Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the impact of artificial intelligence (AI) on education, with a focus on assessment and academic integrity in higher education. We conducted a thematic analysis of literature on AI and academic integrity, framed by possible utopic and dystopic scenarios. We found that AI can be used to generate text, summarize work, create outlines, and provide information and resources on a particular topic, saving time and money. We argue that effective institutional policies should be established around the use of AI technologies, such as ChatGPT, to better serve the fields of education and academic research. The paper also discusses the implications of AI for university students, including the potential for personalized learning, quick feedback on student work, and improved accessibility for students with disabilities. However, the use of AI in education raises concerns about academic integrity and the potential for cheating. We caution that ethical considerations under existing academic integrity frameworks must be considered when implementing AI in education. The article concludes by calling for further research on the impact of AI on education and the development of guidelines and policies to ensure that AI is used in a responsible and ethical manner.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it