A Common Framework for Artificial Intelligence in Higher Education (AAI-HE Model)
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
This research aims to 1) Develop a common framework for artificial intelligence in higher education (AAI-HE model) and 2) Assess the AAI-HE model. The research process is divided into two stages: 1) Develop an AAI-HE model, and 2) Assessment the model. The sample consists of five experts chosen through purposive sampling. The data is analyzed by means and standardized deviations statistically. The research result shows that 1) the AAI-HE model consists of seven key components which are 1.1) User Interactive Components and Technology of AI, 1.2) Components and Technology of AI, 1.3) Roles for Artificial Intelligence in Education 1.4) Machine Learning and Deep Learning 1.5) DSS Modules 1.6) Applications of Artificial Intelligence in Education, and 1.7) AI to enhance campus efficiencies, and 2) The result of the assessment of the AAI-HE model is rated as absolutely appropriate overall.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it