{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JWUJQILLJG32KVXZESJIEKJTGU","short_pith_number":"pith:JWUJQILL","schema_version":"1.0","canonical_sha256":"4da898216b49b7a556f92492822933353c9f2ccc95667219fe666240b298adc8","source":{"kind":"arxiv","id":"2506.17410","version":1},"attestation_state":"computed","paper":{"title":"Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.CL","authors_text":"Conrad Borchers, Danielle R. Thomas, Erin Gatz, Jionghao Lin, Kenneth R. Koedinger, Ralph Abboud, Sanjit Kakarla, Shambhavi Bhushan, Shivang Gupta","submitted_at":"2025-06-20T18:13:33Z","abstract_excerpt":"Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem. This present study investigates the feasibility and scalability of using generative AI to identify and evaluate specific tutor moves in real-life math tutoring. We analyze 50 randomly selected transcripts of college-student remote tutors assisting middle school students in mathematics. Using GPT-4, GPT-4o, GPT-4-turbo, Gemini-1.5-pro, and LearnLM, we assess tutors' application of two tutor skills:"},"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":"2506.17410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-20T18:13:33Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"844ccc61d0e56ee2df54179e8f85f62add9d320383f9aa421f25e0e427d33629","abstract_canon_sha256":"757e250f85c339db09ae5e3064234361c68333d49c6a71cf6df94998d93e4271"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:17.309091Z","signature_b64":"YmzULABPsE9C1weYQn2iX8ILeDC+lJw+wk49gjmqckzhgayOzN485PY4ziGiGWplcOeLLvL5+WwVqHjTn2EuDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4da898216b49b7a556f92492822933353c9f2ccc95667219fe666240b298adc8","last_reissued_at":"2026-07-05T11:25:17.308583Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:17.308583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.CL","authors_text":"Conrad Borchers, Danielle R. Thomas, Erin Gatz, Jionghao Lin, Kenneth R. Koedinger, Ralph Abboud, Sanjit Kakarla, Shambhavi Bhushan, Shivang Gupta","submitted_at":"2025-06-20T18:13:33Z","abstract_excerpt":"Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem. This present study investigates the feasibility and scalability of using generative AI to identify and evaluate specific tutor moves in real-life math tutoring. We analyze 50 randomly selected transcripts of college-student remote tutors assisting middle school students in mathematics. Using GPT-4, GPT-4o, GPT-4-turbo, Gemini-1.5-pro, and LearnLM, we assess tutors' application of two tutor skills:"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17410","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/2506.17410/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":"2506.17410","created_at":"2026-07-05T11:25:17.308656+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17410v1","created_at":"2026-07-05T11:25:17.308656+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17410","created_at":"2026-07-05T11:25:17.308656+00:00"},{"alias_kind":"pith_short_12","alias_value":"JWUJQILLJG32","created_at":"2026-07-05T11:25:17.308656+00:00"},{"alias_kind":"pith_short_16","alias_value":"JWUJQILLJG32KVXZ","created_at":"2026-07-05T11:25:17.308656+00:00"},{"alias_kind":"pith_short_8","alias_value":"JWUJQILL","created_at":"2026-07-05T11:25:17.308656+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU","json":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU.json","graph_json":"https://pith.science/api/pith-number/JWUJQILLJG32KVXZESJIEKJTGU/graph.json","events_json":"https://pith.science/api/pith-number/JWUJQILLJG32KVXZESJIEKJTGU/events.json","paper":"https://pith.science/paper/JWUJQILL"},"agent_actions":{"view_html":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU","download_json":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU.json","view_paper":"https://pith.science/paper/JWUJQILL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17410&json=true","fetch_graph":"https://pith.science/api/pith-number/JWUJQILLJG32KVXZESJIEKJTGU/graph.json","fetch_events":"https://pith.science/api/pith-number/JWUJQILLJG32KVXZESJIEKJTGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU/action/storage_attestation","attest_author":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU/action/author_attestation","sign_citation":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU/action/citation_signature","submit_replication":"https://pith.science/pith/JWUJQILLJG32KVXZESJIEKJTGU/action/replication_record"}},"created_at":"2026-07-05T11:25:17.308656+00:00","updated_at":"2026-07-05T11:25:17.308656+00:00"}