{"paper":{"title":"Prediction Model of Motivators and Demotivators of Integrating Large Language Models in Software Engineering Education: An Empirical Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Governance mechanisms like integrity and ethical safeguards should be prioritized for cost-efficient LLM integration in software engineering education.","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Estefan\\'ia Mart\\'in-Barroso, Jussi Kasurinen, Maryam Khan, Muhammad Azeem Akbar","submitted_at":"2026-05-10T07:41:25Z","abstract_excerpt":"Context: Large Language Models (LLMs) are increasingly influencing software engineering practice and education. While prior studies examine their technical performance and classroom use, limited research provides cost-aware and empirically grounded models for systematic institutional integration.\n  Objective: This study develops and validates a prediction model to identify cost-efficient strategies for integrating LLMs into software engineering education using motivating and demotivating factors.\n  Method: Based on our previously developed literature survey taxonomies [1], we operationalized 1"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Optimization results indicate that governance-related mechanisms, particularly integrity and ethical safeguards, should be prioritized under cost constraints.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That Likert-scale survey responses from 126 stakeholders can be directly encoded into probabilistic predictions of real-world LLM familiarity and that the resulting probabilities can be meaningfully traded off against unspecified implementation costs in a genetic algorithm.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Survey data from 126 stakeholders is used to train probabilistic models whose outputs feed a genetic algorithm that recommends prioritizing governance mechanisms for cost-constrained LLM integration in software engineering education.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Governance mechanisms like integrity and ethical safeguards should be prioritized for cost-efficient LLM integration in software engineering education.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"040c1b89d67c00e7ad1835e560c4a0bd06ca4dd572c8287b50fb03dde3a5791e"},"source":{"id":"2605.09393","kind":"arxiv","version":2},"verdict":{"id":"fe3084d6-edfd-4bfe-b829-83679353f310","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T04:33:54.429410Z","strongest_claim":"Optimization results indicate that governance-related mechanisms, particularly integrity and ethical safeguards, should be prioritized under cost constraints.","one_line_summary":"Survey data from 126 stakeholders is used to train probabilistic models whose outputs feed a genetic algorithm that recommends prioritizing governance mechanisms for cost-constrained LLM integration in software engineering education.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That Likert-scale survey responses from 126 stakeholders can be directly encoded into probabilistic predictions of real-world LLM familiarity and that the resulting probabilities can be meaningfully traded off against unspecified implementation costs in a genetic algorithm.","pith_extraction_headline":"Governance mechanisms like integrity and ethical safeguards should be prioritized for cost-efficient LLM integration in software engineering education."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.09393/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:36:16.467338Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T13:01:18.352632Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T10:18:30.741815Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"908c913728d74011b0e5880b1c6bd2e1e4f4780dffbfd82d7616546250cf4ba4"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}