Students’ Perspective on the Use of Artificial Intelligence in Education
Bibliographic record
Abstract
Abstract While researchers and policymakers contribute crucial insights into the technical, ethical, and systemic dimensions of AI in education, the learner's perspective introduces a different viewpoint in which their representation of AI’s potential influences their perspectives and hopes for improving the learning experience. In this chapter, we focus on studies which analyse the perspective of middle school students at Life Bloom Academy before and after their participation in a semester of AI acculturation activities. Throughout this designated semester, students at Life Bloom Academy embarked on a comprehensive and interdisciplinary educational journey aimed at cultivating their understanding of AI. Under the guidance of their teachers, students began their journey by delving into the philosophical nuances of human intelligence. An integral component of this educational journey involved a visit to the Maison de l'Intelligence Artificielle (MIA), where students actively participated in a diverse range of multidisciplinary activities centred around both contemporary and prospective applications of AI. The study suggests that middle school students not only display a keen awareness of the societal implications linked to the utilisation of AI but also manifest a lasting enthusiasm for the technology that extends beyond the boundaries of a traditional academic setting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".