Integrating Artificial Intelligence with NHEQF Descriptors for Pedagogical Excellence
Bibliographic record
Abstract
This research investigates the critical need to integrate affective and psychomotor domains alongside cognitive development in educational systems to achieve the comprehensive ‘Exit Outcomes’ of Outcome-Based Education (OBE) and align with the National Higher Education Qualification Framework (NHEQF) descriptors. Traditional educational approaches are inadequate for these goals, prompting the introduction of the AI-Charya framework—a novel, artificial intelligence (AI)-driven pedagogical model. Utilizing a qualitative approach, this study explores the limitations of existing models and the transformative potential of generative AI. The AI-Charya framework provides adaptive, multimodal educational strategies that personalize learning and significantly enhance critical and creative thinking skills. Findings indicate that students engaged with AI-Charya show marked improvements in these areas, positioning them for success in an increasingly automated global workforce. However, the study’s generalizability is limited by its specific educational contexts, and further research is needed to assess long-term outcomes. Despite these limitations, the AI-Charya framework offers a pioneering blueprint for aligning educational practices with OBE and NHEQF standards, equipping students with the holistic competencies required for dynamic, future-oriented careers. This research has significant implications for policymakers, educators and curriculum developers aiming to enhance educational excellence through innovative methodologies.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".