The Educational Guidance Platform via Artificial Intelligence Chatbot to Promote Vocational Aptitude for Vocational Students
Bibliographic record
Abstract
The educational guidance platform via artificial intelligence chatbot to promote vocational aptitude for vocational students or the educational guidance platform via AI Chatbot is a research tool that was designed with the combination of educational guidance process, artificial intelligence technology, and chat platforms like LINE and Messenger. The platform in this study is intended primarily to be used as a tool to analyze vocational aptitude and provide personalized educational advice, which will assist learners to choose suitable programs for further study in vocational education level. This study were aimed to (1) study and synthesis the conceptual framework of the educational guidance platform via AI Chatbot, (2) develop the educational guidance platform via AI Chatbot, and (3) evaluate the results of the developed the educational guidance platform via AI Chatbot. There were nine participants from different institutions included in this research, derived by means of purposive sampling, and with experience in the design and development application. The research instruments include (1) the architecture the educational guidance platform via AI Chatbot, and (2) evaluation form on the architecture the educational guidance platform via AI Chatbot. The results of this study, which were derived from the study on the prototype design of the architecture of the educational guidance platform via AI Chatbot, are designated to be used as a guideline for future studies in order to develop the educational guidance platforms via AI Chatbot that can be put in practical use in an effective manner. The results of this study show that the overall suitability of the development of the architecture of the educational guidance platform via AI Chatbot is at strongly agree level. Nevertheless, there are still some research gaps in this study that need to be further addressed in the future. For instance, the future studies should cover a wider range of application of the developed platforms by conducting the survey with more diverse population and broader educational environments. This is to confirm the suitability of the development of the architecture of the educational guidance platforms via AI Chatbot that can be used as a guideline for future development coupled with the related technologies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".