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Cited by 274Teaching and Learning ProgrammingArtificial Intelligence in EducationEducational Robotics and Engineering

Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education

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Tedre, Matti, Toivonen, Tapani, Kahila, Juho, Vartiainen, Henriikka, Valtonen, Teemu, Jormanainen, Ilkka, Pears, Arnold·2021·IEEE Access
DOI 10.1109/access.2021.3097962
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Abstract

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based “traditional” programming is a central aspect and building block in developing next generation computational thinking.

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  • When students lean too heavily on AI-generated code, their ability to develop core competencies such as logical reasoning, collaboration, and debugging may be compromised.

    “When students lean too heavily on AI-generated code, their ability to develop core competencies -such as logical reasoning, collaboration, and debugging -may be compromised (Groothuijsen et al., 2024;Manorat et al., 2025;Tedre et al., 2021).”

    From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Tedre et al., 2021

    The paper’s “Pitfalls and Weaknesses” section warns that low-floor machine-learning tools can produce shallow learning because children learn workflows rather than underlying mechanisms. That supports a general concern about learning through opaque AI tools, but the paper does not address reliance on AI-generated code or say that it compromises logical reasoning, collaboration, or debugging. (LLM, conf=0.76)

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Authors
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A.
Affiliations
  • University of Eastern Finland (FI)
  • KTH Royal Institute of Technology (SE)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
110558-110572
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3097962
OpenAlex
W3192744758
Cited by
274
References
5
Citations by year
2026: 49 · 2025: 84 · 2024: 81 · 2023: 36 · 2022: 23 · 2021: 1
FWCI
29.85
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
January Collective
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
Keywords
machine learning education, K-12 computing education, artificial intelligence in education, computational thinking, rule-based programming, machine learning deployment, teacher agency, educational technology

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