Student Engagement and Teacher Perceived Support in <scp>STEAM</scp> Education Using Generative <scp>AI</scp> : A Systematic Review and Direction for Future Research
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.
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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 | 0.021 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".