Aprendizagem ao Longo da Vida: Por que a Educação é Importante na Era da IA?
Bibliographic record
Abstract
Este trabalho apresenta os principais pontos da palestra de Marc Sosna, da IESE Business School, intitulada "Aprendizagem ao Longo da Vida: Por que a Educação é Importante na Era da IA?". A apresentação utiliza a metáfora da viagem no tempo para discutir como a educação precisa evoluir diante do avanço acelerado da inteligência artificial (IA). O autor defende a importância da aprendizagem contínua, da capacidade de aprender (learnability) e da prontidão para o futuro, tanto no nível individual quanto organizacional. Sosna alerta para os riscos de uma dependência excessiva da IA, que pode enfraquecer capacidades cognitivas humanas, e propõe quatro lentes para repensar a educação executiva: tecnologia, sociedade, aluno e academia. A palestra conclui que, mesmo com a presença crescente da IA, o fator humano continua essencial. O aprendizado deve ser personalizado, baseado em experiências significativas, e alinhado às rápidas mudanças do mercado e da sociedade.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".