{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NAN3TJKB6IV62TC22XUSFWSGZR","short_pith_number":"pith:NAN3TJKB","schema_version":"1.0","canonical_sha256":"681bb9a541f22bed4c5ad5e922da46cc79bd8c88d58a31f5746ff28e027eaa94","source":{"kind":"arxiv","id":"2405.09482","version":2},"attestation_state":"computed","paper":{"title":"Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anastassia Shaitarova, Dirk Hovy, Donya Rooein, Paul Rottger","submitted_at":"2024-05-15T16:22:16Z","abstract_excerpt":"Using large language models (LLMs) for educational applications like dialogue-based teaching is a hot topic. Effective teaching, however, requires teachers to adapt the difficulty of content and explanations to the education level of their students. Even the best LLMs today struggle to do this well. If we want to improve LLMs on this adaptation task, we need to be able to measure adaptation success reliably. However, current Static metrics for text difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude and brittle. We, therefore, introduce and evaluate a new set of Promp"},"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":"2405.09482","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-15T16:22:16Z","cross_cats_sorted":[],"title_canon_sha256":"776af96733a695c6caabd8f7a7a926b70425dbb1a3f650ff1f82baea2cceeaad","abstract_canon_sha256":"487b3177eae9b836fd5ee7aef1759b41ee401ae5ff5a51cbfe3e747d0e823b2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:09.980793Z","signature_b64":"kuftIcG5SRbTmZmzThvVHUh1dw7glY/KCwHbIPxSDTZn4xzs56qTZRbte6bKme38YiARBfsSI8H+tT6QljGOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"681bb9a541f22bed4c5ad5e922da46cc79bd8c88d58a31f5746ff28e027eaa94","last_reissued_at":"2026-07-05T08:28:09.980259Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:09.980259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anastassia Shaitarova, Dirk Hovy, Donya Rooein, Paul Rottger","submitted_at":"2024-05-15T16:22:16Z","abstract_excerpt":"Using large language models (LLMs) for educational applications like dialogue-based teaching is a hot topic. Effective teaching, however, requires teachers to adapt the difficulty of content and explanations to the education level of their students. Even the best LLMs today struggle to do this well. If we want to improve LLMs on this adaptation task, we need to be able to measure adaptation success reliably. However, current Static metrics for text difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude and brittle. We, therefore, introduce and evaluate a new set of Promp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.09482","kind":"arxiv","version":2},"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/2405.09482/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":"2405.09482","created_at":"2026-07-05T08:28:09.980341+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.09482v2","created_at":"2026-07-05T08:28:09.980341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.09482","created_at":"2026-07-05T08:28:09.980341+00:00"},{"alias_kind":"pith_short_12","alias_value":"NAN3TJKB6IV6","created_at":"2026-07-05T08:28:09.980341+00:00"},{"alias_kind":"pith_short_16","alias_value":"NAN3TJKB6IV62TC2","created_at":"2026-07-05T08:28:09.980341+00:00"},{"alias_kind":"pith_short_8","alias_value":"NAN3TJKB","created_at":"2026-07-05T08:28:09.980341+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/NAN3TJKB6IV62TC22XUSFWSGZR","json":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR.json","graph_json":"https://pith.science/api/pith-number/NAN3TJKB6IV62TC22XUSFWSGZR/graph.json","events_json":"https://pith.science/api/pith-number/NAN3TJKB6IV62TC22XUSFWSGZR/events.json","paper":"https://pith.science/paper/NAN3TJKB"},"agent_actions":{"view_html":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR","download_json":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR.json","view_paper":"https://pith.science/paper/NAN3TJKB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.09482&json=true","fetch_graph":"https://pith.science/api/pith-number/NAN3TJKB6IV62TC22XUSFWSGZR/graph.json","fetch_events":"https://pith.science/api/pith-number/NAN3TJKB6IV62TC22XUSFWSGZR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR/action/storage_attestation","attest_author":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR/action/author_attestation","sign_citation":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR/action/citation_signature","submit_replication":"https://pith.science/pith/NAN3TJKB6IV62TC22XUSFWSGZR/action/replication_record"}},"created_at":"2026-07-05T08:28:09.980341+00:00","updated_at":"2026-07-05T08:28:09.980341+00:00"}