A Review of the Evolution and Application Scenarios of Generative Artificial Intelligence Technology
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI), as one of the most transformative technological directions in the AI field, has recently undergone leapfrog development from statistical language models to large-scale pre-trained models, and then to multimodal unified generation systems. This evolution has profoundly reshaped the underlying logic of content production, knowledge acquisition, and industrial innovation. Cutting-edge models represented by the GPT series, Stable Diffusion, and Sora demonstrate powerful capabilities in text, image, and video generation, driving generative AI rapidly from laboratory research to diverse application scenarios such as education, healthcare, finance, security defense, and cultural creativity, sparking widespread attention from both academia and industry. However, the rapid technological iteration also brings complex challenges, including model hallucinations, algorithmic bias, copyright disputes, data privacy leaks, and the widening digital divide, necessitating systematic theoretical review and governance responses. Based on domestic and international frontier literature from 2023 to 2024, this paper adopts a systematic literature review method to comprehensively sort out and deeply analyze the current research status of generative AI from four dimensions: technological evolution trajectory, typical application scenarios, core challenges and countermeasures, and future development trends. Research shows that the technological evolution of generative AI presents stage characteristics of “rule-driven → statistical learning → deep gener-ation → multimodal unification.” Its application scenarios have expanded from general content generation to deep penetration into vertical domains, demonstrating significant value particularly in personalized education, intelligent medi-cal diagnosis, financial advisory services, cybersecurity defense, and AIGC-enabled design. At the same time, technical reliability, ethical fairness, legal compliance, and system security constitute the key bottlenecks currently restrict-ing its sustainable development. In the future, generative AI will evolve towards multi-technology integration, industry specialization, and systematic governance, achieving a transition from “tool empowerment” to “ecosystem reconstruction.” This paper aims to provide a panoramic reference for the theoretical research and practical application of generative AI, supporting academic exploration and industrial decision-making in related fields.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".