GazeQ-GPT: Gaze-Driven Question Generation for Personalized Learning from Short Educational Videos
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".