Bridging Educational Achievement Gaps with Generative AI: Personalized Curriculum for Targeted Learning Support
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.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".