Creative Application of Artificial Intelligence in Education
Bibliographic record
Abstract
Abstract The chapter commences by presenting the emergence of artificial intelligence (AI) in the field of education. It aims to provide an overview of the AI environment in education, highlighting the importance of a nuanced comprehension of its effects, ethical implications, and potential to stimulate innovative teaching methods. The chapter explores the historical background of technological interventions in education and takes a critical approach to examining the potential benefits and drawbacks of AI. It also considers the sociocultural and creative aspects of using AI in education. AI has typically focused on imitating human intelligence. Within the realm of human abilities, we recognise various degrees of creative involvement in AI in education, which demonstrates its capacity to revolutionise learning experiences. At the most advanced stages of creative involvement, we explore the possibilities for collaboration between human intelligence and AI, suggesting a viewpoint of human–AI co-creativity. The chapter also outlines the book's structure, which consists of three main sections: the creative engagement approach, real examples in K-12 education, and advances and prospects in higher education. The different chapters envision not only the acculturation and education of AI, but also the potential of human–AI collaboration to support learners in expressing their unique talents and developing expansive, AI-supported learning initiatives.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".