Ethical principles for artificial intelligence in K-12 education
Why this work is in the frame
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Bibliographic record
Abstract
Advances in Artificial Intelligence in Education (AIED) are providing teachers with a wealth of new tools and smart services to facilitate student learning. Meanwhile, growing public concern over the potentially harmful societal effects of AI has prompted the publication of a flurry of AI ethics guidelines and policy documents authored by national and international government agencies, academic consortia and industrial stakeholders. AI ethics policy guidance specific to children and K-12 education1 has lagged behind; this scene is swiftly changing. In this paper, we examine the ethical principles currently informing AI ethics policy development for children and K-12 education. To accomplish this, we located four recent and globally relevant Artificial Intelligence in K-12 Education (AIEdK-12) ethics guideline statements; we then performed a content analysis of these documents using eleven AI ethics principles identified by Jobin et al. (2019)Jobin et al. (2019). We found that these AIEdK-12 ethics guidelines employed many of the core principles already employed in non-AIEdK-12 documents—Transparency; Justice and Fairness; Non-maleficence; Responsibility; Privacy; Beneficence; Freedom & Autonomy—and were sometimes adapted for children. We further identified four new ethical principles being employed that are unique to K-12 education, specifically: Pedagogical Appropriateness; Children's Rights; AI Literacy; and Teacher Well-being. Our analysis also calls for a decolonized “humanized posthuman” ethic able to address the intensifying human-AI collaborative environment in classrooms, and able to weigh the complex indications and contraindications for children's and youth's cognitive, social-emotional, physical, cultural and political development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it