Bridging Educational Achievement Gaps with Generative AI: Personalized Curriculum for Targeted Learning Support
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 inequitable distribution of educational resources plays a major role in widening achievement gaps, placing students from lower socioeconomic status (SES) backgrounds at a disadvantage due to systemic barriers that restrict access to these resources. Recent advancements in the development of artificial intelligence (AI), namely ChatGPT by OpenAI, showcased its ability to generate text responses in natural language format based on input prompts. The accessibility and convenience of ChatGPT hold promise for offering personalized learning support. In pursuit of this goal, the authors built Ligare – an AI-powered curriculum builder that integrates, optimizes, and presents generated responses with a user-friendly interface. The design process followed rigorous Human-Computer Interaction (HCI) protocols, including pre-development analysis, low- and high-fidelity prototyping, and subsequent evaluation. Although Ligare currently supports only math learning, the evaluation results demonstrate its potential for broader application, highlighting future directions for providing more accessible personalized education and addressing achievement gaps.
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 | 0.000 | 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