Leveraging artificial intelligence for enhancing postgraduate teaching: a framework to engaging professional learners
Bibliographic record
Abstract
In the contemporary landscape of postgraduate education, the strategic integration of Artificial Intelligence (AI) has gained prominence as a transformative approach for optimizing pedagogical experiences, particularly among professional learners. This paper presents a framework aimed at harnessing the potential of AI to elevate postgraduate teaching, focusing on fostering active engagement and meaningful learning interactions within the context of professional education. Following the PRISMA guidelines, this research conducts a systematic review of pertinent literature sourced from Scopus and other reputable databases. Studies encompassing empirical investigations, conceptual frameworks, and pedagogical models that explore the integration of AI in postgraduate education for professional learners are selected. A stringent search strategy and clear eligibility criteria ensure the inclusion of studies that contribute to the development of the proposed framework. Drawing from the findings of the systematic review, this paper proposes a multidimensional framework that strategically incorporates AI into postgraduate teaching for professional learners. This paper contributes a novel framework for integrating AI into postgraduate education, specifically tailored to the needs of professional learners. The synthesized framework serves as a pragmatic guide for educators, curriculum designers, and policymakers aiming to leverage AI’s potential to enrich professional education. By strategically combining AI technologies with pedagogical strategies, educators can empower professional learners to thrive in their careers, offering them a robust learning experience that resonates within their professional pursuits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".