Zero-Shot to Head-Shot: Hyperpersonalization in the Age of Generative AI
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) is rapidly transforming industries and organizations through automation and augmentation. Personalization of human–system interaction is a key area that can be significantly advanced through the effective implementation of GenAI. GenAI, positioned as an intermediary between humans and systems, can transform the human experience from the pre-defined, conventional notions of personalization into a dynamic and integrated hyperpersonalization capability. This article presents the zero-shot-to-head-shot hyperpersonalization (Z2H2) framework, which aims to achieve this through the effective adoption of GenAI techniques. It is a domain-neutral framework of three incremental stages named zero-shot, few-shot, and head-shot that gradually increase the level of hyperpersonalization of the human–system interaction. The framework is further represented in a layered system design and the Z2H2 Data Modality Matrix (ZDMM), which systematically maps data types, AI capabilities, and personalization objectives for each stage. The capabilities of the framework are demonstrated in an educational setting, followed by an empirical evaluation using the Open University Learning Analytics Dataset (OULAD). Although this dataset only contains demographic and aggregated clickstream data, which is a subset of attributes relevant to the entire framework, the gradual development of zero-shot-to-head-shot hyperpersonalization is effectively demonstrated and validated on these student interactions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".