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The Turing Teacher: Identifying core attributes for AI learning in K-12
Alexander Pelaez1, Amal Jacobson2, Kara Trias3
1Information Systems and Business Analytics, Hofstra University, Hempstead, NY, United States.
Artificial intelligence (AI) in K-12 education faces challenges, but developing a "Turing Teacher" requires attributes addressing diverse student needs and overcoming negative tech perceptions. This AI must collaborate with educators to enhance learning.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- K-12 Learning
Background:
- AI applications in education present unique challenges within the K-12 environment.
- Teaching primary and secondary students requires more than fact recall, demanding adaptive AI solutions.
- Existing research on AI and online education in K-12 informs the development of effective AI tools.
Purpose of the Study:
- To explore hurdles faced by AI applications in K-12 teaching.
- To define core attributes for an AI-powered learning technology, termed the "Turing Teacher."
- To provide a foundation for future research in AI-enhanced K-12 education.
Main Methods:
- A survey with qualitative responses was conducted during the Covid Pandemic's online learning implementation.
- Univariate and multivariate tests were used to analyze quantitative survey data.
- Qualitative responses were analyzed to identify essential attributes for AI teaching technology.
Main Results:
- AI technology must overcome negative sentiments towards technology in educational settings.
- Identified core attributes for AI teaching tools must consider perspectives from teachers, parents, and students.
- Challenges inherent in educational technology implementation were highlighted.
Conclusions:
- The "Turing Teacher" concept requires AI to adapt, collaborate with human teachers, and meet varied student needs.
- AI in education can potentially bridge the digital divide in underserved communities.
- Further research is needed to optimize AI's role in K-12 education.
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