Exploration of the Application of Artificial Intelligence in the Intelligent Evaluation System of Information and Innovation Education
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
With the rapid development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. The Xinchuang Education Intelligent Evaluation System, as an important tool for educational informatization, integrates artificial intelligence technology into it, which helps to improve the quality of education, achieve personalized education and intelligent evaluation. This article provides an overview of the development history, main technologies, and application advantages of artificial intelligence technology, and explores the architecture design, key technology selection, and module division of the intelligent evaluation system for information and innovation education. At the same time, the application of artificial intelligence in the intelligent evaluation system of information and innovation education was analyzed, such as automatic scoring and evaluation, learning content recommendation and personalized learning, intelligent assisted teaching and learning, and student learning behavior analysis. Finally, corresponding solutions were proposed to address the challenges faced by artificial intelligence in the intelligent evaluation system of information and innovation education, such as data privacy and security issues, fairness and bias issues, human-computer interaction and user experience issues.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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