Artificial Intelligence in Higher Education
Bibliographic record
Abstract
Since November 2022, ChatGPT has had very high visibility in higher education, raising an impressive amount of debate and discussion. These conversations have been focused on both the various risks and issues raised by such powerful AI tools but also on the diverse possibilities they offer to assist, facilitate, and even augment the work of learners and teachers. For many people, ChatGPT represents an eruption of AI in the field of education. Yet this sudden media attention obscures the fact that AI has been present in higher education for many years already. The field of learning analytics is growing significantly in education, resulting in descriptive or predictive analyses based on the traces left by learners in digital environments, and giving rise to predictive dropout models and dashboards that have been implemented in some universities ( Ifenthaler & Yau, 2020 ). Technological developments by large cloud providers make it much easier to accumulate data for analysis (data mining) or to develop intelligent conversational agents (chatbots) that can be used to support students ( Heryandi, 2020 ). The field of AI in education (AIED) focuses on learning analytics, conversational robots and natural language processing, adaptive learning, speech and visual recognition, expert systems, and decision support systems. It now also encompasses generative AI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".