From Mind to Machine: An Embodied Approach to Image Creation with Generative AI
Bibliographic record
Abstract
This thesis explores the integration of embodied interactions within Human-to-Artificial Intelligence (AI) collaborative activity to support creative engagement and reduce the perception of AI as an uncontrollable, autonomous entity, also known as the AI “black box” rhetoric. Specifically, it investigates the utilization of kinetic sand as a sensory material and physiological data collector, facilitating the translation of users' hand motions and imprints in the sand into inputs for generative AI image creation. Informed by sociocultural frameworks of creativity, theories of embodied cognition and the positioning of AI as a statistical model, while grounded in iterative design methodologies and phenomenological analysis, the research aims to identify emergent guidelines from this collaborative creative process between humans and AI. The findings hope to contribute to the development of guidelines that inform the future design and implementation of generative AI systems for creative work. These guidelines account for embodied cognition as an essential facet of human creativity, promoting more intuitive and meaningful interactions between humans and generative AI. Ultimately, this research seeks to advance the discourse on human-AI collaboration, emphasizing the importance of embodied techniques in fostering creative synergy and mitigating the black box effect.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".