TutorialPlan: automated tutorial generation from CAD drawings
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
Authoring tutorials for complex software applications is a time consuming process. It also highly depends on the tutorial designer’s skill level and experience. This paper introduces an approach which automatically generates software tutorials using the digital artifacts produced by the users of a software program. We model this process as an optimal planning problem using software produced artifacts, software specifications and the human-computer interaction Keystroke-Level Model (KLM). We present TutorialPlan, an automated tutorial generator, which creates stepby-step text and image instructions from CAD drawings and helps users learn AutoCAD, a complex design and drafting software. In our tutorial generator, the optimal planning problem is represented and solved using DLV, a general Answer Set Programming (ASP) system. DLV offers a natural representation of both the problem and the heuristics needed to solve it efficiently. A user study shows that the tutorials generated by our system are comparable to those generated by experienced AutoCAD users. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it