This paper specifies a course grounded large language model (LLM) tutoring pipeline with versioned retrieval, a bounded hint policy, claim checks, and teacher escalation. An offline component test used 10 constructed passages, 12 questions, and 12 candidate responses. Best Matching 25 (BM25) retrieved the labeled passage at rank one for 11 of 12 questions; the deterministic gate blocked seven of seven seeded source or hint-level violations and accepted five compliant candidates. The test does not evaluate generated language or student learning. The paper provides an algorithm, trace schema, failure analysis, and prospective classroom protocol.
Keywords: Large Language Models, Course Grounded Tutoring, Retrieval Augmented Generation, Best Matching 25, Versioned Source Corpus, Bounded Hints, Claim Verification, Human Escalation, Offline Component Testing, Learning Transfer, Educational Data Privacy.
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Source of Funding:
No specific external funding source is reported in the source manuscript.
Competing Interests Statement:
There are no conflicts of interest by the authors in any way.
Consent for publication:
The authors give consent for the publication.
Authors' contributions:
All the authors contributed equally to the article.
Informed Consent:
Not Applicable. The reported offline checks used constructed records and no human participants.
Availability of Data and Material:
Constructed test inputs and deterministic procedures are described in the manuscript.
Institutional Review Board Statement:
Not Applicable. No human participants or identifiable student records were included in the offline checks.
Ethical Approval:
Not Applicable to the constructed offline unit tests.
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