ChatGPT in education: Methods, potentials, and limitations
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
ChatGPT has been under the scrutiny of public opinion including in education. Yet, less work has been done to analyze studies conducted on ChatGPT in educational contexts. This review paper examines where ChatGPT is employed in educational literature and areas of potential, challenges, and future work. A total of 64 publications were included in this review using the general framework of open and axial coding. We coded and summarized the methods, and reported potentials, limitations, and future work of each study. Thematic analysis of reviewed studies revealed that most extant studies in the education literature explore ChatGPT through a commentary and non-empirical lens. The potentials of ChatGPT include but are not limited to the development of personalized and complex learning, specific teaching and learning activities, assessments, asynchronous communication, and feedback, accuracy in research, personas, and task delegation and cognitive offload. Several areas of challenge that ChatGPT is or will be facing in education are also shared. Examples include but are not limited to plagiarism deception, misuse lack of learning, accountability, and privacy. There thus are both concerns and optimism about the use of ChatGPT in education, yet the most pressing need is to ensure student learning and academic integrity are not sacrificed. Our review provides a summary of studies conducted on ChatGPT in education literature. We further provide a comprehensive and unique discussion on future considerations for ChatGPT in education.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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