What happens when an artist appropriates emerging technologies to create paintings? An exploration through portraiture
Bibliographic record
Abstract
This studio thesis investigates the process and result of merging traditional art-making media with emerging technologies in creating portraits. Through the creation of 10 oil portrait paintings with AI and AR components, the following questions are considered: What happens when artificial intelligence (AI) and augmented reality (AR) are paired with traditional art-making tools, and what benefits can students gain from using AI and AR in the art room? How can emerging technologies such as AI and AR be used in conjunction with traditional methods of creation such as painting and drawing? Through interviews, journaling, and the creation of the portraits, I explored the subject and found a marked distinction between the use of traditional media and emerging technologies, both physically and mentally. I described how it affected my creative process and the result of the final works. I also look at using emerging technologies with traditional media in the art class and its possible uses and effects on students and teachers. This thesis calls for the responsible use of emerging technologies in art-making and considers how elementary teachers can use and teach these emerging technologies as a tool in the creation of artworks.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".