Can companies in digital marketing benefit from artificial intelligence in content creation?
Bibliographic record
Abstract
AI is tanking different functions of businesses, and marketing is no exception. Digital marketing is gaining pace with the advancement in technology and the internet. The research aims to find the answer to the research question that marketers can benefit from AI in content creation for the digital market. The study also finds the relevance and use of AI in content creation and develops an AI infrastructure adoption model for content creators in digital marketing. The findings of this study were compiled using a systematic literature review that adhered to the Preferred Reporting Items for Systematic Reviews (PRISMA) statement and the criteria established by Meta-Analyses. The findings revealed that using AI in content creation provides personalized data, which helps the creators make relevant, targeted, and specific content. The research also finds that AI alone is not mature enough to carry out the whole content creation procedure as there is some limitation attached, especially regarding ethical implications. That’s why human surveillance of AI systems involved in content creation for the digital market is needed.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".