Rethinking Higher Educational Practices in the Age of Artificial Intelligence
Bibliographic record
Abstract
In the contemporary digital landscape, the exclusion of digital tools in higher education undermines the essence of learning and advancement. This research delves into the symbiotic relationship between artificial intelligence (AI) and education, advocating for the integration of cutting-edge AI language learning tools like ChatGPT to keep pace with innovation. Through innovative methods of integrating generative AI language models, this study proposes a hyperaware curriculum design, fostering a revamped teaching and learning environment. It suggests that by leveraging AI, education can prioritize real-world knowledge application. Rather than viewing education as a static endpoint, this research emphasizes an ongoing process of enlightenment. We propose to situate AI in education as a crucial aspect of multiliteracy pedagogical approach. Through the theoretical lens of the four crucial dimensions of multiliteracy pedagogy by New London Group (1996) including situated practice, overt instructions, critical framing, and transformed practice we postulate each dimension in the light of interweaving it with the integration of technology. As we move towards a future heavily reliant on AI, incorporating AI language models and digital tools into education is imperative.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 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".