{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OGOM33WEHCJY3KT5MHEX4QZFOS","short_pith_number":"pith:OGOM33WE","schema_version":"1.0","canonical_sha256":"719ccdeec438938daa7d61c97e4325748c3566d8e6dca7ac311a1a9c3df77121","source":{"kind":"arxiv","id":"2306.01815","version":1},"attestation_state":"computed","paper":{"title":"Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Ashley Ricker Gyllen, Daniel Leiker, Mutlu Cukurova, Sara Finnigan","submitted_at":"2023-06-02T10:58:05Z","abstract_excerpt":"As Large Language Models (LLMs) and other forms of Generative AI permeate various aspects of our lives, their application for learning and education has provided opportunities and challenges. This paper presents an investigation into the use of LLMs in asynchronous course creation, particularly within the context of adult learning, training and upskilling. We developed a course prototype leveraging an LLM, implementing a robust human-in-the-loop process to ensure the accuracy and clarity of the generated content. Our research questions focus on the feasibility of LLMs to produce high-quality a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.01815","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2023-06-02T10:58:05Z","cross_cats_sorted":[],"title_canon_sha256":"ac23b0dcf042cb13b23434141dab3684c63de22bc8f0ed2bb621e254e4e07bf5","abstract_canon_sha256":"63bbfa59552424a320dc7ace32ea7be408254a5a79a8b17088764ff45df5a6fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:09.839842Z","signature_b64":"LUOMQI0YIjFENt8IhhTt5KsGBlWhurqd+3fnosl62MYVrvz6Dd5KYEVgipyOI9GYisDsoCh6W1hXUQy8TZjHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"719ccdeec438938daa7d61c97e4325748c3566d8e6dca7ac311a1a9c3df77121","last_reissued_at":"2026-07-05T06:17:09.839361Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:09.839361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Ashley Ricker Gyllen, Daniel Leiker, Mutlu Cukurova, Sara Finnigan","submitted_at":"2023-06-02T10:58:05Z","abstract_excerpt":"As Large Language Models (LLMs) and other forms of Generative AI permeate various aspects of our lives, their application for learning and education has provided opportunities and challenges. This paper presents an investigation into the use of LLMs in asynchronous course creation, particularly within the context of adult learning, training and upskilling. We developed a course prototype leveraging an LLM, implementing a robust human-in-the-loop process to ensure the accuracy and clarity of the generated content. Our research questions focus on the feasibility of LLMs to produce high-quality a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01815","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.01815/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.01815","created_at":"2026-07-05T06:17:09.839421+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.01815v1","created_at":"2026-07-05T06:17:09.839421+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01815","created_at":"2026-07-05T06:17:09.839421+00:00"},{"alias_kind":"pith_short_12","alias_value":"OGOM33WEHCJY","created_at":"2026-07-05T06:17:09.839421+00:00"},{"alias_kind":"pith_short_16","alias_value":"OGOM33WEHCJY3KT5","created_at":"2026-07-05T06:17:09.839421+00:00"},{"alias_kind":"pith_short_8","alias_value":"OGOM33WE","created_at":"2026-07-05T06:17:09.839421+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.16345","citing_title":"Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS","json":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS.json","graph_json":"https://pith.science/api/pith-number/OGOM33WEHCJY3KT5MHEX4QZFOS/graph.json","events_json":"https://pith.science/api/pith-number/OGOM33WEHCJY3KT5MHEX4QZFOS/events.json","paper":"https://pith.science/paper/OGOM33WE"},"agent_actions":{"view_html":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS","download_json":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS.json","view_paper":"https://pith.science/paper/OGOM33WE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.01815&json=true","fetch_graph":"https://pith.science/api/pith-number/OGOM33WEHCJY3KT5MHEX4QZFOS/graph.json","fetch_events":"https://pith.science/api/pith-number/OGOM33WEHCJY3KT5MHEX4QZFOS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS/action/storage_attestation","attest_author":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS/action/author_attestation","sign_citation":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS/action/citation_signature","submit_replication":"https://pith.science/pith/OGOM33WEHCJY3KT5MHEX4QZFOS/action/replication_record"}},"created_at":"2026-07-05T06:17:09.839421+00:00","updated_at":"2026-07-05T06:17:09.839421+00:00"}