INCORPORATION OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION SYSTEM IN THE REPUBLIC OF ARMENIA: CONTEXT AND INTERNATIONAL PERSPECTIVES
Bibliographic record
Abstract
Higher education is a key driver of societal change, a role profoundly shaped and accelerated by a range of interconnected global influences and factors. These include advancements in technology, the forces of globalization and internationalization, rapid shifts in sociocultural norms, and transitions in geopolitical landscapes. Artificial Intelligence (AI) has become a strategic priority worldwide, driving innovations in the knowledge economy and transforming higher education. The incorporation of AI into higher education systems is being shaped by a complex web of interconnected global influences and structural forces. These include advancements in digital technologies, the accelerating momentum of globalization and internationalization, and shifts in the global geopolitical landscape. This article analyzes higher education policies of the Republic of Armenia (RA) to identify explicit references to AI and to evaluate the extent to which these policy provisions are being implemented at the institutional level. The article examines how AI-related policies are being applied across the RA HEIs, namely YSU, NPUA, and AUA. To contextualize Armenia’s trajectory, the article also offers a comparative analysis of international best practices in AI integration within higher education systems, drawing on the experiences of the United States, the United Kingdom, and Canada. This article posits that the strategic and timely incorporation of AI into the RA’s higher education policies, institutional strategies and frameworks, and academic offerings serves as a critical lever for advancing national innovation capacity, driving educational modernization in the RA higher education system, and enhancing competitiveness within the knowledge economy at national and global levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".