The Role of Artificial Intelligence in Shaping Robotics Innovation
Bibliographic record
Abstract
Applying Artificial Intelligence in Robotics is a revolutionary move in industries, education, and the health sector. In this paper, we critically survey recent work in the literature to analyse the ways AI is influencing robotics innovation, emphasizing both its potential and its limitations. The results show that AI-enabled robotics improves efficiency and flexibility in industrial applications, enabling predictive maintenance, smart manufacturing, and operational resilience. In education, interactive and voice-activated robots promote student engagement, problem-based learning, and progressive pedagogy, but uneven access may reinforce digital divides. In medicine, AI-based surgery and diagnostics robots enable greater precision, fewer mistakes, and better patient outcomes, but raise unanswered ethical questions regarding accountability, autonomy, and patient–doctor trust. In addition to applications in industry, mandates from society, ethics, and regulation are also recognized as barriers to unfettered use. Problems of data privacy, transparency, and fairness remain, while high adaptation costs and immature regulatory frameworks limit the spread. The findings suggest that AI in robotics is both a technical disruptor and a social transformer, offering a wealth of opportunities while requiring robust ethical governance. The paper concludes by suggesting that drawing on the expertise of multiple disciplines – stemming from the work of engineers, ethicists, and policymakers- and engaging key stakeholders, AAI-powered robotics will continue to evolve for the better. More work is needed on governance models, inclusiveness, and long-range societal consideration to strike a balance between innovation and responsibility.
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.
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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".