{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:G37743DBPJFH67AIXW5JI5YOKH","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":"327644cac0314b5aefb7311072ffec1d3fa44bf88bc8bc0ea723978fe95d7e7d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-12-18T15:17:09Z","title_canon_sha256":"639ac42cc726b365f282596d0070c313fb22a1daf0db2bffffb0fe63a472620b"},"schema_version":"1.0","source":{"id":"2312.11274","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.11274","created_at":"2026-07-05T07:27:10Z"},{"alias_kind":"arxiv_version","alias_value":"2312.11274v3","created_at":"2026-07-05T07:27:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11274","created_at":"2026-07-05T07:27:10Z"},{"alias_kind":"pith_short_12","alias_value":"G37743DBPJFH","created_at":"2026-07-05T07:27:10Z"},{"alias_kind":"pith_short_16","alias_value":"G37743DBPJFH67AI","created_at":"2026-07-05T07:27:10Z"},{"alias_kind":"pith_short_8","alias_value":"G37743DB","created_at":"2026-07-05T07:27:10Z"}],"graph_snapshots":[{"event_id":"sha256:4a25507af30503c2404aeb4bdcf4fb5f45cc4f480ccd14962169d9ade1ac6d96","target":"graph","created_at":"2026-07-05T07:27:10Z","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/2312.11274/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among students from low-income backgrounds. We introduce an AI-assisted tutoring model that combines human and AI tutoring and hypothesize that this synergy will have positive impacts on learning processes. To investigate this hypothesis, we conduct a three-study quasi-experiment across three urban and low-income middle schools: 1) 125 students in a Pennsylvania school; 2) 385 students (50% Latinx) in a California school; and 3","authors_text":"Ashish Gurung, Danielle R. Thomas, Emma Brunskill, Erin Gatz, Jionghao Lin, Kenneth R. Koedinger, Kole Norberg, Lee Branstetter, Shivang Gupta, Stephen E. Fancsali, Vincent Aleven","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-12-18T15:17:09Z","title":"Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11274","kind":"arxiv","version":3},"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":"sha256:11c718875c012398afd7d01c827910e6ba896dd6b06fb48f5d5c1f1605d21be2","target":"record","created_at":"2026-07-05T07:27:10Z","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":"327644cac0314b5aefb7311072ffec1d3fa44bf88bc8bc0ea723978fe95d7e7d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-12-18T15:17:09Z","title_canon_sha256":"639ac42cc726b365f282596d0070c313fb22a1daf0db2bffffb0fe63a472620b"},"schema_version":"1.0","source":{"id":"2312.11274","kind":"arxiv","version":3}},"canonical_sha256":"36fffe6c617a4a7f7c08bdba94770e51c08d480d3b823c59d68f5dd766a21bc7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"36fffe6c617a4a7f7c08bdba94770e51c08d480d3b823c59d68f5dd766a21bc7","first_computed_at":"2026-07-05T07:27:10.250785Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:27:10.250785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"miQMmiZ4MH+QXCWLvI5eazuj2P/pybg4fQYOMI3DPP4O9GGJ2+E7RJQmSTlXyE1tcbecioTz66aMTHgMevcTBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:27:10.251309Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.11274","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11c718875c012398afd7d01c827910e6ba896dd6b06fb48f5d5c1f1605d21be2","sha256:4a25507af30503c2404aeb4bdcf4fb5f45cc4f480ccd14962169d9ade1ac6d96"],"state_sha256":"db08883809d9ce77a411af7b55b6741b9f2aac40c9edc0826c077568f08364b6"}