Got It! Prompting Readability Using ChatGPT to Enhance Academic Texts for Diverse Learning Needs
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
Reading skills are crucial for students' success in education and beyond. However, reading proficiency among K-12 students has been declining globally, including in Sweden, leaving many underprepared for post-secondary education. Additionally, an increasing number of students have reading disorders, such as dyslexia, which require support. Generative artificial intelligence (genAI) technologies, like ChatGPT, may offer new opportunities to improve reading practices by enhancing the readability of educational texts. This study investigates whether ChatGPT-4 can simplify academic texts and which prompting strategies are most effective. We tasked ChatGPT to re-write 136 academic texts using four prompting approaches: Standard, Meta, Roleplay, and Chain-of-Thought. All four approaches improved text readability, with Meta performing the best overall and the Standard prompt sometimes creating texts that were less readable than the original. This study found variability in the simplified texts, suggesting that different strategies should be used based on the specific needs of individual learners. Overall, the findings highlight the potential of genAI tools, like ChatGPT, to improve the accessibility of academic texts, offering valuable support for students with reading difficulties and promoting more equitable learning opportunities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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