A retrieval-grounded study platform for secondary curricula engineered to enforce boundary fidelity. Comprising thirty modules, 101 interactive structural diagrams, 9,226 cryptographically pinned passages, and a pre-generative confidence gate calibrated to refuse ungrounded queries.
Teacher ONE originated from an empirical observation made during the deployment of an earlier educational system, Scholar Pro Connect. Conventional digital learning tools typically operate on the premise that student underperformance is caused by a scarcity of learning resources. Consequently, existing platforms aggregate vast repositories of lecture notes, video recordings, and problem sets. Observation of actual study habits revealed that students rarely suffer from an absolute deficit of material; rather, they lack mechanisms to evaluate the correctness of their comprehension in real time.
The introduction of unconstrained generative artificial intelligence into secondary education exacerbated this vulnerability. General-purpose large language models prioritize conversational fluency over domain accuracy. When presented with queries outside a prescribed examination syllabus, such models frequently generate authoritative, polished explanations that contain subtle factual inversions or extraneous theoretical concepts. For a secondary school student revising for national board examinations, an uncalibrated model answer presents an acute epistemic risk: plausible falsehoods are rehearsed, internalised, and reproduced under examination conditions.
This technical report terms this structural vulnerability boundary truncation: the pedagogical requirement that instructional explanation must terminate precisely at the perimeter of the evaluated curriculum, rather than at the boundaries of general human knowledge.
To eliminate hallucination and ungrounded extrapolation, Teacher ONE inverts the standard generative framework. Generative synthesis is rendered strictly subordinate to deterministic document retrieval, and retrieval is conditioned upon an antecedent confidence gate.
By enforcing refusal prior to prompt assembly, the generative language model is never exposed to queries that lack corpus support. Refusal operates as an architectural invariant rather than a post-hoc output filter, rendering the system resilient against prompt manipulation or conversational circumvention.
Lexical retrieval provides absolute determinism, mathematical interpretability, and zero runtime model overhead. Every generated answer can be audited against the exact passage indices that produced it. Furthermore, the BM25 index compiles to a lightweight data artifact rather than neural network weights, allowing local execution without GPU acceleration. The empirical trade-off is acknowledged: BM25 cannot recognize semantic equivalence when terminology diverges from the index, resulting in retrieval misses on heavily visual or non-textual questions.
Client-side execution satisfies three primary requirements: absolute data privacy, curriculum permanence, and cost elimination. A student's revision query history and diagnostic performance records remain strictly local to the host machine. Additionally, local execution insulates the platform from remote API drift or model updates. Crucially, the marginal operational cost per student is reduced to zero, enabling distribution across economically constrained secondary schools. The packaged backend cold-starts in 455 ms with a memory footprint of 78.6 MB.
Guaranteeing offline functionality requires bundling both the retrieval index and the local execution runtime within the client application. While Tauri produces significantly smaller binary distributions, Electron offers established cross-platform packaging stability across Windows and macOS environments without requiring external system dependencies.
In academic assessment, providing no answer is strictly preferable to providing a plausible but incorrect answer. A refusal forces the student to consult primary course literature, whereas a hallucinated answer instils false confidence. The refusal rate represents the deliberate price of mathematical integrity within an automated instructional system.
Full experimental methodologies and statistical distributions are documented in the evaluation report. Key findings include: