Architecture of the Service Platform via Artificial Intelligence Chatbots to Promote Students’ Digital Competency
Bibliographic record
Abstract
The architecture of the service platform via AI chatbots is a research tool which was fabricated to specifically promote digital competency through the use of AI chatbots. The architecture of the service platform herein was initiated by the application of artificial intelligence technology integrated with chatbot technology to create the user interface that can interact with users through the use of languages; thereby, this service platform shall analyze questions or keywords from users and then respond with optimal answers. The objectives of this research are (1) to synthesize the conceptual framework of the architecture of the service platform via AI chatbots, (2) to develop the architecture of the service platform via AI chatbots, and (3) to study the results after development the architecture of the service platform via AI chatbots. The research instruments consist of (1) the architecture of the service platform via AI chatbots, and (2) the evaluation form on the suitability of the architecture of the service platform via AI chatbots. The results of this research show that the suitability of the architecture of the service platform via AI chatbots is at a highest level. However, this study is considered merely a pilot study, which is intended primarily to study the concepts and the feasibility to devise prototype architecture of the service platform via AI chatbots before using it as a guideline to further develop other service platforms via AI chatbots, which can be put in practical use indeed in the future.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".