SUPERVISION AND TEACHING IN EDUCATION: HISTORICAL DEVELOPMENT AND CONTEMPORARY TRANSFORMATION IN THE DIGITAL AGE
Bibliographic record
Abstract
This study examines the meeting of pedagogical thought and technological innovation, focusing on the role of artificial intelligence in the evolution of education and the necessary reconfiguration of pedagogical practice within the contemporary European context. From a historical perspective, education has always been a field of relationships and dialogue, with technology emerging in various periods as a tool for reorganization rather than a replacement of the human factor. The European history of education, shaped by successive socially and politically transformed environments, from the educational restructuring of the 19th century to the post-war period of European integration, highlights the ever-increasing significance of the pedagogical value of the human element alongside the integration of new technologies and tools. Artificial intelligence, as a modern tool, is introduced in this context not as a substitute for the teacher but as a means of enhancing pedagogical work. Throughout European educational history, emphasis on humanistic values has remained central, with new technologies expected to highlight these values and reshape educational processes. The teacher, as facilitator and guide, bears the responsibility of ensuring that artificial intelligence does not distance the learning process from its humanistic core, but rather strengthens it, maintaining its connection to the values of respect, ethics, and democracy. The study combines contemporary theoretical analyses with historical approaches to European educational reforms, emphasizing the timeless necessity of re-evaluating the concept of pedagogy. The digital age, as reflected in the European context, calls for reflection on the roles of the teacher and the student, who remain fundamental actors in the educational process despite the increasing use of technology.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".