Features, Components and Processes of Developing Policy for Artificial Intelligence in Education (AIED): Toward a Sustainable AIED Development and Adoption
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
Governments and private sectors are now putting in place the needed resources and infrastructure to harness the power of emerging technologies in education. One of these technologies is Artificial Intelligence (AI) which gained increasing attention due to its potential to enhance learning and teaching experiences, hence achieving better learning outcomes. However, AIED also comes with several concerns that raise continuous questions about its safe and effective adoption. The application of AIED needs to be planned and executed properly. One of the basic requirements for this to happen is that a comprehensive national policy on the use of AIED is put in place to guide its implementation and evaluate its effectiveness. Limited information exists in the literature on how to write an AIED policy. To address this research gap, this study therefore discusses the features, major components and processes that countries are advised to adopt for a comprehensive AIED policy development. Specifically, this study highlights four features for a good AIED policy, namely contextual, consultative, dynamic, and, implementable and measurable. It further proposes seven steps for an AIED policy development, namely (1) pre-drafting consultation, (2) stakeholder survey, (3) writing draft policy, (4) discussion on draft policy, (5) adoption and communication of policy document, (6) policy implementation plan, and (7) policy evaluation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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