Cognitive Apprenticeship and Artificial Intelligence Coding Assistants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this chapter is to examine the impact that AI coding assistants have on the manner in which novice programmers learn to read, write, and revise code. These discussions revolve around the concept of cognitive apprenticeship, a pedagogical framework informed by extensive research on tutoring dialogues and collaborative problem-solving practices. It involves guided instruction through modeling, coaching, and scaffolding. Within the realm of programming, these principles hold the key to nurturing skills in reading, writing, and revising code, thus making the learning process more effective and engaging. The chapter concludes by reflecting on the challenges and considerations of implementing cognitive apprenticeship within AI coding assistants. These insights are intended to benefit educators, developers, and researchers alike, offering a roadmap to enhance the learning experiences of novice programmers through AI support.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it