Manifesto in Defence of Human-Centred Education in the Age of Artificial Intelligence
Bibliographic record
Abstract
Abstract This manifesto advocates for the thoughtful integration of AI in education, emphasising a human-centred approach amid the rapid evolution of artificial intelligence (AI). The chapter explores the transformative potential of large language models (LLM) and generative AI (GenAI) in education, addressing both opportunities and concerns. While AI accelerates change in education, adapting to students’ diverse learning needs, it also poses challenges to traditional assessment paradigms. The manifesto stresses the importance of empowering teachers and students as decision-makers, highlighting the need for a balanced approach to AI integration. It emphasises human-centricity in AI use, promoting ethical considerations, responsible practices, and regulations. The right to choose and co-create is underscored, giving autonomy to educators and learners in selecting technologies aligned with their philosophies. Additionally, the manifesto introduces the concept of hybrid intelligence (HI), advocating collaboration between human and machine intelligence to enhance educational experiences. The manifesto encourages creative uses of AI in education, envisioning a harmonious partnership where AI and humans co-create transformative knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".