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Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Control

arXiv cs.CL / 3/11/2026

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Key Points

  • The paper introduces a fully self-hosted, API-free pipeline that transforms lecture PDFs into multiple-choice questions (MCQs) using a local large language model (LLM) combined with deterministic quality control processes.
  • This approach ensures that no lecture content is sent to external LLM services, enhancing privacy and data security in educational content generation.
  • The pipeline includes thorough quality checks such as JSON schema validation, correct option marking, and numeric equivalence tests, with an additional warning system to identify potential residual quality risks.
  • Experimental evaluation was conducted on three dummy lectures across various scientific topics, producing a high-quality 24-question set with documented fixes for common errors.
  • The authors emphasize the benefits of self-hosted MCQ generation for accountability, privacy, and environmentally sustainable (Green AI) practices in educational technology workflows.

Computer Science > Computers and Society

arXiv:2603.08729 (cs)
[Submitted on 20 Feb 2026]

Title:Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Control

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Abstract:We present an end-to-end self-hosted (API-free) pipeline, where API-free means that lecture content is not sent to any external LLM service, that converts lecture PDFs into multiple-choice questions (MCQs) using a local LLM plus deterministic quality control (QC). The pipeline is designed for black-box minimization: LLMs may assist drafting, but the final released artifacts are plain-text question banks with an explicit QC trace and without any need to call an LLM at deployment time. We run a seed sweep on three short "dummy lectures" (information theory, thermodynamics, and statistical mechanics), collecting 15 runs x 8 questions = 120 accepted candidates (122 attempts total under bounded retries). All 120 accepted candidates satisfy hard QC checks (JSON schema conformance, a single marked correct option, and numeric/constant equivalence tests); however, the warning layer flags 8/120 items (spanning 8 runs) that expose residual quality risks such as duplicated distractors or missing rounding instructions. We report a warning taxonomy with concrete before->after fixes, and we release the final 24-question set (three lectures x 8 questions) as JSONL/CSV for Google Forms import (e.g., via Apps Script or API tooling) included as ancillary files under anc/. Finally, we position the work through the AI to Learn (AI2L) rubric lens and argue that self-hosted MCQ generation with explicit QC supports privacy, accountability, and Green AI in educational workflows.
Comments:
Subjects: Computers and Society (cs.CY); Computation and Language (cs.CL)
ACM classes: I.2.7; K.3.2
Cite as: arXiv:2603.08729 [cs.CY]
  (or arXiv:2603.08729v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2603.08729
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arXiv-issued DOI via DataCite

Submission history

From: Seine A. Shintani [view email]
[v1] Fri, 20 Feb 2026 09:52:39 UTC (194 KB)
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