Originality of this study: This study presents a novel three-dimensional framework that interconnects pedagogy, technology, and policy as mutually reinforcing components in AI-enhanced STEM education. The model presents a novel analytical framework for assessing existing initiatives and outlines a strategy for creating inclusive and sustainable AI-enhanced learning environments.
Artificial Intelligence for Physics Education in STEM Classrooms: A Narrative Review within a Pedagogy Technology Policy Framework Kotsis, Konstantinos T.
Schrödinger: Journal of Physics Education Vol. 6 No. 3 (2025): September
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/sjpe.v6i3.2148

Abstract

Purpose of the study: This study seeks to consolidate existing global research on the incorporation of Artificial Intelligence (AI) into school-level STEM education, with a particular emphasis on physics teaching and learning in primary and secondary settings, to delineate principal trends, recognize emerging opportunities, and underscore ongoing challenges in pedagogy and learning. Methodology: A narrative literature review was performed utilizing Google Scholar and Scopus to identify significant studies published from 2015 to 2025. The selection emphasized peer-reviewed journal articles and conference proceedings that concentrate on the pedagogical, technological, and policy aspects of AI in STEM education. Main Findings: The analysis indicates that artificial intelligence is transforming STEM education via intelligent tutoring systems, adaptive learning platforms, automated assessments, and virtual laboratories. These technologies improve personalization, engagement, and inquiry-based learning, yet they also present ethical dilemmas concerning bias, privacy, and equity. A novel conceptual framework that integrates pedagogy, technology, and policies is proposed to direct future research and practice. Novelty/Originality of this study: This study presents a novel three-dimensional framework that interconnects pedagogy, technology, and policy as mutually reinforcing components in AI-enhanced STEM education. The model presents a novel analytical framework for assessing existing initiatives and outlines a strategy for creating inclusive and sustainable AI-enhanced learning environments.
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