{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:S473CDQKQWBZIECK77EYQRPUJ5","short_pith_number":"pith:S473CDQK","schema_version":"1.0","canonical_sha256":"973fb10e0a858394104affc98845f44f7056685e5571626d87380ba60d6a3ce3","source":{"kind":"arxiv","id":"2205.01730","version":1},"attestation_state":"computed","paper":{"title":"Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Caiming Xiong, Chien-Sheng Wu, Lidiya Murakhovs'ka, Philippe Laban, Wenhao Liu","submitted_at":"2022-05-03T18:59:03Z","abstract_excerpt":"Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap. In this paper, we measure whether these metric improvements translate to gains in a practical setting, focusing on the use case of helping teachers automate the generation of reading comprehension quizzes. In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason. Even though we find that recent progress in QGen leads to a significant increase in question acceptance rates, there is still large room for improvement,"},"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":"2205.01730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T18:59:03Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"404eccaec2877cf997160dfe19cbe5c51037a786650e186104e1e8deee902c71","abstract_canon_sha256":"bdeaf00866d353c5ea59e6d368308dceaf9ef4b0bfa9a7b5acf544cb77020337"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:21.509196Z","signature_b64":"+K75zZu81C/G6XnuE14wiUAONeEzv0kQMRzcFJ+ID4Vo8ybvuJAqsWRNzFa8XanJuSkxxYZAzYxX/FPCc0BWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"973fb10e0a858394104affc98845f44f7056685e5571626d87380ba60d6a3ce3","last_reissued_at":"2026-07-05T04:20:21.508792Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:21.508792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Caiming Xiong, Chien-Sheng Wu, Lidiya Murakhovs'ka, Philippe Laban, Wenhao Liu","submitted_at":"2022-05-03T18:59:03Z","abstract_excerpt":"Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap. In this paper, we measure whether these metric improvements translate to gains in a practical setting, focusing on the use case of helping teachers automate the generation of reading comprehension quizzes. In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason. Even though we find that recent progress in QGen leads to a significant increase in question acceptance rates, there is still large room for improvement,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01730","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/2205.01730/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":"2205.01730","created_at":"2026-07-05T04:20:21.508854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.01730v1","created_at":"2026-07-05T04:20:21.508854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01730","created_at":"2026-07-05T04:20:21.508854+00:00"},{"alias_kind":"pith_short_12","alias_value":"S473CDQKQWBZ","created_at":"2026-07-05T04:20:21.508854+00:00"},{"alias_kind":"pith_short_16","alias_value":"S473CDQKQWBZIECK","created_at":"2026-07-05T04:20:21.508854+00:00"},{"alias_kind":"pith_short_8","alias_value":"S473CDQK","created_at":"2026-07-05T04:20:21.508854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18257","citing_title":"From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5","json":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5.json","graph_json":"https://pith.science/api/pith-number/S473CDQKQWBZIECK77EYQRPUJ5/graph.json","events_json":"https://pith.science/api/pith-number/S473CDQKQWBZIECK77EYQRPUJ5/events.json","paper":"https://pith.science/paper/S473CDQK"},"agent_actions":{"view_html":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5","download_json":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5.json","view_paper":"https://pith.science/paper/S473CDQK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.01730&json=true","fetch_graph":"https://pith.science/api/pith-number/S473CDQKQWBZIECK77EYQRPUJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/S473CDQKQWBZIECK77EYQRPUJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5/action/storage_attestation","attest_author":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5/action/author_attestation","sign_citation":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5/action/citation_signature","submit_replication":"https://pith.science/pith/S473CDQKQWBZIECK77EYQRPUJ5/action/replication_record"}},"created_at":"2026-07-05T04:20:21.508854+00:00","updated_at":"2026-07-05T04:20:21.508854+00:00"}