GazeQ-GPT: Gaze-Driven Question Generation for Personalized Learning from Short Educational Videos
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
Effective comprehension is essential for learning and understanding new material. However, human-generated questions often fail to cater to individual learners’ needs and interests. We propose a novel approach that leverages a gaze-driven interest model and a Large Language Model (LLM) to generate personalized comprehension questions automatically for short (∼ 10 min) educational video content. Our interest model scores each word in a subtitle. The top-scoring words are then used to generate questions using an LLM. Additionally, our system provides marginal help by offering phrase definitions (glosses) in subtitles, further facilitating learning. These methods are integrated into a prototype system, GazeQ-GPT, automatically focusing learning material on specific content that interests or challenges them, promoting more personalized learning. A user study (N = 40) shows that GazeQ-GPT prioritizes words in the fixated gloss and rewatched subtitles with higher ratings toward glossed videos. Compared to ChatGPT, GazeQ-GPT achieves higher question diversity while maintaining quality, indicating its potential to improve personalized learning experiences through dynamic content adaptation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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