{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WVN5CCTJAJUSIQRUMZXAEU5ZSP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1c464f77a477a7a2a937a3d2e3b30dc467eed76f84d63d444a8dceb9f356bf73","cross_cats_sorted":["cs.AI","cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2025-08-20T14:44:00Z","title_canon_sha256":"105a3fab6d18e9e22b7ffc26ef581485cc7ea9803aae470ec24a5fe82413ffbf"},"schema_version":"1.0","source":{"id":"2508.16659","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.16659","created_at":"2026-07-05T11:58:01Z"},{"alias_kind":"arxiv_version","alias_value":"2508.16659v1","created_at":"2026-07-05T11:58:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.16659","created_at":"2026-07-05T11:58:01Z"},{"alias_kind":"pith_short_12","alias_value":"WVN5CCTJAJUS","created_at":"2026-07-05T11:58:01Z"},{"alias_kind":"pith_short_16","alias_value":"WVN5CCTJAJUSIQRU","created_at":"2026-07-05T11:58:01Z"},{"alias_kind":"pith_short_8","alias_value":"WVN5CCTJ","created_at":"2026-07-05T11:58:01Z"}],"graph_snapshots":[{"event_id":"sha256:084f96a6156cfc61f153e31b1e5c1a5e6d10e18ae8e712049d61df14202928dd","target":"graph","created_at":"2026-07-05T11:58:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.16659/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedago","authors_text":"Jiayi Wang, John Stamper, Ruiwei Xiao, Xinying Hou","cross_cats":["cs.AI","cs.HC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2025-08-20T14:44:00Z","title":"Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.16659","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3592691e9f93ef7a18320616dccb046af73fa17243d08710af8abb1e8f284864","target":"record","created_at":"2026-07-05T11:58:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1c464f77a477a7a2a937a3d2e3b30dc467eed76f84d63d444a8dceb9f356bf73","cross_cats_sorted":["cs.AI","cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2025-08-20T14:44:00Z","title_canon_sha256":"105a3fab6d18e9e22b7ffc26ef581485cc7ea9803aae470ec24a5fe82413ffbf"},"schema_version":"1.0","source":{"id":"2508.16659","kind":"arxiv","version":1}},"canonical_sha256":"b55bd10a690269244234666e0253b993ebf0eaa9d50fd4fe54fdc6b6f36ed06b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b55bd10a690269244234666e0253b993ebf0eaa9d50fd4fe54fdc6b6f36ed06b","first_computed_at":"2026-07-05T11:58:01.021628Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:01.021628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FNYYetv8wQ9GzTqbe+bPLALv4ZfN6yPsdngHj1RR9xE+X7tklcc4IcPhY9B6Eutha0My7kWLcuaWWdM6N5X3BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:01.022136Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.16659","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3592691e9f93ef7a18320616dccb046af73fa17243d08710af8abb1e8f284864","sha256:084f96a6156cfc61f153e31b1e5c1a5e6d10e18ae8e712049d61df14202928dd"],"state_sha256":"5a5c80273243730ade117754ced0de1453eff663717e8098be8b96dd0ee29e65"}