Student Engagement and Teacher Perceived Support in <scp>STEAM</scp> Education Using Generative <scp>AI</scp> : A Systematic Review and Direction for Future Research
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
ABSTRACT The emergence of generative AI (GenAI), such as ChatGPT, in education reconceptualizes the realm and is novel to researchers and practitioners alike. Over the past few years, systematic reviews of the impact of GenAI on education have increased, focusing on language education and general education. Such reviews may overlook other integrated disciplines. This impact can be reflected in student engagement (learning outcomes) and teacher perspectives. In response, this review aims to investigate the impact of integrating GenAI on student engagement and teacher‐perceived support in science, technology, engineering, art, and mathematics (STEAM) education. It used a thematic analysis approach to examine relevant articles published over the past 5 years (2020–2024). The findings suggest 11 constructs on how GenAI tools affect the development of student cognitive, behavioral, and emotional engagement. They also suggest three themes about how STEAM teachers felt about GenAI tools—attitude, pedagogy, and 21st‐century skills. We used the findings to suggest recommendations for future directions of GenAI research.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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