The Framework for K-12 Science Education (National Research Council, 2012) has set forth an ambitious vision for science learning by integrating disciplinary science ideas [DCIs], scientific and engineering practices [SEPs], and crosscutting concepts [CCCs], so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformations of instruction, curriculum, and assessments from relying on rote memorization of knowledge to knowledge-in-use (Nordine et al., 2018), which has created significant challenges in science education specifically, and STEM education in broad.
This Research Topic specifically seeks contributions using Artificial Intelligence (AI) and machine learning, the most cutting-edge technology to tackle these educational challenges in STEM education. AI is broadly defined as a technology to mimic human cognitive behaviors, and its most potent subcategory, machine learning, has been applied in assessment practices (Zhai et al., 2020a, b), demonstrating the great potential of AI in tackling the most challenging problems in STEM education (Neumann, & Waight, 2020; Zhai, 2021; Zhai, Krajcik, & Pellegrino, 2021). Articles in this research topic must identify challenges in STEM education based on literature and rigorously applied AI in tackling the challenges.
This Research Topic welcomes empirical, conceptual, and review studies. Articles published in this Research Topic must make conceptual and/or empirical contributions to our knowledge about STEM education.
The Framework for K-12 Science Education (National Research Council, 2012) has set forth an ambitious vision for science learning by integrating disciplinary science ideas [DCIs], scientific and engineering practices [SEPs], and crosscutting concepts [CCCs], so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformations of instruction, curriculum, and assessments from relying on rote memorization of knowledge to knowledge-in-use (Nordine et al., 2018), which has created significant challenges in science education specifically, and STEM education in broad.
This Research Topic specifically seeks contributions using Artificial Intelligence (AI) and machine learning, the most cutting-edge technology to tackle these educational challenges in STEM education. AI is broadly defined as a technology to mimic human cognitive behaviors, and its most potent subcategory, machine learning, has been applied in assessment practices (Zhai et al., 2020a, b), demonstrating the great potential of AI in tackling the most challenging problems in STEM education (Neumann, & Waight, 2020; Zhai, 2021; Zhai, Krajcik, & Pellegrino, 2021). Articles in this research topic must identify challenges in STEM education based on literature and rigorously applied AI in tackling the challenges.
This Research Topic welcomes empirical, conceptual, and review studies. Articles published in this Research Topic must make conceptual and/or empirical contributions to our knowledge about STEM education.
