Artificial intelligence-based robots in education: A systematic review of selected SSCI publications
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
With the rapid development of artificial intelligence, the application of AI robots (Artificial Intelligence-based robots) for instruction has become an attractive research topic. Numerous studies have shown that AI robots may provide new opportunities for learning designs in school settings or professional training. However, there is no review examining the role and research foci of AI-Robots in Education (AIRE) research. This study therefore explored the research trends of AIRE by conducting a systematic review of SSCI (Social Sciences Citation Index) journal articles published in the Web of Science (WoS). The study analyzed the participants, duration of the studies, learning environments, application domains, data analysis, evaluations of learners' performance, learning strategies, roles of AI-robots, and research issues. The research findings are concluded as follows: (1) The countries of Canada, Chile, and South Korea invested in AIRE research early, and focused on students' learning performance and learning behavior. (2) Most AIRE research focuses on research targets under the age of 13 and completed experiments within 4 weeks in a physical environment. Most AIRE research has been applied in the disciplines of Language and Science, and problem-solving related strategies and mixed strategies are the most commonly used strategies. (3) AI-robots are often applied and regarded as tutees or tutors. Regardless of what the AI-robot's role is, learning performance is the most widely focused variable in AIRE research. Furthermore, attitudes, opinions of learners or learning perceptions and learning behavior are other frequently discussed themes. To sum up, this study makes several recommendations for AIRE research for educators, researchers, and policy makers in higher education settings as a reference based on the results.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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