How Instructors Incorporate Generative AI into Teaching Computing
Bibliographic record
Abstract
Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".