{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EEYERHPXFZHBTL3RCM2LWAQ46T","short_pith_number":"pith:EEYERHPX","schema_version":"1.0","canonical_sha256":"2130489df72e4e19af711334bb021cf4c92b906d58240f33dff00366857a2767","source":{"kind":"arxiv","id":"2507.20335","version":1},"attestation_state":"computed","paper":{"title":"Cultivating Helpful, Personalized, and Creative AI Tutors: A Framework for Pedagogical Alignment using Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aimin Zhou, Bo Jiang, Fei Tan, Hao Hao, JinZe Lv, Ruohua Zhang, Siyu Song, Tao Liu, Wentao Liu, Xinyun Wang, Ye Lu","submitted_at":"2025-07-27T15:56:29Z","abstract_excerpt":"The integration of large language models (LLMs) into education presents unprecedented opportunities for scalable personalized learning. However, standard LLMs often function as generic information providers, lacking alignment with fundamental pedagogical principles such as helpfulness, student-centered personalization, and creativity cultivation. To bridge this gap, we propose EduAlign, a novel framework designed to guide LLMs toward becoming more effective and responsible educational assistants. EduAlign consists of two main stages. In the first stage, we curate a dataset of 8k educational in"},"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":"2507.20335","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-27T15:56:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"177c09d62eb9b9429212fbd47fb3ccab5a106185c210c9eb499ca6639baba9fb","abstract_canon_sha256":"f40b2d6c25727e041387966598091250f43356df5c382674dfa3d6854ca37af2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:17.206592Z","signature_b64":"Pvi0Hz8T0o2Ywb2aXT8f1CJ+/zr+3rBm/3AjafUcERl64O6TutHdz+zC4InvzQmbT/MQENbqeI8mguVVbCCDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2130489df72e4e19af711334bb021cf4c92b906d58240f33dff00366857a2767","last_reissued_at":"2026-07-05T11:44:17.206090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:17.206090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cultivating Helpful, Personalized, and Creative AI Tutors: A Framework for Pedagogical Alignment using Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aimin Zhou, Bo Jiang, Fei Tan, Hao Hao, JinZe Lv, Ruohua Zhang, Siyu Song, Tao Liu, Wentao Liu, Xinyun Wang, Ye Lu","submitted_at":"2025-07-27T15:56:29Z","abstract_excerpt":"The integration of large language models (LLMs) into education presents unprecedented opportunities for scalable personalized learning. However, standard LLMs often function as generic information providers, lacking alignment with fundamental pedagogical principles such as helpfulness, student-centered personalization, and creativity cultivation. To bridge this gap, we propose EduAlign, a novel framework designed to guide LLMs toward becoming more effective and responsible educational assistants. EduAlign consists of two main stages. In the first stage, we curate a dataset of 8k educational in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20335","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/2507.20335/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":"2507.20335","created_at":"2026-07-05T11:44:17.206148+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.20335v1","created_at":"2026-07-05T11:44:17.206148+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20335","created_at":"2026-07-05T11:44:17.206148+00:00"},{"alias_kind":"pith_short_12","alias_value":"EEYERHPXFZHB","created_at":"2026-07-05T11:44:17.206148+00:00"},{"alias_kind":"pith_short_16","alias_value":"EEYERHPXFZHBTL3R","created_at":"2026-07-05T11:44:17.206148+00:00"},{"alias_kind":"pith_short_8","alias_value":"EEYERHPX","created_at":"2026-07-05T11:44:17.206148+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02933","citing_title":"Relation Reasoning with LLMs in Expensive Optimization","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T","json":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T.json","graph_json":"https://pith.science/api/pith-number/EEYERHPXFZHBTL3RCM2LWAQ46T/graph.json","events_json":"https://pith.science/api/pith-number/EEYERHPXFZHBTL3RCM2LWAQ46T/events.json","paper":"https://pith.science/paper/EEYERHPX"},"agent_actions":{"view_html":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T","download_json":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T.json","view_paper":"https://pith.science/paper/EEYERHPX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.20335&json=true","fetch_graph":"https://pith.science/api/pith-number/EEYERHPXFZHBTL3RCM2LWAQ46T/graph.json","fetch_events":"https://pith.science/api/pith-number/EEYERHPXFZHBTL3RCM2LWAQ46T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T/action/storage_attestation","attest_author":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T/action/author_attestation","sign_citation":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T/action/citation_signature","submit_replication":"https://pith.science/pith/EEYERHPXFZHBTL3RCM2LWAQ46T/action/replication_record"}},"created_at":"2026-07-05T11:44:17.206148+00:00","updated_at":"2026-07-05T11:44:17.206148+00:00"}