HereS a breakdown of the provided text, focusing on the cited research and key themes:
Overall theme: The text discusses the integration of Artificial Intelligence (AI) in education, specifically focusing on human-AI collaboration in teaching and learning environments. It highlights the necessary adjustments in teaching goals, processes, methods, and the overall learning environment to effectively leverage AI.
Key Areas Discussed:
Teaching goals: Goals need to align with the demands of human-AI collaboration, emphasizing thinking skills. The learning context of students (location, resources, goals) must be considered, and the approach should be adaptable to changes in the learning process.
Teaching Process: Content selection should break down barriers between knowledge production and dissemination, diversifying resources. Content presentation needs to be reasonable and appropriate for students. Accurate feedback and continuous adjustments to teaching methods are crucial.
Teaching Methods: Methods should be versatile and extendable, allowing teachers or AI to provide diverse strategies and models to support learners’ goals. The methods must be suitable for the specific teaching activities.
Collaboration Environment: AI infrastructure and technology are essential for an intelligent teaching environment. Classrooms become a ternary space involving teachers, students, and machines. The environment includes sensor technology and resource platforms. Key factors influencing the collaboration environment are space-time structure, infrastructure, and key technology.Cited Research (with brief summaries):
bredeweg and Kragten (2022): Focuses on the requirements and challenges for hybrid intelligence in education.
Chiu (2021): Advocates for a holistic approach to AI education in K-12 schools.
Christinck and Kaufmann (2017): Discusses facilitating change within transdisciplinary research and sustainability.
Fan and Zhang (2022): Examines a model for teachers’ data intelligence competence in the era of Big Data and AI.
Fang et al. (2022): Explores human-machine collaborative education theory in the age of AI.
Gabriel et al. (2021): Investigates how accountability increases resource sharing between humans and AI systems.
Hakim et al.(2022): Studies the use of robots in situated learning classrooms with immediate feedback to improve student learning.
Järvelä et al. (2023): Examines human and AI collaboration for socially shared regulation in learning.
Kay (2023): Discusses the foundations for human-AI teaming for self-regulated learning with explainable AI (XAI).
Lee and Yeo (2022): Focuses on developing an AI-based chatbot for practicing responsive teaching in mathematics.
Liang et al. (2022): (Cited twice) Discusses precise teaching modes of human-computer collaboration for cultivating computational thinking.
Lu et al. (2020): Discusses the role, awareness, and value of teachers in the era of AI.
Lugrin et al. (2016): Presents a tool for learning classroom management using virtual reality.
Mora et al. (2020): Presents a collaborative working model for enhancing the learning process of science & engineering students.
Shahab et al. (2021): Explores the use of social virtual reality robots for music education for children with autism.
Wienrich and Latoschik (2021): Discusses new prospects of human-AI interaction research.
In essence, the text argues that prosperous integration of AI in education requires a basic rethinking of teaching practices and learning environments, with a focus on collaboration, adaptability, and the specific needs of learners.
