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.
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.
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 | 0.059 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| 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".