Pathways for Design Research on Artificial Intelligence
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
An expanding body of information systems research is adopting a design perspective on artificial intelligence (AI), wherein researchers prescribe solutions to problems using AI approaches rather than describing or explaining AI-related phenomena being studied. In this editorial, we address some of the challenges faced in publishing design research related to AI and articulate viable pathways for publishing such work. More specifically, we highlight six major impediments, use the explosion in the state of the art for large language models to underscore these impediments, propose some pathways for overcoming the impediments, and use several example articles to illustrate how the pathways can be followed for different types of AI-related design artifacts. History: Senior Editor; Associate Editor. Funding: A. Abbasi was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915] and a Kemper Faculty Award. J. Parsons was funded by the Natural Sciences and Engineering Council of Canada (NSERC) [Grant RGPIN-2020-04916].
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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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