Digital wireless audio · connecting smart spaces
Campus AI · Enable on demand, evaluate before scaling
The value of campus AI lies not in continuously collecting classroom sounds, but in providing verifiable auxiliary results for clearly defined tasks. With authorized collection, clear data pathways, and teacher review as prerequisites, choose applications and deployment methods that suit the school.
The following are all product planning directions; accuracy, language coverage, concurrency, and response must be tested using school-authorized samples.
The plan is to convert teacher speech into recognized text, translations, and synthesized speech, with support for subtitles and a terminology database. Language ambiguity, proper nouns, and classroom noise can affect the results, so teachers should be able to review, correct, or turn off the translated voice.
Plan to provide pronunciation, fluency, completeness, and intonation feedback. Per-person analysis requires distinguishable student audio tracks and lawful authorization; a shared environment microphone cannot guarantee accurate differentiation of each student.
Plan transcription, speaker diarization, topic organization, and action item extraction. Generated drafts must be reviewed for speaker attribution, decisions, and dates before publication; do not treat automatically generated text as an unconfirmed official record.
Plan the extraction of teaching clues such as knowledge points, lecture and interaction structures, and question distribution. To be used only for authorized teaching and research assistance; no emotional inference or automatic ranking, and not as the sole basis for student disciplinary action, school advancement, or teacher evaluation.
Plan to provide Q&A, lesson preparation, and practice drafts based on the course knowledge base. The knowledge base should be authorized, and teachers should review answers, questions, and grading suggestions; filtering and retrieval cannot guarantee that the model will always be correct.
Restrict courses, venues, time slots, and participants, and provide collection status prompts and a stop entry point. Do not enable sound collection without necessity and a lawful basis; do not install listening devices in highly private spaces.
Register item by item the locations and service providers for recognition, translation, synthesis, model inference, and knowledge base retrieval. TP-DS05 is the computing power planning, and TP-DA06 is the unified interface planning; neither automatically changes the data paths of external services.
Clarify access roles for audio, text, reports, and logs, and apply de-identification as needed while restricting downloads, sharing, and cross-system pushes; de-identification must not be treated as a guarantee of inevitable non-identifiability.
Establish minimum necessary retention and deletion processes, covering endpoint caches, servers, model service providers, backups, and export copies; set up handling channels for applicable data subject rights requests.
| Deployment Method | Data Processing Conditions | Must be verified before going live |
|---|---|---|
| Fully local processing | Only when ASR, MT, TTS, LLM, the knowledge base, logs, and backups are all located on campus and local inference is completed, and external model APIs, external telemetry, and other data exfiltration channels are disabled and the configuration has been verified, may it be stated that the relevant business data is processed on campus and not transmitted externally; a local server or local gateway by itself cannot prove that data does not leave the campus. | Model and voice component licensing, on-campus computing power, offline dependencies, outbound access policies, log backup, and actual traffic |
| Cloud models or voice services | Audio, text, or prompts may be sent to authorized external services; it must not be promoted as data not leaving the school. | Supplier, processing region, purpose, retention, training usage terms, deletion, and cross-border compliance conditions (if applicable) |
| Local gateway + cloud model | Unified gateway authentication, but calls may still transmit data externally; local anonymization does not equal eliminating all personal information risks | Request fields, whether audio is uploaded, result feedback, audit scope, and external service contracts |
| Mixed processing by task | Sensitive tasks are handled exclusively through verified local links, while other tasks use cloud services after completing authorization and compliance assessments. | Default routing, fallback strategies, whether to switch to the cloud during failures, and controls to prevent unauthorized external transmission |
The school and teachers shall confirm the teaching objectives, content scope, participants, and alternative methods, and retain manual processes for tasks that are not suitable for AI use.
Teachers can pause the audio translation, disable interaction, correct recognition errors, or switch to manual explanation; service availability should not be the sole condition for students' normal learning.
Verify translation facts, meeting minutes decisions, question answers, and scoring suggestions. Sensitive content broadcast or published to students should be reviewed first, and model errors should not be directly amplified.
Automated assessments and classroom reports only provide auxiliary clues, retaining channels for manual review, explanation, and correction; no disciplinary, academic progression, or teacher evaluation decisions are made solely based on algorithmic results.
Performance figures are planning targets rather than commitments; record the model version, compute power, languages, and test sample conditions.
| Applications | Recommended test | Confirmation method |
|---|---|---|
| Translation | Terminology, accents, noise, subtitles, and voice translation; TP-DA01 end-to-end <1s is the planning target and requires testing | Checked for accuracy and comprehensibility by personnel with the corresponding language proficiency |
| Spoken language | Individual audio track differentiation, different pronunciation characteristics, device differences, and feedback consistency | Teachers review representative samples, not replacing classroom observation with scores |
| Minutes | Multi-person overlap, speech attribution, dates, decisions, and to-dos | After the responsible person attending the meeting confirms each item one by one, it is released. |
| Classroom Analysis | Knowledge point extraction and lecture delivery, deviation in identifying interactive periods | Compare against manual annotations; do not automatically infer teaching quality or personal conduct |
| Interactive | Knowledge base coverage, unanswerable questions, incorrect content, and out-of-scope requests | Teachers review the content and verify the refusal, suspension, and manual takeover processes. |
Tell us the number of classrooms, network conditions, and usage scenarios, and let's determine the right broadcast, conferencing, and AI configuration together.
Content maintenance:SOYO Technology Development Co., Ltd. · Update:
Product line solution planning | Functions, specifications, and compatibility are subject to official models, actual configurations, and testing. AI applications must implement data authorization and human review according to the deployment method; images are for reference only to the appearance of existing products.
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