AI-assisted knowledge assessment techniques for adaptive learning environments
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
The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instructions for students in an adaptive learning environment. There are increasing interests as well as many challenges in the application of Artificial Intelligence (AI) techniques in educational settings to provide adaptive learning content to learners. Knowledge assessment is necessary for providing an adaptive learning environment. A student model serves as a fundamental building block of knowledge assessment in an adaptive learning environment. This paper intends to review the development of dominant families of student models with psychometric theory in early educational research, recent adaptations, and advances with machine learning and deep learning techniques. Our review covers not only the important families of student models but also why they were invented from both theoretical and practical viewpoints with AI and educational perspectives. We believe that the discussion covered in this review will be a valuable reference of introductory insights to AI for educational researchers, as well as an endeavor of introducing basic psychometric perspectives to AI experts for knowledge assessment in the field of learning science. Finally, we provide recent challenges and some potential directions for developing efficient knowledge assessment techniques in future adaptive learning ecosystems.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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