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セルフホスト型講義からクイズへ：決定論的品質管理を伴うローカルLLMによる選択式問題生成

arXiv cs.CL / 2026/3/11

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要点

  • 本論文は、講義PDFをローカルの大型言語モデル（LLM）と決定論的品質管理プロセスを組み合わせて選択式問題（MCQ）に変換する、完全なセルフホスト型かつAPI不要のパイプラインを紹介しています。
  • この手法は講義内容を外部のLLMサービスに送信しないため、教育コンテンツ生成におけるプライバシーとデータセキュリティを強化します。
  • パイプラインにはJSONスキーマ検証、正解選択肢のマーク、数値等価性テストといった厳格な品質チェックが含まれ、残存品質リスクを特定する警告システムも備えています。
  • 実験評価では情報理論、熱力学、統計力学に関する3つのダミー講義を用い、高品質な24問のセットを生成し、一般的な誤りの修正も文書化しています。
  • 著者らは、教育技術ワークフローにおいてアカウンタビリティ、プライバシー、環境持続可能性（グリーンAI）を支援するセルフホスト型MCQ生成の利点を強調しています。

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

View a PDF of the paper titled Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Control, by Seine A. Shintani
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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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