Teaching, Technology, and Time: Revisiting Ellen Rose’s Call for Reflection in an AI Era
Bibliographic record
Abstract
Ellen Rose’s On Reflection explores the importance of reflective thought in education, particularly in response to the technological shifts that have reshaped post-secondary teaching. She outlines three forms of reflection—reflection-in-action, reflection-on-action, and reflection-then-action—drawing from established concepts previously discussed in literature. While Rose grounds her arguments in interdisciplinary theory, including the work of Dewey and Schön, the latter half of the book adopts a nostalgic tone, advocating for a return to pre-digital modes of reflection—an era that no longer exists. This paper critiques that stance by addressing the current realities of post-secondary educators who must navigate bureaucratic barriers, limited time, and rapid technological advancement. These challenges demand ongoing pedagogical adaptation, often leaving little room for the deep, sustained reflection Rose calls for. Reflection remains essential but must be reimagined to align with contemporary constraints. As institutions respond to demands for accountability and innovation, reflective practice is increasingly sidelined, creating new tensions around academic integrity, assessment design, and student learning. This paper examines how educators can respond to the ethical and instructional challenges posed by AI while maintaining pedagogical integrity and calls for renewed institutional support for reflection as a foundational element of effective teaching and learning.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".